Kyoto University, Graduate School of Informatics

From Kyoto to the world.

Informatics is a cutting-edge interdisciplinary
field for the 21st century which assimilates information
with humans and society.

Interfacas with
Humans & Society●intelligence Science &Technology●Social Infornatics

Analysis
Modeling
Fundamentals in Modeling●Advanced Mathematical

Sciences●Applied Mathematics &Physics●Data Science

Application

Human
Society

Systematization
Infrastructures●Systems Science●Communications &ComputerEngineering

CONTENTS■Outlines of Graduate School of Informatics
Deﬁnition of Informatics

03

Welcome to Graduate School of Informatics

04

List of Courses and Groups

07■Introduction of the Courses
Intelligence Science and Technology Course

09

Social Informatics Course

19

Advanced Mathematical Sciences Course

29

Applied Mathematics and Physics Course

35

Systems Science Course

41

Communications and Computer Engineering Course

49

Data Science Course

57■ Related Educational Programs

65

Kyoto University School of Platforms (KUSP)
Kyoto University Collaborative Graduate Program in Design
International Program at Graduate School of Informatics■ Academic Programs

68

Curriculum Policy
Academic Programs
University Fellowship in Informatics
Career path after graduation■ List of Departments, Divisions and Groups

71

List of Departments, Divisions and Groups
Advances Information-Related Education &Digital Education Infrastructure Unit(Center for the Promotion of Interdisciplinary Education and Research(c-PiER))

The Kyoto University ICT Collaboration Promotion Network■Graduate School of Informatics Fund74

D eﬁ nit io n o f Info r m at ic s

Informatics in Kyoto University is the study of information innatural and artiﬁcial systems.

Informatics studies the creation, recognition, representation,collection, organization,
optimization, transformation, communication, evaluation andcontrol of information incomplex and dynamic systems.

Informatics has human, social, cognitive,
biological, linguistic, computational,
mathematical and engineering aspects.
It includes systems science andcommunications engineering.

Informatics has close relations with a numberof disciplines in the natural and human sciences.
It is developed employing contributions from many diﬀerent
areas : in turn, it can contribute to their further development.

Interfaces to human and social areas, mathematical modelingand information systems are the three pillars of Informatics
in Kyoto University.

03

Welcome to Graduate School of Informatics
Kyoto University Graduate School of Informatics, which was thefirst graduate school in Japan to use the term “informatics” in its
name when it was established in April 1998, recently celebrated
the 25th anniversary of its founding. Here, I would like to offer an
overview of the research fields and educational programs of
Graduate School of Informatics and outline some of its
outstanding features.
Dean, Graduate School of Informatics

Atsushi Igarashi

What is Informatics?
The preceding section of this booklet includes a definition of
informatics as envisioned by this graduate school. It states that
“informatics in Kyoto University is the study of information innatural and artificial systems,” that “informatics has human,social, cognitive, biological, linguistic, computational,
mathematical, and engineering aspects … and includes systems
science and communications engineering,” and that it “has close
relations with a number of disciplines in the natural and humansciences.” The term “information” tends to highlightcomputer-based technologies, such as information andcommunication technologies (ICT) and artificial intelligence(AI), but our definition of informatics points to an academic
discipline that is far more interdisciplinary than thesetechnologies suggest.
As mentioned, our graduate school was the first in Japan todefine itself using the term “informatics.” Up to that time, most
schools and departments in Japan in this general field used names
containing “computer science/engineering” or “information
science,” but between the late 1990s and early 2000s, Kyoto

schools in information-related science, typically with names like
“Graduate School of Information Science/Engineering” or“Graduate School of Information Science.” Only Kyoto
University adopted the broader term “informatics.” In fact, twoyears before Graduate School of Informatics was established, the“School of Informatics and Mathematical Science” was created in
the Faculty of Engineering through a merger of the School ofInformation Science and the School of Applied Mathematics andPhysics. The use of “informatics” was very unusual at the time.
These name choices were motivated by the desire of the people
involved in setting up this graduate school to establish a broader
discipline of “informatics” to transcend the limits of theconventional disciplines of “information science/engineering” or“information science.”
I imagine that all the students who apply to this graduate
school are keen to study their chosen field of specialization
seriously. As they steadfastly pursue their desire, I hope that theyalso take the time to look around to survey the whole of thegraduate school and experience the full breadth of “informatics.”
As explained below, the educational program is designed to allow
students to experience a wide range of this rich and broad field.

University and some other leading universities, including
Hokkaido University, Tohoku University, The University ofTokyo, Tokyo Institute of Technology, Nagoya University,
Osaka University, and Kyushu University established graduate

Graduate School of Informatics

04

Education and Research in Graduate Schoolof Informatics
Graduate School of Informatics is one of the largest graduate

students naturally cultivate a deeper knowledge of their field ofspecialization through the research guidance of their academic
advisors, but they also acquire a comprehensive academic vision
through seminar subjects in each course.

schools at Kyoto University, with a current intake (per academicyear) of 240 students for the Master’s program and 60 students

As part of its efforts to internationalize education, the schoolfor the Doctoral program. The graduate school consists of sevenoffers a number of courses in English. These are taught by both“courses” of study under the “major” of informatics.

Japanese and non-Japanese faculty. In some courses, we evenenable students to earn a Master’s degree without learningIntelligence Science and Technology Course

Japanese, by offering classes and research guidance in English

Social Informatics Course( “International Program” ). In the case of Doctoral programs,

Advanced Mathematical Sciences Course

students can pursue their research and earn a PhD in any of the

Applied Mathematics and Physics Course

courses without the need to learn Japanese. Even Japanese

Systems Science Course

students (who are not international students) can choose to takeCommunications and Computer Engineering Course

the International Program if it is offered for the course they are

Data Science Course

pursuing. On successful completion, such students are awarded
an “International Program Completion Certificate” along with

Each course is taught not only by faculty from within thetheir degree.

Graduate School of Informatics, but also by faculty (ascooperating professors) from other departments within the

Graduate School of Informatics also takes part in five-year

university, such as the Academic Center for Computing anddoctoral programs offered by multiple Kyoto University graduate

Media Studies, Disaster Prevention Research Institute, Kyoto

schools in collaboration with companies, overseas universities,

University Hospital, Institute for Chemical Research, Researchresearch institutes, and other external organizations. These are

Institute for Sustainable Humanosphere, and Institute forthe Kyoto University School of Platforms and Kyoto University

Liberal Arts and Sciences, as well as by collaborating researchers

Collaborative Graduate Program in Design, run by the Centerfrom institutions outside the university, including RIKEN,

for the Promotion of Excellence in Higher Education (CPEHE).

NTT, Hitachi, Advanced Telecommunications ResearchStudents in Graduate School of Informatics can take advantage

Institute International (ATR), and Okinawa Institute of Scienceof these programs to earn a degree. For additional information,

and Technology (OIST). The research fields (labs) of each course

see a more detailed outline or access applicable website URLs of

are described in the introduction to each course below. The

these programs below (in this booklet).

names of these courses alone indicate the remarkable breadth of
study and research covered by Graduate School of Informatics.

It is not an overstatement to say that graduates of this school

Leading-edge informatics research is conducted in all these fields.

who have received this kind of advanced education are becoming
increasingly sought after for a variety of roles as the field of

A key feature of the curricula of our graduate school is that it isinformatics expands. Such graduates typically find work as

designed not only to allow students to study the primaryresearchers in universities or corporate research labs, and as

academic major of their choice, but also to equip them with aexpert engineers in a wide range of industries, including ICT,

broad range of knowledge that transcends the boundaries of their

manufacturing, finance, broadcasting, and services.

c h o s e n fi e l d o f s p e c i a l i z a t i o n . O n e e x a m p l e i s t h einter-departmental course “Perspectives in Informatics,” required
electives for the Master’s program. In the Doctoral program,05

Welcome to Graduate School of Informatics

Our Reorganization

places great importance on pure research. Without a deep
knowledge of the fundamentals, it is not possible to create robust

In April 2023, on the 25th anniversary of its establishment,

technologies that can remain useful for many years. Informatics as

Kyoto University Graduate School of Informatics wasa field of study is still in its nascent phase. Given the youthfulness

reorganized. The six existing departments (Intelligence Scienceof this field, it seems very fitting to pursue research in informatics

and Technology, Social Informatics, Advanced Mathematicalat Kyoto University, in a city where tradition and innovation have

Sciences, Applied Mathematics and Physics, Systems Science, andcoexisted for many centuries. We look forward to seeing students

Communications and Computer Engineering) were merged into

take up exciting challenges under this new structure.

a single Department of Informatics. The educational programs of
the previous six departments were transformed into “courses” anda new “Data Science Course” was added (making a total of sevencourses). At the same time, the total student intake for the
Master’s program was increased (from 189 to 240 students per
academic year).
The main purposes of the reorganization were to respond to
the sharp rise in the number of students wishing to study
informatics in recent years, as well as to cultivate experts in theincreasingly important field of data science, which is closely
connected to informatics. Creating multiple educational courses
under a single major (field of specialization) also allows the school
to establish and deliver more flexible educational programs. In
fact, education in the new Data Science Course will be provided
both by faculty who work primarily in Graduate School ofInformatics and faculty from the Center for Innovative Researchand Education in Data Science of the Kyoto University Institutefor Liberal Arts and Sciences, who will concurrently serve in
Graduate School of Informatics. Structuring the educational
program in this way would have been difficult under aAtsushi Igarashi

Dean, Graduate School of Informatics

conventional framework of majors. We regard this reduction to asingle major as an important initiative for enabling us to provide
education that is better attuned to the anticipated changes anddevelopments in informatics over the coming years.

Concluding Remarks
This brief overview of Graduate School of Informatics isfocused on educational programs. Of course, a wide variety of
research in informatics is also conducted within the school. The
presentations of the labs in each of the courses offer a glimpse of
these research activities. Graduate School of Informatics also

Atsushi Igarashi is a Professor at Graduate School ofInformatics, Kyoto University. He received his B.S.,
M.S., and D.S. degrees from Department ofInformation Science, University of Tokyo in 1995,
1997, and 2000, respectively. Formerly, he belonged
to Dept. of Graphics and Computer Science, Graduate
School of Arts and Sciences, University of Tokyo. His
major research interests are in theoretical computerscience, in particular, principles of programming
languages and program verification. He has been
awarded a few domestic and international academic
prizes, including the AITO Dahl-Nygaard Junior Prizefor his investigations into the foundation of
object-oriented programming languages and their
type systems.

Graduate School of Informatics

06

Courses in Graduate School of Informatics

Distributed Information Systems
Human-Robot Interaction
Social Media
Sociotechnical Design (Adjunct Unit)

Socia

Consensus Informatics

cs Co

i
rmat

l InfoInformation Security (Adjunct Unit)

Neuroinformatics

urse

Psycho informatics

Intel

Cognitive Informatics

ce Sc

ligen

Computational Cognitive Neuroscience(Adjunct Unit)
Computational Intelligence

ience

Collective Intelligence

Integrated Disaster Management Systems
Emergency Management for Disaster
Reduction Systems
Crisis Information Management System
Medical Informatics
Learning and Educational Technologies
Data Engineering and Platform Research

Language Media Processing
Speech and Audio Processing
Computer Vision
Human Sensing
Text Media

Applied Analysis

Nonlinear Physics

cal

Biological Information Networks

07

Environmental Informaticsti
ema
Math urseoncedAdva iences C
Sc

rse

y Cou
olog
echn

and T

Conversational Informatics

Bioresource Informatics

Computational Mechanics

Industrial Mathematics

Computer Algorithms
Computer Architecture

Com
mun

Computer Software

icatioDigital Communications

ns an

Integrated-Media Communicationsd CoIntelligent Communication Networks
Processor Architecture andSystems Synthesis

mpu

Applied Mathematical Analysis

ter E

cs an
mati
athe urseoied MAppl hysics C
P

Discrete Mathematics

Integrated Circuits Design Engineering

ngin
eerin

System Optimizationg Co

Control Systems Theory

urse

Physical Statistics

Advanced Signal Processing
Remote Sensing Engineering
Atmospheric ObservationsdSupercomputing

Dynamical Systems
Applied Mathematical Modeling(Adjunct Unit)

Multimedia and Secure Networking

Statistical Mathematics

Mechanical Systems Control

Learning Machines
Integrated Systems Biology

urse

Statistical Intelligence

ce Co

urse

e Co

cienc

ms S

Mathematical Information Systems

Scien

Syste

Integrated Dynamical Systems

Statistical Inference

Data

Human Systems

Information-Theoretic Learning
Signal and Information Processing
Applied Machine Learning
Medical and Healthcare Data Science
Computational Intelligence Systems(Adjunct Unit)

Biomedical Engineering
Computational Neuroscience(Adjunct Unit)

Graduate School of Informatics

08

Intelligence Science and Technology Course
09

Construction and Elucidation of Intelligence,and Realization of Flexible,
Human-like Information Processing.
In an advanced information-oriented society, information processing
with flexible, human-like information capabilities becomes essential.
Information processing in human and animal organic systems is
unparalleled in nature which has developed through structural andfunctional adaptation to the environment in the long process of evolution.
Intelligence Science and Technology is a multidisciplinary field that aims to
elucidate the mechanisms of biological-, particularly, human-information
processing for the development of higher-level information processing.

Intelligence Science and Technology Course

Welcome to Intelligence Science andTechnology CourseIntelligence Science and Technology CourseIntelligence Science and Technology is an interdisciplinary research field
in which we study sophisticated human information processing and develop
new technologies on the basis of an understanding of human intelligence.
By “intelligence,” we do not mean simply “artificial intelligence” but rather
a wider scope embracing intelligent aspects of human, systems, andinformation technology. More specifically, foci of our research andeducational activities include (i) basic mechanisms of human beings, such as
life, brain, cognition, and behavior, (ii) information media related tolanguage, speech/auditory, and visual information, and (iii) a broad range of
topics related to intelligence and informatics, such as interaction between
human and intelligent systems, artificial intelligence and machine learning,and software and networks.
One of our distinguishing features is integration of these topics under aunique discipline, “intelligence science and technology,” notwithstandingthat these topics are also investigated in separate research fields such asneuroscience, psychology, engineering, and computer science. Faculty
members and students coming from a wide variety of academic backgrounds
devote themselves to cooperating in pursuit of this common goal. We hopethat, in this unique academic environment, many young students will
engage in this exciting research field, tackle the mysteries of complex human
intelligence, and develop new ideas for addressing challenging issues
regarding intelligent media and computation.
Our course welcomes outstanding students from various fields besides
those of computer science and engineering. We provide interdisciplinary
curricula and research projects. Join us, and enjoy an intellectually exciting
experience!

Shin’ya Nishida

Professor, Intelligence Science and Technology Course
1990 Research Associate, ATR Auditory and Visual Perception Laboratories
1992 Research Scientist, NTT Basic Research Laboratories
1996 Ph.D., Kyoto University
1999 Research Scientist, NTT Communication Science Laboratories
2019 Professor, Kyoto University

Graduate School of Informatics

10Intelligence Science and Technology Course

Outline■Group and Teaching Staff
Group

Teaching Staff

Neuroinformatics

Yukiyasu Kamitani/Professor Yukiori Goto/Associate Professor Hiroshi Hosokawa/Senior Lecturer
Yoshihiro Nagano/Assistant Professor Shingo Maegawa/Assistant Professor

Psychoinformatics

Takatsune Kumada/Professor Ryoichi Nakashima/Associate Professor

Cognitive Informatics

Shin'ya Nishida/Professor Hiroaki Mizuhara/Associate Professor
Kiyofumi Miyoshi/Assistant Professor

Computational Cognitive
Neuroscience (Adjunct Unit)

Hiroyuki Nakahara/Adjunct Professor Wataru Sato/Adjunct Associate Professor(RIKEN)

Computational Intelligence

Akihiro Yamamoto/Professor Nozomi Akashi/Assistant Professor

Collective IntelligenceHisashi Kashima/Professor Koh Takeuchi/Assistant Professor
Han Bao/Program-SpecificConversational Informatics
Language Media
Processing

Sadao Kurohashi/Program-Specific Professor Yugo Murawaki/Senior Lecturer
Chenhui Chu/Program-Specific Associate Professor Fei Cheng/Program-Specific Assistant Professor
Yin Jou Huang/Program-Specific Assistant Professor

Speech and Audio
Processing

Tatsuya Kawahara/Professor Kazuyoshi Yoshii/Associate Professor
Koji Inoue/Assistant Professor Keiko Ochi/Program-Specific Assistant Professor
Eita Nakamura/Program-Specific Assistant Professor

Computer Vision

Ko Nishino/Professor Shohei Nobuhara/Associate Professor
Marc Aurel Kastner/Assistant Professor

Human Sensing

Yuichi Nakamura/Professor Kazuaki Kondo /Associate Professor
Kei Shimonishi/Assistant Professor

Text Media

Shinsuke Mori/Professor Hirotaka Kameko/Assistant Professor

Biological Information
Networks

Tatsuya Akutsu/Professor Takeyuki Tamura/Associate Professor
Tomoya Mori/Assistant Professor■Curriculum of Intelligence
Science and Technology Course
CurriculumofIntelligenceScienceandTechnologyCourseDoctoralProgram(Informatics)
DoctoralThesis

rd

3 

nd
2 

st
1 

Subjects provided by the Course (total 6 credits including 4 credits from seminars)

Seminar on Intelligence Science and Technology, Adv. E (Mandatory, 2 credits)
Seminar on Brain and Cognitive Sciences, Adv. A, B E,
Seminar on Cognitive System, Adv. A, B E,
Seminar on Intelligence Media, Adv. A, B E,
Seminar on Application of Multimedia, A, B Adv. E,
Seminar on Bio-system Informatics, A, B Adv. E(2 credits each)ResearchGuidance

Master’sProgram(Informatics)



Master’sThesis
nd

- 4 -

2 

SubjectsprovidedbytheCourse(optional6creditsormore)







AdvancedSubjects

Seminars(4credits,Mandatory)

SeminaronCognitiveScience,ComputationalCognitiveNeuroscience,PatternRecognitionAdv.E,�SpeechProcessingAdv.E,Language
InformationProcessingAdv.,ComputerVisionE,VisualInterface,BioinformaticsAdv.(2creditseach)

SeminaronIntelligenceScienceandTechnologyIIE
SeminaronIntelligenceScienceandTechnologyIVE�
(AssignedtoM2,2creditseach)
SeminaronIntelligenceScienceandTechnologyIE,
SeminaronIntelligenceScienceandTechnologyIIIE�
(AssignedtoM1,2creditseach)

BasicSubjects

RecommendedSubjectsProvidedbyOther
CoursesIntroductiontoCognitiveScience,IntroductiontoInformationScience,
(2creditseach)
IntroductiontoBioinformatics 

st

1 

Computationallearningtheory
Statisticallearningtheory

GeneralsubjectsprovidedbytheSchool
InterdisciplinarysubjectsofthePerspectivesin
Informatics(Mandatory2creditsormore,upto4
credits)
PerspectivesinInformatics1
PerspectivesinInformatics2�
PerspectivesinInformatics3E PerspectivesinInformatics4E�
PerspectivesinInformatics5E (2creditseach)

Priorto
admission

Bioinformatics

Psychology

PerspectivesinPlatformStudies(2credits),ComputationalScience,Introduction(2credits),ComputationalScience,ExerciseA(1credit),
InformationandIntellectualProperty(2credits),Innovationand
Information(2credits),InformationAnalysisandManagement(2credits),
InformationAnalysisandManagement,Exercise(1credit),Social
ContributionsthroughInformaticsE(1credit)InternshipintheFieldofInformaticsE(1credit)

Computational
ScienceElectricaland
ElectronicEngineeringSeminarsand
exercisesfor
Master’sthesis

(Mandatory8credits)

AdvancedStudyinIntelligenceScienceand
Technology2E
(AssignedtoM2,6credits)
AdvancedStudyinIntelligenceScienceand
Technology1E�
(AssignedtoM1,2credits)
Specific
subjects
provided
bytheschoolRequiredbasicbackgroundofeithersubjectontheleft

Note:�Courses�marked�with�the�letter�“E”�will�be�provided�in�English.

11

Intelligence Science and Technology Course

Brain and Cognitive Sciences
Decoding neural codes
Brain signals can be seen as “codes” that encode ourmental contents. We study methods for modeling brain
functions and representations using information scienceand technology including machine learning and artificial
neural networks. Our approach is based on data-driven
predictive models that relate brain data and mind states
via analysis of massive neural, behavioral, andmultimedia data. Using these models, we aim toIntelligence Science and Technology Course

understand basic principles of neural information
processing, and seek to develop real-life applications
such as brain–machine interfaces that exploit decoded
brain information.
[Yukiyasu Kamitani , Yukiori Goto , Hiroshi Hosokawa , Shingo Maegawa , Yoshihiro Nagano]Psychoinformatics
Toward understanding human cognition and applying it tohuman-machine interface
Human activities in daily life are supported by basic
cognitive functions, such as perception, attention,
memory and high-order executive control. We investigate
the psychological and neuroscientific bases of these
cognitive functions (especially focusing on attention andexecutive function), using psychological experiments,
brain-imaging and computational techniques. We areinterested in human behavior not only in well-controlled
experimental settings in a laboratory, but also in

An experiment examining eye and action coordination

real-world settings such as IT-equipment use and real
car driving. We are also interested in cognitive functions
in a wide range of populations, from healthy young adults
to older or disabled individuals.
[Takatsune Kumada , Ryoichi Nakashima]

Graduate School of Informatics

12

Intelligence Science and Technology Course

OutlineCognitive Informatics
Understanding Human Sensory and Cognitive Information Processing
Our lab studies the computational principle
and the neural mechanisms of human functions
including perception, cognition and language
processing, by means of psychophysics,
computer simulations and EEG recordings. By
comparing human brains with cutting-edge

artificial intelligent systems, we attempt to reveal
the characteristic nature of the human
information processing. We are also interested inleveraging human scientific studies for innovationof information technologies.
[Shin'ya Nishida , Hiroaki Mizuhara , Kiyofumi Miyoshi]

Material perception is one of
our research topics.

Brain researches by
EEG measurements

Computational Cognitive Neuroscience (Adjunct Unit)
Neural information processing and computational models
We aim to understand the computational
principles that underlie the way neural systems
realize adaptive behavior and complex minds:e.g., decision-making, reinforcement learning,
motivation and emotion, and social behaviors.
Our approaches are tightly linked to statisticaland information science including machinelearning and neural networks, as well as toquestions in neuroscience and psychology. To
address our questions, we build computational13

and mathematical models, and develop data
analysis methods for linking those behavioral
functions and brain signals throughcomputations. We use human fMRI to examine
neural signals and computations, combined withthose modeling and quantitative methods. We
also seek to use our insights to be applied toconstructing brain-based intelligence.
[RIKEN: Hiroyuki Nakahara , Wataru Sato]Intelligence Science and Technology Course

Computational Intelligence
Formulation of principles to support intelligent information processing
We will formalize the intelligent information
processing seen in human activities and conduct
studies on the basic principles that underlie theseprocesses, as well as realization methods.
Specifically, this will involve education and researchr e l a t i n g t o a r t i fi c i a l i n t e l l i g e n c e i n f o r m a t i o nprocessing such as inductive logic, knowledge
discovery, hypothetical reasoning, and evolvable
computers, using mathematical logic, inference
procedures, machine learning theories andself-organization.
[Akihiro Yamamoto , Nozomi Akashi]

Machine Leaning and Data Structure
Second Principal
Component

Supporting Intelligent Activity
Evidence

Projection

Choices
PCA

Verifying Robustness of NN Models
Minimum Word Replacement Problem

x：an input text
 : a replacement patterny：the correct label of x f : the target NN model  : the size of the replacement pattern Intelligence Science and Technology CourseFirst Principal
Component

Collective Intelligence
Machine Learning That Matters
Our research focus is on the development offundamental technologies for intelligent information
technology, with a focus on machine learning, andtheir real-world applications. While pursuing R&D on
new problems and methods of machine learning anddata analysis, we also tackle various challenges inscience and business. At the same time, we conduct
research on methodologies for solving challenging
problems that are difficult to address by AI or humans
alone, by combining AI with human power.
[Hisashi Kashima , Koh Takeuchi , Han Bao]

⋇ Advanced machine learning algorithms

K1�
K2�
Ky�
A(1)�
�

J 1�

X�Jy� Y�

A(2)�

~
=

V(y)�
T(y)�

U(y)� +

V(1)�
T(y)�

U(1)� +

V(2)�
T(2)�

U(y)�

+

⋈ Applied machine learning

V(y)�
T(3)�

U(3)�

A(3)�

J3�
Iy�

I2�

I3�

⋉ Human-in-the-loop AI

Graduate School of Informatics

14

Intelligence Science and Technology Course

OutlineLanguage Media Processing
Making computers that can understand language
Since language lies at the core of human
intellectual activity, the emergence of computerscapable of freely manipulating language will
significantly impact society in a broad variety ofways. With this goal, we are engaged in research
to shed light on how humans utilize language andto enable computers to communicate using
language in human-like ways. We are pursuingboth pure research on language understanding
based on large-scale language models andapplied research for useful real-world purposes,
such as translation, dialogue, and knowledge
structuring.

࠙Speech Recognitionࠚ࠙Structured Knowledgeࠚ

E: I polish it using sandpaper.
I: Howtomoveitwhilepolishing?
E: >ŝŬĞ drawing a circle, …

<replace CHANGE>[PURPOSE] to remove scratches[OBJECT] sandpaper
Q: In what [CONDITION]?

࠙Pre-made Imageࠚ<ƉŽůŝƐŚPOLISH>[INSTRUMENT] sandpaper[MANNER] ůŝŬĞdrawing a circle
Q: How much [TIME]?

DomainExpertInterviewer[Sadao Kurohashi , Yugo Murawaki , Chenhui Chu , Fei Cheng , Yin Jou Huang]

Speech and Audio Processing
Recognition and understanding of speech, audio and music
Speech communication plays a key role inhuman intelligence. We are studying theintelligent processing of speech, audio and music
as exchanged by human beings for automatic
recognition, understanding and interaction
systems, specifically (1) automatic speechtranscription of meetings and lectures, (2)analysis of audio scenes and music signals
composed of multiple sound sources, and (3)humanoid robots capable of natural interaction
by combining non-verbal information.
[Tatsuya Kawahara , Kazuyoshi Yoshii , Koji Inoue , Keiko Ochi , Eita Nakamura]

15

Intelligence Science and Technology Course

Computer Vision
Making Computers See
Neural substrates dedicated to vision are said
to occupy about 40 percent of the cerebral
cortex. Realizing computer vision as a truly
intelligent perceptual modality is fundamental
for artificial intelligence, and would also inform
our understanding of human visual intelligence.
Towards computational visual intelligence, ourresearch is focused on establishing thet h e o r e t i c a l f o u n d a t i o n s a n d e f fi c i e n t
implementations of computational methods forbetter understanding people, objects and scenes
from their appearance in images and video, as
well as the development of novel computational
imaging systems that can see beyond what wesee.
[Ko Nishino , Shohei Nobuhara , Marc Aurel Kastner]
Intelligence Science and Technology Course

Tracking People in Crowds

Shape from Water

Reflectance and Natural Illumination from a Single Image

Graduate School of Informatics

16

Intelligence Science and Technology Course

OutlineHuman Sensing
Toward human-centered information and machine systems
through visual and embodied interactions
The main purpose of this group is to create
information media and mechanical systems
that support human with respecting theirsubjective actions.
Such media include watching over humans toprovide supports in appropriate way and time,
supplying only deficient force in motions,interfaces that connects humans andcomputers, and assisting human memory.
A common approach in all of these media isthat information or mechanical systems noticehuman’s intention and augment their abilities

for meeting what they want to do.
To realize this support scheme, this group is
engaged in basic researches involving (1)artificial intelligence and knowledge processing,(2) recognition of human physical andphysiological activities using visual, acoustic,
and biological measurements, (3) modelinghuman behaviors in motion and communication,
while (4) designing and implementing thehuman-centered supporting media using (5)robotic and artificial agent control technology.

･ Interface of visualizing human intentions
inferred from motions･ Wearable computers for recording andsupporting personal/group experiences･ Motion assist based on behavioral analysis
using electromyography･ Facial expression recognition for healthscience and care of dementia persons･ Joint research using image analysis andAI with other laboratories and research
centers within and outside of Kyoto
University[Yuichi Nakamura , Kazuaki Kondo,
Kei Shimonishi]

Text Media
Speech and Natural Language Processing for Multi-media Archives
Since time immemorial human knowledge
has been recorded as text. The research
activities of this group focus on computerscapable of understanding these texts anddescribing new knowledge. As a basis we are
studying fundamental natural language
processing. And we are studying natural
language generation to explain data analysis
and future prediction by computer or todescribe other media such as video and17

speech.
Specifically, we deal with real-world media,
including procedural texts such as cooking
recipes with execution videos, academic
knowledge such as history/geography
research, and game/data analysis bycomputers.
We also try to expand human knowledge
based on our research results.
[Shinsuke Mori , Hirotaka Kameko]

Intelligence Science and Technology Course

Biological Information Networks
It for analysis of biological information
We develop algorithms for inferring interactions among genes, proteins and chemical structures,and for analyzing their interactive networks based on mathematical methods. We also develop
algorithms and software tools for other problems in bioinformatics, including sequence analysis andinference of higher-order structures and functions of protein.
[Tatsuya Akutsu , Takeyuki Tamura , Tomoya Mori]Intelligence Science and Technology Course

Analysis of three-dimensional structures and interactions of protein

Graduate School of Informatics

18

Social Informatics Course

Harmonizing Society andInformation Technology
Global networking is rapidly expanding via information technology.
Based upon the trends of technologies that interact with our daily lives,
Social Informatics Course seeks to clarify the structures of highly complex
information societies and actually design information systems that are
globally useful.
Therefore, we endeavor to support globalizing activities in the areas ofculture, economics, ecology, disaster management, healthcare andeducation.

19

Social Informatics Course

Information is Born in the Lives of People
Sources of social information do not exist on the Internet.

Nobuhito Ohte

Professor, Social Informatics Course
He is a Professor of the Biosphere Informatics Laboratory in the Division of Biosphere
Informatics at Kyoto University. His research themes are to understand mechanisms ofhydrological controls of nutrient transformations and transportations in the forested
catchment, and to elucidate the geographical variations of those ecosystem dynamics among
various types of climatic and geological conditions. He holds a BA in forestry from Graduate
School of Agriculture at Kyoto University in 1987, and a PhD also from Kyoto University inforest hydrology in 1992. He was awarded Biwako Prize for Ecology in 2013. He is currently an
associate editor of Biogeosciences (European Geoscience Union).

Social Informatics Course

These sources are usually found in people’s daily lives. Social informatics includes
processes from the acquisition of information from the sources, through constructing
systems to utilize the information, to designing a better society using those information
systems. For example, we collect ecological information relevant to fish by using bio-logging
techniques for fishery resource conservation. We also glean biological information fromforest ecosystems using various types of sensors to optimize the sustainable utilization of
ecosystem services. Then, we study methods for database development to organize that
information on natural resources and environments. Moreover, we study informatics
applications in the fields of medical services and education, and also determine what types of
information are needed for disaster prevention and harm minimization. We consider ways
to utilize the data to design societies and social systems. We,
the people in the Social Informatics Course, work toacquire data directly related to our lives and societies, andto reformulate and store it in the most accessible and usefulway to enable the development of our sustainable future.

Applications of Information Technologies Expanding
Information technologies are causing our society to make great strides, with data science,
artificial intelligence (AI), and information search becoming an integral part of ourlifestyles. Meanwhile, robots, autonomous vehicles, and other information systems withadvanced AI continue to blend into our physical world. As one new information technology
after another is being developed, how can information systems be created that achieve
harmony with society and are accepted and utilized? The Course of Social Informatics isprivileged to have a faculty comprised of experts in information technologies and teaching
staff versed in applications of information technologies in suchfi e l d s a s b i o l o g y , a g r i c u l t u r e , m e d i c a l s c i e n c e , d i s a s t e r
management, and education. If you wish to create information
technologies that lead to practical applications or information
systems that are of genuine use to society, this is the place youwant to conduct your research!

Takayuki Kanda
Social Informatics Course

He received a bachelor's degree from the Department of Information Science, Kyoto University
Faculty of Engineering, in 1998, and completed his doctoral program at the Department ofSocial Informatics, Kyoto University Graduate School of Informatics, in 2003 to earn a Ph.D. in
Informatics. He joined ATR Intelligent Robotics and Communication Laboratories in 2003 tosuccessively assume the positions of researcher, senior researcher, and Group Leader. Since
2018, he has held a professorship at the Department of Social Informatics, Kyoto University
Graduate School of Informatics. His research interests include human-robot interactions,especially robots' autonomous dialogue mechanisms and social competencies, dialogues byanthropomorphic robots using their bodies, and their applications to everyday life settings.

Graduate School of Informatics

20

Social Informatics Course

Outline■Group and Teaching Staff
Group

Teaching Staff

Distributed Information
Systems

Human-Robot Interaction

Takayuki Kanda/Professor Dražen Bršcić/Associate Professo Jani Even/Program-Specific Senior Lecturer
Stela H. Seo/Assistant Professor Malcolm Doering/Program-Specific Assistant Professor
Kanae Kochigami/Program-Specific Assistant Professor

Social Media

Keishi Tajima/Professor (Secondary appointment: Institute of Liberal Arts and Sciences)

Sociotechnical Design(Adjunct Unit)

Naomi Yamashita/Kyoto University Visiting Professor (NTT Communication Science Laboratories)

Consensus Informatics

Takayuki Ito/Professor Rafik Hadfi/Program-Specific Associate Professor
Ryuta Arisaka/Assistant Professor Shiyao Ding/Assistant Professor
Sofia Sahab/Program-Specific Assistant Professor Jawad Haqbeen/Program-Specific Assistant Professor

Information Security(Adjunct Unit)

Masayuki Abe/Kyoto University Visiting Professor
Mehdi Tibouchi/Kyoto University Visiting Associate Professor (NTT Informatics Laboratories)

Bioresource Informatics

Hideyuki Doi/Professor Hideaki Nishizawa/Assistant Professor
Satsuki Tsuji/Assistant Professor

Environmental Informatics

Nobuhito Ohte/Professor Lina A. Koyama/Associate Professor
Masumi Hisano/Assistant Professor

Integrated Disaster
Management Systems

Hirokazu Tatano/Professor Toshio Fujimi/Associate Professor
Subhajyoti Samaddar/Program-Specific Associate Professor Huan Liu/Program-Specific Assistant Professor

Emergency Managementfor Disaster Reduction Systems

Katsuya Yamori /Professor Genta Nakano/Assistant Professor

Crisis Information
Management System

Michinori Hatayama/Professor Kei Hiroi /Senior Lecturer

Medical Informatics

Tomohiro Kuroda/Professor Goshiro Yamamoto/Associate Professor
Yukiko Mori /Senior Lecturer Chang Liu/Assistant Professor

Learning and Educational
Technologies

Hiroaki Ogata/Professor Rwitajit Majumdar/Program-Specific Senior Lecturer
Izumi Horikoshi/Assistant Professor

Data Engineering and
Platform Research

Kazuyuki Shudo/Professor Shiori Hironaka/Assistant Professor

CurriculumofSocialInformaticsCourse■Curriculum of Social Informatics
CourseDoctoralProgram(Informatics)
rd

DoctoralThesis

ndSubjects provided by the Course (total 6 credits including 4 credits from seminars)

3 
2 
nd

1 


Seminar on Social Informatics, Adv. E (Mandatory, 2 credits)
Seminar on Social Information Model, Advanced A, B E
Seminar on Social Information Network, Advanced A, B E
Seminar on Biosphere Informatics, Advanced A, B E
Seminar on Regional Disaster Prevention Information Systems, Advanced A, B E
Seminar on Medical Informatics, Advanced A, B E
Seminar on Social informatics Analytics Infrastructure, Advanced A, B E (2 credits each)Research
Guidance

Master’sProgram(Informatics)


Master’sThesis

nd

- 5 -

2  SubjectsprovidedbytheCourse(optional10creditsormore,including6creditsfrombasiccourses)

AdvancedSubjects(2creditseach)
MultiagentSystemsHumanͲRobotInteractionE, BiosphereInformaticsE,DisasterInformationEmergencyManagement,
MedicalInformatics
InformaticsofEͲbusiness
InformationEducation

DistributedSystemsE,CryptographyandInformationSociety
UserExperience(UX)E,
ServiceModeling
FieldbasedLearning/ProblembasedLearning(FBL/PBL)1,2

st

1 

BasicSubjects(AssignedtoM1,2creditseach)

InformationandSocietyE,InformationSystemAnalysis�E,PracticeofInformationSystems�E

GeneralSubjectsprovidedbytheSchool
InterdisciplinarySubjectsofthePerspectivesin
Informatics(Mandatory2credits)
PerspectivesinInformatics1 PerspectivesinInformatics2�
PerspectivesinInformatics3E PerspectivesinInformatics4E�
PerspectivesinInformatics5E (2creditseach)

Priorto
admission

FundamentalbackgroundofthesubjectsintheCourse,etc.

PerspectivesinPlatformStudies(2credits),ComputationalScience,Introduction(2credits),ComputationalScience,ExerciseA(1credit),InformationandIntellectualProperty(2credits),Innovation
andInformation(2credits),InformationAnalysisandManagement
(2credits),InformationAnalysisandManagement,Exercise(1credit),SocialContributionsthroughInformaticsE(1credit)
InternshipintheFieldofInformaticsE(1credit)

Preferredtounderstandthesubjectsontheright

Datastructureand
Algorithms

Fundamental
Programing

Seminarsandexercises
forMaster’sThesis
(Mandatory10credits)


AdvancedStudyinSocial
Informatics2 E 
(AssignedtoM2,5credits)

AdvancedStudyinSocial
Informatics1 E 
(AssignedtoM1,5credits)

Specificcourses
providedbytheschool

Fundamentalconcepts
ofComputationalNote:�Courses�marked�with�the�letter�“E”�will�be�provided�in�English.

21

Social Informatics Course

Human-Robot Interaction
Intelligent robotics for our daily social environments
There are a growing number of everyday applications for artificial
intelligence and robotics such as social robots, self-driving cars, andautomated shops. We expect that future society will be ubiquitous with various
robots. Towards such a robotized society, we aim to study robots that interact
with people and operate in harmony alongside them. Such robots areembodied agents with sensory feedback that have the ability to interact inreal-time with their environments. Consequently, we conduct fundamental
research in intelligent robotics, human interaction, sensor networks, andartificial intelligence (AI). We are aiming for applications in the service
industry, elderly care, health care, collaborative work places, and learning.
Social Informatics Course[Takayuki Kanda , Dražen Bršcić , Jani Even , Stela H. Seo , Malcolm Doering , Kanae Kochigami]

Social MediaInformation environment for sharing and utilizing all useful information in the world"Sharing and utilizing all useful information in the world" may sound to
you like a mission statement of a major search engine company. It has,
however, been the dream of researchers in the area of database systems
and information retrieval since a long time ago. Thanks to the advance ofcomputer and network technologies, we can now easily share and utilize
data of large volume and high diversity which we cannot even think about
until decades ago, and this "dream" is not a daydream anymore but is the

Web = distorted mirror
Need to remove distortion

goal. To achieve this goal, we conduct research on technologies for

Extraction of social
information from the Webcollecting, analyzing, extracting information and technologies for retrieving

Example of Twitter follow
link classification

information you want from the extracted information. Our recent research
themes includes: extraction of social information from the Web, social
network analysis, information retrieval, and information access interface.
[Keishi Tajima]

Graduate School of Informatics

22

Social Informatics Course

OutlineSociotechnical Design (Adjunct Unit)
Toward an inclusive future society (in collaboration with NTT Corporation)
The progress of the information society has enabled people to connect with anyone more easily, but at thesame has given rise to problems such as weakening of interpersonal relations, fragmentation, and isolation.
These problems cannot be resolved simply by providing more opportunities to connect with other people orsupport for those who are isolated. It is crucial to engage the surrounding community and environment inorder to achieve solutions. This laboratory pursues research on basic communication technologies that
foster deeper human relations and ensure that diverse individuals are included rather than left isolated.
The research topics addressed include:
Information technologies for the realization of inclusive societies Basic communication technologies that foster deeper human relations
Information technologies that serve the collective good Collaborative technologies that enable sustainable work styles[NTT Communication Science Laboratories: Naomi Yamashita]“Minamo-mate”: a support tool for familiesof people with depression

Support for non-native speakers in
multilingual projects
Dialogue agents fostering
person-to-person trust

Consensus Informatics
Toward AI-powered Collective Intelligence
Our interest lies in AI-powered collective
intelligence, whereby people and computers or AI
agents interact to form consensus and cooperatewith each other. Recent research has shown thathuman collective intelligence is superior toindividual intelligence. Adding highly networked
computers and AI to a human population therefore
has the potential to achieve higher levels ofintelligence on a large scale and with great
efficiency. Spectacular advances in AI technology
in recent years, typified by large-scale language
models, have enabled AI agents with dramatically
improved accuracy. So, by getting these AI agentsand humans to build consensus and cooperatewith each other, we hope to demonstrate an
unprecedented degree of collective intelligence,with the ultimate goal of shaping totally new social
systems.
The multi-agent AI presents a methodology andconcepts that allow us to realize such new social
systems and promote them with collective
intellect and intelligent information technology. In
multi-agent systems, we mainly explore
possibilities of new social systems and implementsuchsystems, while seeking the essence of social
intelligence, with a focus on interdisciplinarys t u d i e s w i t h s u c h fi e l d s a s d i s t r i b u t e d A I ,simulations, robotics, and game theory.
Our interest lies in AI-powered collective
intelligence, whereby people and computers or AI
agents interact to form consensus and cooperatewith each other. Recent research has shown thathuman collective intelligence is superior to23

individual intelligence. Adding highly networked
computers and AI to a human population therefore
has the potential to achieve higher levels ofintelligence on a large scale and with great
efficiency. Spectacular advances in AI technology inrecent years, typified by large-scale language
models, have enabled AI agents with dramatically
improved accuracy. So, by getting these AI agentsand humans to build consensus and cooperate witheach other, we hope to demonstrate an
unprecedented degree of collective intelligence,with the ultimate goal of shaping totally new social
systems. Our research in this field is focused on AI
and multi-agent AI, including support forconsensus formation, computational mechanism
design, automated negotiating agents, large-scalelanguage models, distributed reinforcement
learning, service computing, IoT, argumentation
theory, and social simulation.
We will pursue both high ideals (theoreticalstudies) and down-to-earth approaches(commercialization of research achievements).

[Takayuki Ito , Rafik Hadfi , Ryuta Arisaka ,Shiyao Ding , Sofia Sahab , Jawad Haqbeen]Social Informatics Course

ColumnFrom the World to Japan, from Japan to the WorldOver 40 foreign students are enrolled in the
Course of Social Informatics, representing
more than 15 countries. You may obtain asense of global community by studying in sucha diverse environment. Many students fromthe Course of Social Informatics makepresentations, discuss research, and joininternship programs abroad. A welcome party
is held in October to welcome newcomers
from foreign countries, and various kinds ofinternational exchanges happen on a daily
basis.

Social Informatics Course

An international event

Research presentation at an international conference (Canada)

Information Security (Adjunct Unit)
Creating a safe digital society through cryptography(in collaboration with NTT Corporation)
In today’s world, where the Internet is
i n c re a s i n g ly u s e d t o c o n d u c t e l e c t r o n i ctransactions such as electronic settlements andbidding in online auctions, or to carry out
functions required by the government, such as
the filing of tax returns, the maintenance of
secure network services is becoming
increasingly important. Modern cryptography isa key technology to achieve such security, andgoes far beyond concealment technology toprevent eavesdropping. It is a field of intense
study that is developing rapidly. It involves
technical and logical systems which include
digital signatures that verify the identity of theparty you are communicating with andguarantee the authenticity of the data (whichmeans that the data have not been altered), as
well as cryptographic protocols that enableadvanced network services which guarantee
privacy. In our laboratory, we will study anddevelop various applied cryptographic
techniques commonly used today such as publickey cryptosystems, crypto currency, electronic
voting, and other applications suitable for cloudcomputing with rigorous security analysis basedon a firm theoretical foundation.
[NTT Social Informatics Laboratories: Masayuki Abe, Mehdi Tibouchi]&ORXG6HFXULW\

e-voting

cryptocurrency

cloud
computingaccesscontrol

CRYPTOGRAPYforSecureICT

Graduate School of Informatics

24Social Informatics Course

OutlineBioresource Informatics
Seeking utilization and conservation of bioresources
Our research and education efforts areenvironmental DNA methods for collecting

directed at a wide range of subjects to do withbioresource information, and tackling various

the extraction, analysis, and utilization ofbasic and applied hypotheses related to

information, in connection to the use andbioresources, biology, and ecology using big dataconservation of terrestrial and marine biologicalin the form of geographic information, satellite

resources. We are particularly focused on

information, and large databases.

developing biologging, biotelemetry, and[Hideyuki Doi , Hideaki Nishizawa , Satsuki Tsuji]

Various methods and techniques are applied

Environmental Informatics
Seeking conservation and utilization of ecosystems
Recently, society has been interested inorganisms or environments in the ecosystems,

environmental changes at various

or how human society influences them withspatial/temporal scales. This group works on

respect to their sustainability in a changing

topics such as understanding/monitoring

environment. Various methods/approaches suchenvironmental information or the role of various

a s fi e l d i n v e s t i g a t i o n , q u e s t i o n n a i r e s ,organisms in ecosystems. Our research interests

remote-sensing or chemical/isotopic analyses

cover various fields related to terrestrial

are applied to collect and analyze key

ecosystems, ranging from their diverse

information about these ecosystems andinhabitants such as plants, animals andorganisms.

microbes to their complete physicalenvironment. We study how these relate to otherFieldwork is conducted in various types of sites

25[Nobuhito Ohte , Lina A. Koyama , Masumi Hisano]Social Informatics Course

Integrated Disaster Management Systems
Aiming to build disaster prevention systems to ensure a safe and secure society
To build a safe and secure society, we need to put in place disasterprevention systems that will enable the planning and implementation ofdisaster risk control, financing, and other integrated policies in arational manner. We will take an informational, organizational andeconomic approach to find out what kind of disaster prevention system
will result in cities with greater resistance to natural disasters.
[Hirokazu Tatano , Toshio Fujimi , Subhajyoti Samaddar , Huan Liu]

Flood Risk Communication Support System

Social Informatics Course

Emergency Management for Disaster Reduction Systems
Social Scientific research for disaster damage reduction
Society is required to prepare and take actions to reduce thepotential damage of disasters by sharing information and knowledge
concerning potential disaster risks through communication among themembers of society. This laboratory aims at developing disaster risk
reduction systems through practical studies from the perspective ofsocial psychology and systems analysis. The research interests include
disaster psychology, disaster risk information and communication,
disaster education, regional crisis management and decision making
support, financing for disaster recovery.
Disaster education materials developed in our lab[Katsuya Yamori , Genta Nakano]

Crisis Information Management System
Disaster Information Systems with Information
Technology
Our goal is to establish design methodologies for the department ofeffective disaster management systems against various types of disaster for
National/Local Governments, local communities in affected areas anddisaster relief organizations. One of the most important key technologies are
spatial temporal database to record, visualize and analyze current/near
future status in affected areas. In addition our laboratory focuses on humanDevelopment of building damage certification
system considering operator’s ability in
the Great East Japan Earthquake

behavior before/during/after disasters as targets to supply valuable services.
[Michinori Hatayama , Kei Hiroi]
Graduate School of Informatics

26

Social Informatics Course

OutlineMedical Informatics
Exploring approaches to medicine in the information age
The Medical Informatics Laboratory seeks tocreate a new vision for medicine in the information
age by developing information platforms, analyzing
real-life clinical information, and providing feasible
information support for existing clinical entities.
Our research covers all areas at the interface of
information and medicine, including systemdevelopment for ubiquitous hospital information
systems, remote medical information systems,
electronic health records, and other systems,
hospital administration analysis, clinicalepidemiological information analysis, data healthresearch, VR-based medical education, clinicaldecision and surgery support, and information
support for health tourism.

[Tomohiro Kuroda , Goshiro Yamamoto , Yukiko Mori , Chang Liu]

Learning and Educational Technologies
Information Systems that Support Learning and Education
Our research focuses on information technology that supports
activities in education and learning by analyzing their log data:(1) Development of the infrastructure for accumulation andanalysis of educational big data
(2) Analysis of learning experiences by using life log technologies(3) Knowledge awareness for collaborative learning support(4) Educational systems for Information security and ethics[Hiroaki Ogata , Rwitajit Majumdar , Izumi Horikoshi]

Example of a learning analytics system(LEAF/BookRoll/LogPallet)

Data Engineering and Platform Research
Research in support ofinternet-scale data infrastructure
Centering on the configuration of distributed systems
ranging from several computers to tens of thousands ofcomputers in size, this labor atory’s research addresses
topics from core systems for the handling of big data tomethods for analysis of such data. Specific topics include:
Peer-to-peer (decentralized-distributed system) algorithms
Large-scale distributed system simulations
Blockchain networks
Decentralized-distributed machine learning methods
Social graph analysis methods[Kazuyuki Shudo , Shiori Hironaka]

27

Social Informatics Course

Special Education Program of Social Informatics Course

1. Specialized Subjects and special Lecture
In the first year of the Master’s program, students are introduced to
specialized subjects including the design and analysis of information
systems, and the relationship between information and society. They canacquire the fundamentals of informatics from the ground up throughthese subjects, and even students with less specific experience ininformatics can benefit by combining their informatics and otherexpertise. In addition, we have more than 30 special lectures a year in ourSpecial lecture

course, for which we invite lecturers from various universities, researchSocial Informatics Course

institutes and government offices.

2. Education for Innovation
The Course of Social Informatics joins Kyoto University Design School,
in which students take a course titled "FBL (Field based Learning)/ PBL(Problembased Learning)" that helps them acquire the skills needed tofind and solve real-world problems. In this course, students select one ofa variety of themes, and form small groups to tackle specific problems.
The themes include "Designing a new environment for education: creating
FBL (Field based Learning)/
PBL (Problem based Learning)a place that fosters creativity", "Design of places for conversation overbooks and tea", and "Organization design in crowd-sourcing" Kyoto
University Summer Design School arose from activities mainly led by theourse of Social Informatics, and has become a seasonal tradition insummer, attracting more than 250 participants to share the sameinnovative space. Over 20 different kinds of themes were proposed bycompanies and universities, and were tackled by many students eager tos o l v e r e a l - w o r l d p r o b l e m s : t h e y w e n t i n t o t h e fi e l d , a n d u s e dtrial-and-error methods by actually creating prototypes. The Course ofSocial Informatics proposed themes such as "Predicting Kyoto in 2050 bysimulation" and "Designing safety into apartment buildings: first priority,
disaster prevention".

3. Education for Globalization
Course of Social Informatics has an International Course Program in
the curriculum. Students in the International Course Programs can takelectures in English, receive guidance in English from their supervisors,
and acquire Master's and Doctoral degrees exclusively in English.
Students who complete the course will receive a certificate of completion
for the international course.
Strategic communication seminar

Graduate School of Informatics

28

Advanced Mathematical Sciences Course
29

Mathematical Modeling andAnalysis of Phenomena― In Pursuit of Introduction of Science and Engineering ―The essence of science is to derive principles from observations, thereby
revealing the simple structures that underlie what appear to be complex
phenomena.
Mathematical sciences, in particular, use mathematical approaches toinvestigate "mathematical models", which are derived throughexperimentation.
The modern frontier of mathematical sciences considers various newmathematical models, including those for biological and social phenomena,in addition to more traditional models in natural sciences. These models areinvestigated through analytical approaches as well as numerical simulations
in order to understand the phenomena.
The knowledge thus obtained is employed to develop innovative technologies
or to predict future developments, and new methodologies are devised fordeeper understanding of phenomena.
In its research and educational activities, our course pays particularattention to large-scale and/or highly non-linear complex phenomena.
Particularly in education, we aim at providing students with both scientificperspectives, which help them to understand principles, and engineering
perspectives which are useful in solving real problems in manufacturing.
This two-pronged approach gives students comprehensive overviews on
both science and engineering in mathematical sciences.

Advanced Mathematical Sciences Course

An Invitation to Mathematical Sciences

Advanced Mathematical Sciences Course

In Graduate School of Informatics, we regard "mathematical modeling and analysis
of natural, social and biological phenomena" as an important foundation of what we
aim at; i.e., "informatics" in the broad sense of the term, which includes not only
traditional computer science but also related disciplines such as mathematicalsciences, applied physics, etc.
Indeed, we have placed importance on research and education in mathematicalsciences since the establishment of our school in 1998 because they are considered toform the academic basis of our disciplines. At the time of inauguration of the school,
we considered it appropriate to name ourselves the "Department of Applied Analysis
and Complex Dynamical Systems", since "complex dynamical systems" was the phrasethat symbolized the advanced mathematical sciences of the time. However, therelentless advancement of mathematical sciences gradually made "complex dynamicalsystems" not necessarily the best phrase to express what we are. Meanwhile,development of computers and networks has made it possible to deal more easily
with so-called “big data,” thus making computers and networks increasingly
important in social life as well as in research and education in mathematical modelingand analysis of natural, social and biological phenomena. With these developments in
mind, we have decided to change the name of our department to "Department ofAdvanced Mathematical Sciences," as of April 2017. We believe that the new namereaffirms and clarifies our directions for both education and research.
In the broad research area of "advanced mathematical sciences," we are particularlyinterested in "mathematical modeling and computer simulations of phenomena,"
which constitute the basis of our activities. Computer simulations and data analyses
are now vitally important in several branches of science and engineering. New
applications of computational methods are found in biology, social sciences, etc. and,of course, as more conventional applications in mechanics. These new trends inmathematical modeling of phenomena call for new methodologies, including
probabilistic and fractal approaches in addition to classical differential equations anddiscrete models. New developments in computer simulation include large-scale,
high-performance computing, use of new computational environments such as
multiple-precision arithmetics, etc. Our course conducts research and education insuch new and advanced areas of mathematical sciences.
Traditionally in Japan, fundamental education in mathematical sciences has been
carried out in schools of science and more application-oriented education has been
provided by engineering schools. However, despite its small size, our course has somefaculty members with science backgrounds and others with engineering backgrounds.
Our aim is to "integrate science and engineering" and develop students withcomprehensive perspectives of advanced
mathematical sciences. We also emphasize
individualized teaching as well as independent
learning by each student, in line with Kyoto
University’s principle of "self-learning based on
dialogue."

Toshio Aoyagi

Professor, Advanced Mathematical Sciences Course

Toshio Aoyagi, Sc.D., earned his doctoral degree from Kyoto University Graduate
School of Science in 1993. Since graduating, he has held such positions as Research
Associate, Department of Applied Mathematics and Physics, Kyoto University Faculty ofEngineering and Lecturer and then Associate Professor, Department of Applied
Analysis and Complex Dynamical Systems, Kyoto University Graduate School ofInformatics, before assuming his present position in 2014. His research specialties are
non-linear physics and theoretical neuroscience, with a focus on the analysis ofrhythmic phenomena and research into coupled dynamical systems on networks. He isa member of the Physical Society of Japan, Japan Neuroscience Society, JapaneseSociety for Mathematical Biology, and Japanese Neural Network Society. 2023
Chairperson of Course.

Graduate School of Informatics30

Advanced Mathematical Sciences Course

Outline■Laboratory and Teaching Staff
Laboratory

Teaching Staff

Yuusuke Iso/Professor Jun Kigami/Professor Hiroshi Fujiwara/Associate Professor
Daisuke Shiraishi/Associate Professor Masayoshi Kubo/Senior Lecturer
Li Douglas/Program-Specific Senior Lecturer Daisuke Kawagoe/Assistant Professor

Applied Analysis
Nonlinear Physics(Nonlinear Dynamics andComputational Statistical Physics)

Nonlinear Physics

Syuji Miyazaki/Senior Lecturer Kenji Harada/Assistant Professor(Non-equilibrium Physics andTheoretical Neuroscience)

Toshio Aoyagi/Professor Jun-nosuke Teramae/Associate Professor
Hiroki Tutu/Assistant Professor

Computational Mechanics

Hitoshi Yoshikawa/Associate Professor Kazuki Niino/Assistant Professor

Industrial Mathematics

Satoshi Taguchi/Professor Tetsuro Tsuji/Associate Professor

Statistical Signal
Processing

Kazunori Hayashi/Professor (Secondary appointment : Institute of Liberal Arts and Sciences)■Curriculum of 㻯㼡㼞㼞㼕㼏㼡㼘㼡㼙㻌㼛㼒㻌㻭㼐㼢㼍㼚㼏㼑㼐㻌㻹㼍㼠㼔㼑㼙㼍㼠㼕㼏㼍㼘㻌㻿㼏㼕㼑㼚㼏㼑㼟㻌㻯㼛㼡㼞㼟㼑
Advanced Mathematical Sciences Course㻰㼛㼏㼠㼛㼞㼍㼘㻌㻼㼞㼛㼓㼞㼍㼙㻌㻔㻵㼚㼒㼛㼞㼙㼍㼠㼕㼏㼟㻕ƌdϯ㻰㼛㼏㼠㼛㼞㼍㼘㻌㼀㼔㼑㼟㼕㼟nĚ

2㻿㼡㼎㼖㼑㼏㼠㼟㻌㼜㼞㼛㼢㼕㼐㼑㼐㻌㼎㼥㻌㼠㼔㼑㻌㻯㼛㼡㼞㼟㼑㻌㻔㼠㼛㼠㼍㼘㻌㻢㻌㼏㼞㼑㼐㼕㼠㼟㻕㻿㼑㼙㼕㼚㼍㼞㻌㼛㼚㻌㻹㼍㼠㼔㼑㼙㼍㼠㼕㼏㼍㼘㻌㻿㼏㼕㼑㼚㼏㼑㼟㻌㻔㻭㼐㼢㼍㼚㼏㼑㼐㻕㻌E 䠄㻞㼏㼞㼑㼐㼕㼠㼟㻌 㻹㼍㼚㼐㼍㼠㼛㼞㼥䠅㻿㼑㼙㼕㼚㼍㼞㻌㼛㼚㻌㻭㼜㼜㼘㼕㼑㼐㻌㻭㼚㼍㼘㼥㼟㼕㼟㻌㻔㻭㼐㼢㼍㼚㼏㼑㼐㻌㻭㻌㻒㻌㻮㻕㻌E 䠄㻞㻌㼏㼞㼑㼐㼕㼠㼟㻌㼑㼍㼏㼔䠅㻘㻌㻿㼑㼙㼕㼚㼍㼞㻌㼛㼚㻌㻺㼛㼚㼘㼕㼚㼑㼍㼞㻌㻼㼔㼥㼟㼕㼏㼟㻌㻔㻭㼐㼢㼍㼚㼏㼑㼐㻌㻭㻌㻒㻌㻮㻕㻌E
䠄㻞㻌㼏㼞㼑㼐㼕㼠㼟㻌㼑㼍㼏㼔䠅㻘㻌㻿㼑㼙㼕㼚㼍㼞㻌㼛㼚㻌㻭㼜㼜㼘㼕㼑㼐㻌㻹㼍㼠㼔㼑㼙㼍㼠㼕㼏㼍㼘㻌㻿㼏㼕㼑㼚㼏㼑㼟㻌㻔㻭㼐㼢㼍㼚㼏㼑㼐㻌㻭㻌㻒㻌㻮㻕E 䠄㻞㻌㼏㼞㼑㼐㼕㼠㼟㻌㼑㼍㼏㼔䠅sƚ

1㻾㼑㼟㼑㼍㼞㼏㼔㻌㻳㼡㼕㼐㼍㼚㼏㼑

㻹㼍㼟㼠㼑㼞䇻㼟㻌㻼㼞㼛㼓㼞㼍㼙㻌㻔㻵㼚㼒㼛㼞㼙㼍㼠㼕㼏㼟㻕㻹㼍㼟㼠㼑㼞䇻㼟㻌㼀㼔㼑㼟㼕㼟

−6−

nd

2

㻿㼡㼎㼖㼑㼏㼠㼟㻌㼜㼞㼛㼢㼕㼐㼑㼐㻌㼎㼥㻌㼠㼔㼑㻌㻯㼛㼡㼞㼟㼑㻌㻔㻻㼜㼠㼕㼛㼚㼍㼘㻌㻤㻌㼏㼞㼑㼐㼕㼠㼟㻌㼛㼞㻌㼙㼛㼞㼑㻕㻿㼑㼙㼕㼚㼍㼞㻌㻿㼡㼎㼖㼑㼏㼠㼟㻌㻭㼐㼢㼍㼚㼏㼑㼐㻌㻿㼡㼎㼖㼑㼏㼠㼟䠄㻭㻌㼍㼚㼐㻌㻮㻌㼍㼞㼑㻌㼛㼒㼒㼑㼞㼑㼐㻌㼑㼢㼑㼞㼥㻌㼛㼠㼔㼑㼞㻌㼥㼑㼍㼞䠅㻌㻿㼑㼙㼕㼚㼍㼞㻌㼛㼚㻌㻭㼜㼜㼘㼕㼑㼐㻌㻭㼚㼍㼘㼥㼟㼕㼟ϩ㻿㼑㼙㼕㼚㼍㼞㻌㼕㼚㻌㻺㼛㼚㼘㼕㼚㼑㼍㼞㻌㻼㼔㼥㼟㼕㼏㼟ϩ㻿㼑㼙㼕㼚㼍㼞㻌㼕㼚㻌㻭㼜㼜㼘㼕㼑㼐㻌㻹㼍㼠㼔㼑㼙㼍㼠㼕㼏㼍㼘㻌㻿㼏㼕㼑㼚㼏㼑㼟ϩ䠄㼀㼔㼛㼟㼑㻌㼜㼘㼍㼚㼚㼕㼚㼓㻌㼠㼛㻌㼜㼞㼛㼏㼑㼑㼐㻌㼠㼛㻌㼐㼛㼏㼠㼛㼞㼍㼘㻌㼜㼞㼛㼓㼞㼍㼙㻦㻭㼟㼟㼕㼓㼚㼑㼐㻌㼠㼛㻌㻹㻞㻘㻌㻠㻌㼏㼞㼑㼐㼕㼠㼟㻌㼑㼍㼏㼔䠅㼀㼛㼜㼕㼏㼟㻌 㼕㼚㻌 㻰㼕㼒㼒㼑㼞㼑㼚㼠㼕㼍㼘㻌 㻱㼝㼡㼍㼠㼕㼛㼚㼟㻌 㻭㻘㻮㻌 㻔㻞㻌 㼏㼞㼑㼐㼕㼠㼟㻌 㼑㼍㼏㼔㻕㻘㻌 㼀㼛㼜㼕㼏㼟㻌㼕㼚㻌㻺㼛㼚㼘㼕㼚㼑㼍㼞㻌㻭㼚㼍㼘㼥㼟㼕㼟㻌㻭㻘㻮㻌㻔㻞㻌㼏㼞㼑㼐㼕㼠㼟㻌㼑㼍㼏㼔㻕㻌㻘㼀㼛㼜㼕㼏㼟㻌 㼕㼚㻌 㻭㼜㼜㼘㼕㼑㼐㻌 㻭㼚㼍㼘㼥㼟㼕㼟䊠㻘䊡㻔㻝㻌 㼏㼞㼑㼐㼕㼠㻌 㼑㼍㼏㼔㻕㻘㻌㻌㼀㼛㼜㼕㼏㼟㻌㼕㼚㻌㻺㼛㼚㼘㼕㼚㼑㼍㼞㻌㻰㼥㼚㼍㼙㼕㼏㼟㻌㻭㻘㻮㻌㻔㻞㻌㼏㼞㼑㼐㼕㼠㼟㻌㼑㼍㼏㼔㻕㻌㻘㼀㼛㼜㼕㼏㼟㻌 㼕㼚㻌 㻺㼛㼚㼑㼝㼡㼕㼘㼕㼎㼞㼕㼡㼙㻌 㻼㼔㼥㼟㼕㼏㼟㻌㻭㻘㻮㻌 㻔㻞㻌 㼏㼞㼑㼐㼕㼠㼟㻌 㼑㼍㼏㼔㻕㻌 㼀㼛㼜㼕㼏㼟㻌㼕㼚㻌㻺㼛㼚㼘㼕㼚㼑㼍㼞㻌㻼㼔㼥㼟㼕㼏㼟䊠㻘䊡㻔㻝㻌㼏㼞㼑㼐㼕㼠㻌㼑㼍㼏㼔㻕㻘㻌㼀㼛㼜㼕㼏㼟㻌 㼕㼚㻌㻯㼛㼙㼜㼡㼠㼍㼠㼕㼛㼚㼍㼘㻌 㻹㼑㼏㼔㼍㼚㼕㼏㼟㻌 㻭㻘㻮㻌 㻔㻞㻌 㼏㼞㼑㼐㼕㼠㼟㻌 㼑㼍㼏㼔㻕㻌 㼀㼛㼜㼕㼏㼟㻌㼕㼚㻌㻹㼍㼠㼔㼑㼙㼍㼠㼕㼏㼍㼘㻌㻿㼏㼕㼑㼚㼏㼑㼟㻌㻭㻘㻮㻌㻔㻞㻌㼏㼞㼑㼐㼕㼠㼟㻌㼑㼍㼏㼔㻕㻘㻌㼀㼛㼜㼕㼏㼟㻌㼕㼚㻌㻭㼜㼜㼘㼕㼑㼐㻌㻹㼍㼠㼔㼑㼙㼍㼠㼕㼏㼍㼘㻌㻿㼏㼕㼑㼚㼏㼑㼟䊠㻘䊡㻔㻝㻌㼏㼞㼑㼐㼕㼠㻌㼑㼍㼏㼔㻕㻮㼍㼟㼕㼏㻌㻿㼡㼎㼖㼑㼏㼠㼟(㻭㻌㼍㼚㼐㻌㻮㻌㼍㼞㼑㻌㼛㼒㼒㼑㼞㼑㼐㻌㼑㼢㼑㼞㼥㻌㼛㼠㼔㼑㼞㻌㼥㼑㼍㼞㻕st1㻭㼜㼜㼘㼕㼑㼐㻌 㻭㼚㼍㼘㼥㼟㼕㼟㻌 㻭㻘㻮㻌 㻔㻞㻌 㼏㼞㼑㼐㼕㼠㼟㻌 㼑㼍㼏㼔㻕㻘㻌 㻺㼛㼚㼘㼕㼚㼑㼍㼞㻌㻼㼔㼥㼟㼕㼏㼟㻌㻭㻘㻮㻌㻔㻞㻌㼏㼞㼑㼐㼕㼠㼟㻌㼑㼍㼏㼔㻕㻌㻘㻌㻭㼜㼜㼘㼕㼑㼐㻌㻹㼍㼠㼔㼑㼙㼍㼠㼕㼏㼍㼘㻌㻿㼏㼕㼑㼚㼏㼑㼟㻌㻭㻘㻮㻌㻔㻞㻌㼏㼞㼑㼐㼕㼠㼟㻌㼑㼍㼏㼔㻕㻳㼑㼚㼑㼞㼍㼘㻌 㻿㼡㼎㼖㼑㼏㼠㼟㻌 㼜㼞㼛㼢㼕㼐㼑㼐㻌 㼎㼥㻌 㼠㼔㼑㻌 㻿㼏㼔㼛㼛㼘䠄㻯㼛㼙㼜㼡㼘㼟㼛㼞㼥㻌 㼑㼘㼑㼏㼠㼕㼢㼑㻌㻌㻌㻌㻌㻌㻌㻞㻌㼛㼞㻌㼙㼛㼞㼑㻌㼏㼞㼑㼐㼕㼠㼟㻌㼍㼚㼐㻌㻠㻌㼏㼞㼑㼐㼕㼠㼟㻌㼛㼞㻌㼘㼑㼟㼟㻌㼒㼛㼞㻌䖂㻌㼟㼡㼎㼖㼑㼏㼠㼟䠅㻌㻵㼚㼠㼑㼞㼐㼕㼟㼏㼕㼜㼘㼕㼚㼍㼞㼥㻌㻿㼡㼎㼖㼑㼏㼠㼟㻌㼛㼒㻌㼠㼔㼑㻌㻼㼑㼞㼟㼜㼑㼏㼠㼕㼢㼑㼟㻌㼕㼚㻌㻵㼚㼒㼛㼞㼙㼍㼠㼕㼏㼟䖂㻼㼑㼞㼟㼜㼑㼏㼠㼕㼢㼑㼟㻌㼕㼚㻌㻵㼚㼒㼛㼞㼙㼍㼠㼕㼏㼟㻌㻝㻌䖂㻼㼑㼞㼟㼜㼑㼏㼠㼕㼢㼑㼟㻌㼕㼚㻌㻵㼚㼒㼛㼞㼙㼍㼠㼕㼏㼟㻌㻞㻌䖂㻼㼑㼞㼟㼜㼑㼏㼠㼕㼢㼑㼟㻌㼕㼚㻌㻵㼚㼒㼛㼞㼙㼍㼠㼕㼏㼟㻌㻟㻌䖂㻼㼑㼞㼟㼜㼑㼏㼠㼕㼢㼑㼟㻌㼕㼚㻌㻵㼚㼒㼛㼞㼙㼍㼠㼕㼏㼟㻌㻠㻌䖂㻼㼑㼞㼟㼜㼑㼏㼠㼕㼢㼑㼟㻌㼕㼚㻌㻵㼚㼒㼛㼞㼙㼍㼠㼕㼏㼟㻌㻡㻌㻔㻞㻌㼏㼞㼑㼐㼕㼠㼟㻌㼑㼍㼏㼔㻕㻼㼞㼕㼛㼞㻌㼠㼛
㼍㼐㼙㼕㼟㼟㼕㼛㼚31㻯㼍㼘㼏㼡㼘㼡㼟

㻸㼕㼚㼑㼍㼞㻌㻭㼘㼓㼑㼎㼞㼍㻲㼡㼚㼐㼍㼙㼑㼚㼠㼍㼘㼟㻌㼛㼒㻌㻻㼞㼐㼕㼚㼍㼞㼥㻌㻰㼕㼒㼒㼑㼞㼑㼚㼠㼕㼍㼘㻌㻱㼝㼡㼍㼠㼕㼛㼚㼟㻿㼑㼙㼕㼚㼍㼞㻌㼛㼚㻌㻭㼜㼜㼘㼕㼑㼐㻌㻭㼚㼍㼘㼥㼟㼕㼟Ϩ㻿㼑㼙㼕㼚㼍㼞㻌㼕㼚㻌㻺㼛㼚㼘㼕㼚㼑㼍㼞㻌㻼㼔㼥㼟㼕㼏㼟Ϩ㻿㼑㼙㼕㼚㼍㼞㻌㼕㼚㻌㻭㼜㼜㼘㼕㼑㼐㻌㻹㼍㼠㼔㼑㼙㼍㼠㼕㼏㼍㼘㻌㻿㼏㼕㼑㼚㼏㼑㼟Ϩ㻭㼟㼟㼕㼓㼚㼑㼐㻌㼠㼛㻌㻹㻝㻘㻌㻠㻌㼏㼞㼑㼐㼕㼠㼟㻌㼑㼍㼏㼔㻿㼑㼙㼕㼚㼍㼞㼟㻌㼍㼚㼐㻌㼑㼤㼑㼞㼏㼕㼟㼑㼟㻌㼒㼛㼞㻌㻹㼍㼟㼠㼑㼞䇻㼟㻌㼠㼔㼑㼟㼕㼟㻌㻔㻹㼍㼚㼐㼍㼠㼛㼞㼥㻌㻤㻌㼏㼞㼑㼐㼕㼠㼟㻕㻭㼐㼢㼍㼚㼏㼑㼐㻌㻿㼠㼡㼐㼥㻌㼕㼚㻌㻹㼍㼠㼔㼑㼙㼍㼠㼕㼏㼍㼘㻌㻿㼏㼕㼑㼚㼏㼑㼟㻌㻞㻌㻔㻭㼟㼟㼕㼓㼚㼑㼐㻌㼠㼛㻌㻹㻞㻘㻌㻢㻌㼏㼞㼑㼐㼕㼠㼟㻕㻭㼐㼢㼍㼚㼏㼑㼐㻌㻿㼠㼡㼐㼥㻌㼕㼚㻌㻹㼍㼠㼔㼑㼙㼍㼠㼕㼏㼍㼘㻌㻿㼏㼕㼑㼚㼏㼑㼟㻌㻝㻌㻔㻭㼟㼟㼕㼓㼚㼑㼐㻌㼠㼛㻌㻹㻝㻘㻌㻞㻌㼏㼞㼑㼐㼕㼠㼟㻕䖂㻼㼑㼞㼟㼜㼑㼏㼠㼕㼢㼑㼟㻌㼕㼚㻌㻼㼘㼍㼠㼒㼛㼞㼙㻌㻿㼠㼡㼐㼕㼑㼟㻌㻔㻞㻌㼏㼞㼑㼐㼕㼠㼟㻕㻘㻯㼛㼙㼜㼡㼠㼍㼠㼕㼛㼚㼍㼘㻌㻿㼏㼕㼑㼚㼏㼑㻘㻌㻵㼚㼠㼞㼛㼐㼡㼏㼠㼕㼛㼚㻔㻞㻌㼏㼞㼑㼐㼕㼠㼟㻕㻘㻌㻯㼛㼙㼜㼡㼠㼍㼠㼕㼛㼚㼍㼘㻌㻿㼏㼕㼑㼚㼏㼑㻘㻌㻱㼤㼑㼞㼏㼕㼟㼑㻌㻭㻌㻔㻝㻌㼏㼞㼑㼐㼕㼠㼟㻕㻘㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌䖂㻵㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㼍㼚㼐㻌㻵㼚㼠㼑㼘㼘㼑㼏㼠㼡㼍㼘㻌㻼㼞㼛㼜㼑㼞㼠㼥㻔㻞㻌㼏㼞㼑㼐㼕㼠㼟㻕㻘㻌㻵㼚㼚㼛㼢㼍㼠㼕㼛㼚㻌㼍㼚㼐㻌㻵㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻔㻞㻌㼏㼞㼑㼐㼕㼠㼟㻕㻘㻌㻵㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻭㼚㼍㼘㼥㼟㼕㼟㻌 㼍㼚㼐㻌 㻹㼍㼚㼍㼓㼑㼙㼑㼚㼠㻔㻞㻌㼏㼞㼑㼐㼕㼠㼟㻕㻘㻌 㻵㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻭㼚㼍㼘㼥㼟㼕㼟㻌㼍㼚㼐㻌㻹㼍㼚㼍㼓㼑㼙㼑㼚㼠㻘㻌㻱㼤㼑㼞㼏㼕㼟㼑㻌㻔㻝㻌㼏㼞㼑㼐㼕㼠㼟㻕㻘㻌㻿㼛㼏㼕㼍㼘㻯㼛㼚㼠㼞㼕㼎㼡㼠㼕㼛㼚㼟㻌㼠㼔㼞㼛㼡㼓㼔㻌㻵㼚㼒㼛㼞㼙㼍㼠㼕㼏㼟㻔㻝㻌㼏㼞㼑㼐㼕㼠㼟㻕㻘㻌㻵㼚㼠㼑㼞㼚㼟㼔㼕㼜㻌㼕㼚㼠㼔㼑㻌㻲㼕㼑㼘㼐㻌㼛㼒㻌㻵㼚㼒㼛㼞㼙㼍㼠㼕㼏㼟㻌㻔㻝㻌㼏㼞㼑㼐㼕㼠㼟㻕㻘㻹㼑㼏㼔㼍㼚㼕㼏㼟㻌㼛㼒㻌㻼㼍㼞㼠㼕㼏㼘㼑㼟㻘㻌㻿㼥㼟㼠㼑㼙㼟㻌㼛㼒㻌㻼㼍㼞㼠㼕㼏㼘㼑㼟㻘㻌㼍㼚㼐㻌㻾㼕㼓㼕㼐㻌㻮㼛㼐㼕㼑㼟㻿㼜㼑㼏㼕㼒㼕㼏㻌㼟㼡㼎㼖㼑㼏㼠㼟㻌㼜㼞㼛㼢㼕㼐㼑㼐㻌㼎㼥㻌㼠㼔㼑㻌㼟㼏㼔㼛㼛㼘㻭㼏㼍㼐㼑㼙㼕㼏㻌㻲㼛㼡㼚㼐㼍㼠㼕㼛㼚㼟㻌㼍㼠㻌㼁㼚㼐㼑㼞㼓㼞㼍㼐㼡㼍㼠㼑㻌㻸㼑㼢㼑㼘㻌㼒㼛㼞㻌㻿㼠㼡㼐㼥㻌㼍㼠㻌㼠㼔㼑㻌㻯㼛㼡㼞㼟㼑㻺㼛㼠㼑㻦㻌㻿㼡㼎㼖㼑㼏㼠㼟㻌㼙㼍㼞㼗㼑㼐㻌㼣㼕㼠㼔㻌㼠㼔㼑㻌㼘㼑㼠㼠㼑㼞㻌䇾㻱䇿㻌㼣㼕㼘㼘㻌㼎㼑㻌㼜㼞㼛㼢㼕㼐㼑㼐㻌㼕㼚㻌㻱㼚㼓㼘㼕㼟㼔㻚㻲㼡㼚㼐㼍㼙㼑㼚㼠㼍㼘㼟㻌㼛㼒㻌㻯㼛㼙㼜㼘㼑㼤㻌㻭㼚㼍㼘㼥㼟㼕㼟

Advanced Mathematical Sciences Course
Admission, Curriculum and Other Efforts in Our Course
In study and research of advanced mathematical sciences, one
needs both basic mathematical skills as well as knowledge of
his/her specialist field. Accordingly, in the entrance exam, allapplicants for our Master's course are required to solve basic
problems in linear algebra and calculus together with one problemof their choice related to their respective specialties. Finaldecisions on acceptance are made through interviews with those
applicants who score above a certain level in the written exam,
since it is not desirable to make such decisions based only on the
results of the written exam where one point may be the difference
between a pass mark and failure. The interviews will be conductedby all the faculty members in order to determine if applicant’sinterests match the expertise of our faculties. In the entrance
exam for the Ph.D. course, final decisions on acceptance are madebased on the achievements of each candidate in his/her researchwork, which are evaluated in interviews conducted by all thefaculty members.
Our curriculum for the Master course consists of both generaland specialized subjects. All students are advised to take three
general subjects, which help them to develop both scientific andengineering perspectives in mathematical sciences. Researchadvice is given mainly on a one-to-one basis, taking into accounteach student’s aptitude. For those wishing to go on to Ph.D.
courses, we provide Seminar II (for second-year students), which
is designed to give students opportunities to learn advanced topics
in addition to receiving standard research mentoring. In the Ph.D.
course, students can receive mentoring not only from theiradvisers but also from other professors in our course andaffiliated professors from science and engineering schools. Thissystem gives students access to a broader spectrum ofstate-of-the-art knowledge in the mathematical sciences.
Students thus have opportunities to deepen their expertise and toobtain a broad appreciation of mathematical sciences from boths c i e n t i fi c a n d e n g i n e e r i n g
perspectives, which we believe
to be a unique feature of theeducation provided by ourcourse.
In addition, almost every
year, we offer open, public
seminars on topical subjects.

Applied Analysis Laboratory
Advanced Mathematical Sciences Course

Analysis in the 21st Century■Numerical Analysis of (Partial) Differential Equations, Numerical andMathematical Analyses
I am interested in both mathematical and numerical analyses in research
regarding the determination of unknown coefficients and other inverse
problems, as well as boundary value problems and other forward problems
with respect to partial differential equations that describe mechanical andphysical phenomena.
[Yuusuke Iso]
■Fractal Analysis, Fractal Geometry
I am interested in the mathematical theory of problems concerning heat
and wave propagation in fractal concept models ‒new models for the natural
world.
[Jun Kigami]■Numerical Analysis of Ill-posed Problems, Design and Implementation ofMulti-precision Arithmetic Environments
I am interested in research concerning regularization methods andnumerical analysis of multiple-precision calculations with the aim of solving
inverse problems that occur in mechanics and geophysics. [ Hiroshi Fujiwara]

Numerical Simulation of Light Propagation in a
Human Brain■Structure of Brownian Motion and Random Walk
How does the trace of Brownian motion look like? What can we say about
the structure of random walk trace? Such questions have fascinatedprobabilists and mathematical physicists for a long time, and they continue tobe an unending source of challenging problems. I am interested in the natureof sample paths of these fundamental processes.
[Daisuke Shiraishi]■Inverse Problem Analysis, Numerical Analysis of (Partial) Differential
Equations, Partial Differential Equations, Brain Model Mathematical
Research
I mathematically analyze partial differential equations that appear inmathematical physics and mathematically and numerically analyze theinverse problems found in these partial differential equations, where theunknown coefficients of these inverse problems are determined by observed
data.
[Masayoshi Kubo]

Uniform spanning tree (solid line) and dual path(dotted line)■Numerical Analysis of Differential Equations and Data-Driven Science
I am interested in numerical simulations of differential equations and data driven modelling in natural science, technology andsocial sciences.
[Li Douglas]■Integro-Differential Equations, Spectral Analysis
I study regularity of solutions to integro-differential equations appearing in kinetic theory and optics.
analysis on boundary integral operators which are related to the theory of elasticity.

Also, I work in spectral[Daisuke Kawagoe]

Graduate School of Informatics

32

Advanced Mathematical Sciences Course

OutlineNonlinear Physics Laboratory■Group of Non-equilibrium Physics andTheoretical Neuroscience■From nonlinear physics to theory of networks,
as well as living and neural systems

I am particularly interested in systems composed ofmany simple elements that, through cooperativeinteractions, come to exhibit complex behavior andhigh-level functions, such as not only many physicalsystems, but also biological and social systems.
Co-evolution involving the intricate interplay between the
dynamics of the network and the elements is a key concept
for understanding the self-organized, flexible nature of
real-world network systems. I study such cooperative
phenomena in systems of this kind, focusing on rhythmic
phenomena and chaos from the perspective of nonlinear[Toshio Aoyagi]
dynamics.

Neural systems and social networks are nonlinear dynamic systems
that have a high degree of freedom and develop as their dynamic
elements change the structure of their interconnections.

■Nonlinear physics of computation and learningin the brain

The brain is a highly complex network composed ofabout 100 billion of neurons. Spike propagation along the
network and plasticity of synaptic connection of themcause high-dimensional nonlinear dynamics, which is thenature of neural computation and learning. Central
questions, however, still remain elusive.
What is intelligence? What is underlying principles ofneural computation and learning? Our recent study, fore x a m p l e , r e v e a l s s i g n i fi c a n t r o l e s o f s p o n t a n e o u s
fl u c t u a t i o n i n n e u r a l c o m p u t a t i o n . B y i n t e g r a t i n gneuroscience, computer science, and nonlinear physics,
here we are trying to answer these questions and trying todevelop fully brain-inspire AIs.
[Jun-nosuke Teramae]

Conceptual diagram of neurons (pink) in the brain interacting throughsynapses (green circles) that transmit impulse currents in a process
called “spike firing.” The process can be mathematically analyzed bymodeling the states of neurons and synapses as random variables.

■Mathematical models for molecular machines

Biological molecular motors are amazing machinesthat generate useful movement (as a pump or a porter).
The relationship between the structures of proteins andtheir highly efficient energy conversion, even under afluctuating environment, remains mysterious. I explore
the underlying principles of such molecular machineswith mathematical models.
[Hiroki Tutu]

Rotary ratchet model consisting of arotor (rigid body consisting of four
points) and a rotor housing (potential)that simulates a Wankel rotaryengine■Group of Nonlinear Dynamics and Computational Statistical Physics■Study on powders and charged particles under cyclic
external forces

Inspired by experiments in which several micron-size charged
corpuscles are injected into an AC trap, a simplified version of a
Paul ion trap, and prior studies into the particle model of ionchannels and its anomalous transport, we mainly study collective
motions of granular materials and charged corpuscles, which are
subject to periodic external force. We also propose mathematicalmodels for numerical analysis that faithfully reproduce
experiment settings and analyze critical phenomena incidental tocollective motions of macroscopic charged corpuscles injected
into an AC trap and various types of bifurcations that occur invibrating powder.
[Syuji Miyazaki]■Statistical physics using computational approaches

Using the computational approach, we advance statisticalphysical research for the emerging properties in complex
systems consisting of many elements with nonlinear interactions.
My research areas range from the atomic scale to the real world,e.g.,quantum critical phenomena and infectious diseases. Basedon tensor network formalism, I am also working on informational
processing technology, such as classical and quantum machinelearning.
[Kenji Harada]

33

It is known that when two different
powders, one of large and one ofsmall grain size, are horizontally
vibrated, the two powders
separate, causing a stripe pattern
to form.
We discovered that when powders,
one of large and one of small
grain size, each one with a size
distribution centered around its
average grain size, are vibrated
horizontally, the two powders
separate, causing a mesh pattern
to form.

Tensor network

Researchers actively study new compression methods forlarge data sets and deep learning models with exponentiallygrowing parameters using tensor network representation.

Advanced Mathematical Sciences Course

Computational Mechanics Laboratory
Computer simulations:development of fast BIEMs and their applications to engineering problems
Numerical simulations provide powerful tools forsolving various problems in science and engineering.
Computational mechanics, together with theoreticaland experimental mechanics, is an effective method ofinvestigating mechanical phenomena in engineering
based on numerical simulations. Our group specializes
in Boundary Integral Equation Methods (BIEM) which
are among major techniques in computational
mechanics. BIEMs are particularly effective in wave andfracture problems. We focus on fast BIEMs and theirapplications to large-scale problems with special
interest in electromagnetic wave propagations inperiodic structures, which have many applications in
optics. Other topics of interest include shape
optimization problems, eigenvalue problems andinverse problems.
[Hitoshi Yoshikawa , Kazuki Niino]

A nanoporous gold mesh (left) and example of numerical computation of its transverse
photo-induced voltage (right)

Advanced Mathematical Sciences Course

Industrial Mathematics Laboratory
Toward new fluid mechanics for
non-equilibrium flows
In our group we investigate the behavior of non-equilibrium flows based on
kinetic theory describing the collective behavior of innumerable particles.
We aim at understanding mechanical and/or thermodynamic properties ofnon-equilibrium flows both theoretically and numerically. We also aim at
elaborating continuum theory and applying it to non-equilibrium flows, by deriving
suitable mathematical models for non-equilibrium flows.
[Satoshi Taguchi]
Recent advances in micro/nanoscale technologies require theunderstanding of transport phenomena in micro/nanoscale and theircontrol. For this reason there is a growing interest in the research ofnon-equilibrium flows. In particular, we aim at investigating moving
boundary problems for non-equilibrium flows and the motion of tinymaterials (or particles) driven by non-equilibrium effects from the view
point of mathematical sciences and from experimental view points. We
also aim at integrating them in industrial applications.
[Tetsuro Tsuji]

Unidirectional flow around a heated U-shaped body (top left)
Flow caused by a discontinuous wall temperature in a vessel (top right)
Flow caused by a rotating sphere impulsively set into motion (bottom left)
The Magnus effect of a spinning microparticle (bottom right)

Statistical Signal Processing Laboratory
Integration of Model-based and Data-driven Approaches
In the discipline of statistical signal processing, we try to systematize
methodologies for extracting useful information from raw observation data.
This has a wide range of applications in telecommunications, as well as
measurement, image processing, and biological signal processing. Statisticalsignal processing is a valuable tool for tackling various kinds of problems
relating to data science, including data sensing, collection, transmission,
analysis, and utilization. We have recently been studying methods for solving
underdetermined linear inverse problems that make use of the sparsity anddiscreteness of signals.
[Kazunori Hayashi]
Example of optical communication receiver configuration using “deep unfolding”
Combining traditional telecommunications knowledge with a data-driven approach makes it
possible to determine all parameters in an optical communication receiver system at once
based on the loss function at output.

Graduate School of Informatics

34

Applied Mathematics and Physics Course

Using Applied Mathematics and Physics to
Seek Solutions to Problems in Engineering/
Natural Systems
In the highly advanced information society of today, we encounter
various situations that involve modeling and analysis of complex andlarge-scale systems, their control, design, and operation.
In these situations, it is extremely important to uncover common
mathematical structures shared by those problems which are seemingly
unrelated, and to develop mathematical methods to solve them, in
addition to acquiring specialized knowledge of individual disciplines suchas information technology, electricity, mechanics, and chemistry.
From this viewpoint, the seven laboratories of the Course of Applied
Mathematics and Physics undertake leading-edge research on appliedmathematical analysis, discrete mathematics, system optimization,
control systems theory, applied mathematical modeling (adjunct unit),
physical statistics, and dynamical system theory.

35

Applied Mathematics and Physics Course

Universality of the Power law andEstablishing Risk Indicators
The discovery and application of a super generalized central limit theorem
Why are power laws so prevalent in nature and the universe? That is the mathematicalquestion that sparked this journey. Power laws, which are different to Gaussian
distributions, can be asymmetric and remarkably diverse. The mathematical basis for such
power laws can be traced back to a generalization of the central limit theorem, stating thatthe sum of random variables converges to a Gaussian distribution. While examining thismathematical basis, we constructed an algorithm to accurately estimate power laws from themassive amounts of data about the world that we now have access to. This has led us todevelop new data analysis methodology for identifying real-time risk indicators for financial
markets. As well as in financial markets, power laws also appear
in many aspects, including physical phenomena in the universe.
A power law is a common mathematical concept that sheds lightin fields as wide-ranging as statistical physics, probability theory,chaos theory, cosmology, financial markets, neuroscience, andnumber theory (Riemann zeta function). Join us on this voyage
of discovery! It has only just begun.

Ken Umeno

Professor, Applied Mathematics and Physics Course

Mathematics and Computers for Solving
Problems through Optimization

Applied Mathematics and Physics Course

Professor Umeno received his Doctoral Degree March 1995 from the University of Tokyo. In
April 1995, he was a special postdoctoral researcher at the Institute of Physical and Chemical
Research (RIKEN). From 1998 until he joined Kyoto University in 2012, he worked for Japan’ s
Ministry of Posts and Telecommunications (currently the National Institute of Information andCommunications Technology). From 2005 to 2010, he was the Laboratory Head of the Next
Generation Mobile Laboratory of RIKEN. He has been a Professor with Graduate School ofInformatics, Kyoto University, since 2012. His research specialty is chaos theory, complex
systems, and statistical mechanics. His recent invention is correlation analysis of GNSS datafor detecting ionospheric Precursors before large earthquakes and is currently investigating inuncovering the physical mechanism to generate electro-magnetic anomalies. Together
earthquake, he is also interested in physically inspired model of Financial Crisis as criticalphenomena of phase transition in statistical mechanics.

As the concept of “optimization” has become increasingly familiar to people, technicalterms such as “optimization” and “optimal solution” have made their way into everyday
language. In the field of applied mathematics, we first describe “everyday optimization” as an“mathematical optimization model” that enables computers to deal with. Then we develop
algorithms to find optimal solutions of such optimization models with large-scale andcomplex real-world applications.
Our laboratory deals mainly with continuous optimization, in which the candidate
optimal solutions are expressed as continuous variables.
Continuous optimization is an essential tool for deep learningand financial engineering. Why not come along and takeadvantage of the power of applied mathematics to help us shapea more optimal societyfor the benefit of many people?

Nobuo Yamashita

Professor, Applied Mathematics and Physics Course

Professor Yamashita received his Doctorate degree in March 1996 from Nara Institute of
Science and Technology. In April 1996 he was a research fellow at Japan Society for the
Promotion of Science. In August 1997 he was appointed an assistant professor at the Section ofApplied Mathematics and Physics, School of Engineering, Kyoto University. In April 2005 hebecame an associate professor at the department of Applied Mathematics and Physics,
Graduate School of Informatics, and In July 2014 he was promoted to professor. His research
specialty is continuous optimization, covering a broad range of fields which include, among
others, large-scale optimization, equilibrium problems, and nonlinear equations.

Graduate School of Informatics

36

Applied Mathematics and Physics Course

Outline■Group and Teaching Staff
Group

Teaching Staff

Applied Mathematical
Analysis

Satoshi Tsujimoto/Professor

Discrete Mathematics

Kazuya Haraguchi/Associate Professor

System Optimization

Nobuo Yamashita/Professor Ellen Hidemi Fukuda/Associate Professor
Hiroyuki Sato/Associate Professor Yuya Yamakawa/Assistant Professor

Control Systems Theory

Kenji Kashima/Associate Professor Kentaro Ohki/Assistant Professor

Physical Statistics

Ken Umeno/Professor Erica Uehara/Senior Lecturer Atsushi Iwasaki/Assistant Professor

Dynamical Systems

Kazuyuki Yagasaki/Professor Mitsuru Shibayama/Associate Professor
Yoshiyuki Yamaguchi/Assistant Professor

Applied Mathematical
Modeling (Adjunct Unit)

Yoichi Nonaka/Adjunct Professor
Yoshiyasu Takahashi/Adjunct Associate Professor (Hitachi Ltd.)■Curriculum of Applied Mathematics
and Physics Course
CurriculumofAppliedMathematicsandPhysicsCourseDoctoralProgram(Informatics)


rd

3 
nd

2 
st

1 


DoctoralThesis
Subjects provided by the Course (total 6 credits including 4 credits from seminars)

Seminar on Applied Mathematics and Physics, Advanced A, B, E (2 credits)
Seminar on Applied Mathematics, Advanced, E Seminar on Applied Mathematical Systems, Advanced, E
Seminar on Mathematical Physics, Advanced, E (2 credits each)ResearchGuidance

Master’sProgram(Informatics)
Master’sThesisndSubjectsprovidedbytheCourseAdvancedSubjects

SeminarSubjects

BasicSubjects(2creditseach)

RecommendedSubjectsprovidedbyother
Courses2 (Optional�12�credits�or�more�from�the�recommended�subjects�provided�by�the�other�Course.�8�or�more�credits�are�required�from�the�subjects�provided�
by�the�Course�and�“Computational�Science,�Introduction”�of�the�general�subject�provided�by�the�School.)�

- 7 -MathematicalAnalysis,Adv.,DiscreteMathematics,Adv.,ControlSystemsTheory,
Adv.,OptimizationTheory,Adv.,PhysicalStatistics,Adv.,DynamicalSystems,Adv.
IntroductiontoMathematicalFinance
(2creditseach)
FinancialEngineering,TopicsinAppliedMathematicsandPhysicsA
TopicsinAppliedMathematicsandPhysicsB (1crediteach)




st

1 

Priorto
admissionOperationsResearchAdv.,MathematicalPhysics,Adv.,SystemsAnalysis,Adv.

GeneralSubjectsprovidedbytheSchool(2creditseach) 
InterdisciplinarySubjectsofthePerspectivesinInformatics
(Mandatory2credits)
PerspectivesinInformatics1 PerspectivesinInformatics2�
PerspectivesinInformatics3E PerspectivesinInformatics4E�
PerspectivesinInformatics5E (2creditseach)



BasicMathematics

Calculus,Linearalgebra,etc.

Preferredto
understandthesubjectsontheright

SeminarinMathematicalAnalysis,Seminarin
DiscreteMathematics,SeminarinSystem
Optimization,SeminarinControlSystemsTheory
SeminarinPhysicalStatistics,SeminarinDynamical
Systems

PatternRecognitionAdv.E(IST),other11subjects

PerspectivesinPlatformStudies(2credits),ComputationalScience,Introduction(2credits),ComputationalScience,ExerciseA(1credit),
InformationandIntellectualProperty(2credits),Innovationand
Information(2credits),InformationAnalysisandManagement(2credits),InformationAnalysisandManagement,Exercise(1credit)
SocialContributionsthroughInformaticsE(1credit)Internshipinthe
FieldofInformaticsE(1credit)

AppliedMathematics

Complexfunctions,Fourieranalysis,
Numericalanalysis,Graphtheory,etc.

MathematicalSystems

Linearprogramming,Optimization,
Controltheory,etc.

Seminarsandexercises
forMaster’sThesis
(Mandatory10credits)

AdvancedStudyinSocial
Informatics2E(Assignedto
M2,5credits)
AdvancedStudyinSocial
Informatics1E�(Assignedto
M1,5credits)

Specific
subjects
providedbytheschool

MathematicalPhysics

Classicaldynamics,Differential
equation,Statisticalmechanics,etc.

Note:�Subjects�marked�with�the�letter�“E”�will�be�provided�in�English.

37AppliedMathematics and Physics Course:空箱，:玉の入った箱

図 3 “箱と玉の系” の時間発展

8

T. KATO, S. TSUJIMOTO, AND A. ZUK2 ソリトンの

この非常にシンプルな箱玉系は，離散可積分系を経由して「連続・離散・超離れる KdV 方程式と関係づく．次節以降で，Figure 1. BBS
この対応関係について解説していく．
Developing algorithms from integrable systems

Applied Mathematical Analysis

We carry out research in the areas of contemporary
soliton research and integrable system research, not only
regarding the applied analysis of orthogonal polynomials
and special functions that are closely associated withintegrable systems, but also regarding the application of
the mathematical methods developed by integrable
system studies to the solution of various problems
hitherto thought to be unrelated to integrable systems

5. BBS with carrier capacity( sLemma
u c h5.1.aThes boxnandum
r i c(BBS)a l withc acarrierl c ucapacityl a tcani obendepicted
a nbyd a l g o r i t h mball esystem
2. KdV 方程式の離散化
development). Our
Group
is
a
pioneer
ina this researchaaafield, and conducts studies into the applied analysis of
本節では，連続な時間・空間上の可積分系である
KdV 方程式
The (simple)
BBS is appeared as the limiting case of the above automaton with k → ∞.
integrable
systems in the development of algorithms and5.1. BBS with carrier capacity k  1.
の解析手法のごく簡単な紹介と，可積分性を保存する離散化おother new branches of mathematics from the perspectiveaa
いて考察する．
of computer science.
1

0

1

0

We have two operator recursions
(12)

a
a
2.1. KdV a方程式
a

0 0
0



0; k1

0

k

2



1

,

a1  a1;k1 [SatoshiTsujimoto]

0
a0

0
a1

.

We can describe theDiﬀerential
action of a 0 and a 1 on the binary sequences of length n by the matrices a 0( n ) and a 1( n ) .
From the definition of our automaton, they satisfy the following recurrence relations:

KdV(Korteweg-deVries)
方程式Waves
Shallow-water
Equation
a , + u
ut +a 6uu
0
x
xxx =


a0(0)  a0 ,

(0)
1

1


0
0
a ( n ) a1( n )  0, a1( n +1) .
a0
a0( n ) a1( n )0 Recurrence 0Computational Algorithm( n +1)は，無限遠方で十分速く u → 0 に収束するという境界条件のt, x に関する関数であり，u の添字は，各変つ．ここで，ut+1は，
11
uMn+1−1 uant =+ aδ 3+ at + −a 3 t+1u)，u4 xxx = ∂( uu/∂x．
例：ut = ∂u/∂tn
n+1

Relation
We can consider the following
transition operator:
(n )
k1

(n )
0

(n )∗
0

(n )
1

(n )∗
1

In section 7, we verify that these transition operators satisfy Markov properties.

Automaton
5.2. BBS with carrier
capacity k  2. In analogy to k  1 case, for k  2, we can consider the following
operators.

Box-Ball System

a0







a2





a0 a1
0 a2
0
a0;k2  linking, a1  a1;k2 and discrete, a2systems,
 a2;k2 
a0 
Theory0 0 continuous
a0 0
a1

Ultra-discrete soliton

and automata

0
a2



.

Discrete Mathematics
Exploring the complexity of discrete mathematics problems and developing algorithms
Topics in discrete mathematics, such as the graphs andnetworks used to represent systems, schedules toenhance the efficiency of production, and the logicalanalysis of large volumes of data, are closely related toapplications of research results. We explore thecomplexity of the computation used to solve these
problems; design exact and approximation algorithms;
develop tabu search algorithms, genetic algorithms andother metaheuristic algorithms; and apply them to solving
actual problems.
[Kazuya Haraguchi]

A puzzle in which you have to fit thepieces into a box of fixed width without
any of the rectangular pieces
overlapping and try to make the heightof the packed pie ces as low as possible.

Applied Mathematics and Physics Course

We have tree operator recursions

a1

The structural formula of a compound expected
to have a certain target value of heat of
combustion. It was constructed by formulating
and solving an inverse problem of a prediction
model learned from a database of compounds inthe form of a mixed-integer optimization problem.

Graduate School of Informatics

38

Applied Mathematics and Physics Course

OutlineSystem Optimization
Optimization is the keyword for solving problems
We conduct education and research regarding thetheory and methodology of system optimization,
which plays an important role as a mathematical
approach that is used to resolve many different kindsof practical problems. In particular, we develop
efficient mathematical optimization approaches toactual large-scale systems, complex nonlinear
systems, and systems with uncertainty, as well asbasic research regarding mathematical

Optimal solutions of an unconstrained problem

programming.
[Nobuo Yamashita , Ellen Hidemi Fukuda , Hiroyuki Sato , Yuya Yamakawa]Control Systems theory
Mathematical approaches to modeling and control
We carry out teaching and research regarding themathematical methodologies of modeling, analysis
and design of control systems, and their application
with the aim of developing practical and expansive
control theories. Our main research themes arerobust control, control systems with input/output
constraints, networked control systems, algebraic
system theory, mathematical optimization in control,
stochastic realization, system identification andquantum control theory.
[Kenji Kashima , Kentaro Ohki]
Conceptual diagram of a control system design

39

Applied Mathematics and Physics Course

Physical Statistics
The mathematical studies on dynamics of coupled multi-element network systems
and design theory of complex engineering systems
We aim to gain a mathematical and unified understandingof the complex and diverse phenomena that arise out of
the intense mutual interactions of multiple elements(units) in a system and apply this understanding to
information processing and design of complex engineeringsystems. For example, we will use stochastic process
theory, ergodic theory, statistical physics, dynamicalsystem theory, computer simulations, and large-scale

The distribution of vertices when a random network is
extended by applying an external field to both ends of the
network is computed from the graph Laplacian.

Polymer physics

Applied Mathematics and Physics Course

Chaos code for signal analysis
and multiuser
communications systemdata processing techniques to analyze information
processing and performance evaluation in neuralnetworks; the structure of the Internet and other complex
networks such as social media systems, and the
propagation of information within them; and the dynamical
properties of price change, stock markets and othereconomic phenomena.
[Ken Umeno , Erika Uehara , Atsushi Iwasaki]

Dynamical Systems
Looking into the world through dynamical systems theory
Our research purpose is to analyze complicated phenomena such as chaosand bifurcations in various systems appearing in natural science, engineeringand other disciplines using dynamical systems approaches, and apply them todevelop novel engineering technologies. For this purpose, we not only use
standard approaches but also establish groundbreaking theories in dynamicalsystems. Moreover, we utilize numerical approaches such as verifiable
computation rigorous numerics and large-scale numerical simulation, andstudy the nonintegrability of dynamical systems and differential equations,nonlinear waves in partial differential equations, periodic motions in the
n-body problem of classical mechanics, several problems in the kinetic theoryof many-body systems, design of spacecraft transfer trajectories, anddynamics and control of flying objects such as quadcopters.

4-body super-eight solution which is proven to exist
by using variational methods

Ω3
Ω4
ωz
ωy

ωx

Ω2

Ω1

[Kazuyuki Yagasaki , Mitsuru Shibayama , Yoshiyuki Yamaguchi]
Mathematical model of a quadcopter

Applied Mathematical Modeling Adjunct Unit (In collaboration with Hitachi, Ltd.)
Infusing information systems with intelligence
To make information systems useful to our day-to-day lives and industry atlarge, we need to be able to mathematically model both the behavior of people
and the movements of objects that these systems deal with. The form of thesemodels ranges from the conceptual to the numerically precise. We will
examine case studies from industry in our research of modeling technology,
including methods of using human knowledge (structural modeling) andmethods using actual data (multivariate analysis).
[Hitachi Ltd.: Yoichi Nonaka , Yoshiyasu Takahashi]

Modeling of a social infrastructure system

Graduate School of Informatics

40

Systems Science Course

New Frontier in Informatics
and Systems
Research and education in the Course of Systems Science areconcerned with a new and unified approach to a variety of technologicalproblems arising in computer communication networks, mechatronics
systems, cyber-physical systems, medical information systems, andbiological systems.
In particular, we are seeking theoretical methodologies applicable tothese complex systems of large scales.
Emphasis is also placed upon understanding of complicated mutualinteractions among human-beings, systems and environments.

41

Systems Science Course

Putting Information into Action
The Systems Science Course is focused on mathematicallygrasping common dynamic pictures of systems consisting ofmultiple objects, both for understanding such systems moredeeply and for studying methodologies for controlling thesepictures. Advances in machine learning technology andgrowing computational power have made it possible to use AI
to generate data analysis results that include someinterpretation of static data, or even to generate new data.
Nevertheless, it is still a significant challenge to develop
methodologies to understand and control dynamic systems in
the real world from data. For example, for decision-making
problems in video games or other kinds of virtual reality
systems, a machine learning technique known as reinforcement
learning has attracted attention by showing that it can learn
from experience and acquire strategies better than humans.
However, robots and other machines are still not capable offlexibly judging situations and taking responsive action likehumans do in the real world. These kinds of technologies arecommonly required in a wide range of other fields (aside fromrobots), including economics, energy, and autonomous vehicles.
Students in the Systems Science Course study how information
should be used for making behavioral choices and how thehuman brain uses the data it obtains for making decisions, andthey can learn useful methodologies for understanding anda p p l y i n g s u c h k n o w l e d g e . Th e c o u r s e o ff e r s a l e a r n i n genvironment for cultivating essential insights for pursuing thiskind research.

Jun Morimoto

Professor, Systems Science Course

March 2001: Received a doctoral degree from Nara Institute of
Science and Technology (NAIST). April 2001: Postdoctoral
Fellow, The Robotics Institute, Carnegie Mellon University. April
2008: Head of Department (concurrent position), Department ofbrain-robot interface, Computational Neuroscience
Laboratories, Advanced Telecommunications Research InstituteInternational (ATR), December 2019: Team Leader,
Man-Machine Collaboration Research Team, Guardian Robotics
Project, RIKEN. April 2021: Professor, Systems Science Course,
Graduate School of Informatics, Kyoto University.

Learning“How”to Address Unknown Issues
abstract level. Issues in the real world can be often solved withestablished methods, but very new methods occasionally ariseout of addressing the challenges posed by difficulties. In the
study of methodology of statistics, for instance, we areconsta ntly explori ng new wa ys to ma ke i n f er en ces an dpredictions from data. What is important for such a situation isagain knowledge in mathematical fields such as probability
theory and optimization.
Thus, mathematical foundations and applications interact
with each other in the Course of Systems Science. Students areinvited to learn an aspect of this scientific discipline, broaden
their perspectives, and acquire universal approaches andattitudes; that is, “how” to address unknown issues, throughresearch activities and lectures at Graduate School ofInformatics. We would be delighted if their experience here
motivates them to address unknown issues and find clues todeveloping new techniques oracademic disciplines.

Systems Science Course

In the Course of Systems Science, we are working on
scientific research across a variety of fields. For instance, we use
sensors to measure information from biological and mechanicalsystems in order to infer their internal states, project their
future behavior, and control them. Also, by elucidating how thebrain’ s neural circuits process information, we are working tocreate a system that is capable of not only learning and making
inferences but also adjusting itself to an uncertain and changing
environment. Furthermore, we are studying algorithms andtheory for making inferences and discoveries from the vast
amount of image and document data available online andelsewhere, together with the high-performance parallel
computing that makes such systems possible.
Not only are these research fields related to systems but, inmany cases, research projects conducted in these fields share acommon approach - researchers conduct their studies throughm a t h e m a t i c a l m o d e l s , c o n s t a n t l y a w a r e o f t h e fl o w o f
information. Using mathematical models, researchers can treat
different objects as if they were the same, thereby gaining broad
perspectives.
For example, by using a “graph” that consists of vertexes andedges, researchers can express not only networks (neuralnetworks, website link structures, railway systems, etc.) but also
structures of relevant data, such as tagged images on social
media. Because the objects thus modeled can be treatedmathematically, scientists can advance their research evenfurther. In the field of machine learning, significant research is
being carried out into a technique called graph embedding for
efficient information search. One drawback of this technique
was that a graph with a hierarchical structure is not expressedvery well in Euclidean space. This problem has been solved by amathematical idea of using a curved space called “hyperbolic
space.”
Another characteristic of students/researchers in the Courseof Systems Science is strongly conscious of systems in the realworld, in addition to conducting mathematical study at an

Hidetoshi Shimodaira
Systems Science Course

March 1990: Received a bachelor’s degree fromthe Department of Mathematical Engineering andInformation Physics, School of Engineering, The
University of Tokyo. March 1995: Received a doctoral degree from the Department ofMathematical Engineering and Information Physics, Graduate School of Engineering,
The University of Tokyo. April 1995: Received a Research Fellowship for Young
Scientists from the Japan Society for the Promotion of Science. July 1996: AssistantProfessor, Department of Prediction and Control, Institute of Statistical Mathematics.
June 2002: Lecturer, Department of Mathematical and Computing Sciences,
Graduate School of Information Science and Engineering, Tokyo Institute ofTechnology. May 2005: Associate Professor, Department of Mathematical andComputing Sciences, Graduate School of Information Science and Engineering, Tokyo
Institute of Technology. April 2012: Professor, Division of Mathematical Science,
Graduate School of Engineering Science, Osaka University. September 2016-present:
Team Leader (concurrent position), Mathematical Statistics Team, RIKEN Center
for Advanced Intelligence Project (AIP). April 2017-present: Professor, Department
of Systems Science, Graduate School of Informatics, Kyoto University.

Graduate School of Informatics

42

Systems Science Course

Outline■Group and Teaching Staff
Group

Teaching Staff

Mechanical Systems
Control

Shun-ichi Azuma/Professor

Human Systems

Manabu Kano/Professor Shota Kato/Assistant Professor

Integrated Dynamical
Systems

Toshiyuki Ohtsuka/Professor Kazunori Sakurama/Associate Professor
Kenta Hoshino/Assistant Professor

Mobility Research

Osamu Nishihara/Associate Professor

Mathematical Information
Systems

Toshiyuki Tanaka/Professor Tomoyuki Obuchi/Associate Professor

Statistical Intelligence

Hidetoshi Shimodaira/Professor Junya Honda/Associate Professor

Learning Machines

Jun Morimoto/Professor Satoshi Yagi/Assistant Professor

Integrated Systems Biology

Shin Ishii/Professor Hideaki Shimazaki/Associate Professor
Paavo Parmas/Program-Specific Assistant Professor
Jaepyung Hwang/Program-Specific Assistant Professor

Biomedical Engineering

Hirohiko Imai/Assistant Professor

Computational Neuroscience(Adjunct Unit)

Mitsuo Kawato /Adjunct Professor (Advanced Telecommunications Research Institute International)
Takuya Isomura/Adjunct Professor Louis Kang/Adjunct Associate Professor (RIKEN)
Kenji Doya/Adjunct Professor (Okinawa Institute of Science and Technology)■Curriculum of Systems Science Course
CurriculumofSystemsScienceCourseDoctoralProgram(Informatics)
DoctoralThesis


rd



3 

nd

2 

st
1 

Subjects provided by the Course (total 6 credits including 4 credits from seminars)
SeminaronSystemsScience,Adv.E(2credits)
SeminaronHumanMachineSymbiosis,AdvancedA,BE,SeminaronSystemsSynthesis,AdvancedA,BE� �
SeminaronSystemsInformatics,AdvancedA,BE(2creditseach)

ResearchGuidance

Master’sProgram(Informatics)



Master’sThesis
ndSeminarsandexercises
forMaster’sthesis

2  SubjectsprovidedbytheCourse(optional8creditsormore)

- 8 -







AdvancedSubjects
SystemsSciences,AdvancedII,ControlTheoryforMechanicalSystems,TheoryofHumanͲ
MachineSystems,TheoryofIntegratedDynamicalSystems,TheoryofLearningMachines,IntegratedSystemsBiology,MedicalInformationSystems,ModelingandProblemͲSolvingofComplexSystems,SystemsBiologyE(2creditseach)
ComputationalNeuroscience,ComputationalIntelligence(1crediteach)
st

1 

RecommendedSubjectsbyOther
Courses
InformationͲTheoreticSystemsTheory(DS)
StatisticalSystemsTheory(DS) (2creditseach)

BasicSubjects

SystemsSciences,Adv.I(2credits)

GeneralSubjectsprovidedbytheSchool
InterdisciplinarysubjectsofthePerspectivesinInformatics
(Mandatory2credits)
PerspectivesinInformatics1 PerspectivesinInformatics2�
PerspectivesinInformatics3E PerspectivesinInformatics4E�
PerspectivesinInformatics5E (2creditseach)

Priorto
admission

Differential and Integral
Calculus

PerspectivesinPlatformStudies(2credits),ComputationalScience,Introduction(2credits),ComputationalScience,ExerciseA(1credit),
InformationandIntellectualProperty(2credits),InnovationandInformation(2credits),InformationAnalysisandManagement(2credit),InformationAnalysis
andManagement,Exercise(1credit),SocialContributionsthroughInformatics
E(1credit),InternshipintheFieldofInformaticsE(1credit)

Linear algebra(Mandatory10credits) AdvancedStudyinSystem
Science2E
(AssignedtoM2,5credits)

AdvancedStudyinSystem
Science1E
(AssignedtoM1,5credits)

Specific
subjects
providedbytheschool

FundamentalbackgroundofthesubjectsprovidedbytheCourse,etc.
Note:�Subjects�marked�with�the�letter�“E”�will�be�provided�in�English.

43

Systems Science Course

Mechanical Systems Control
Control Theory and Applications
Control theory is the academic foundation for designing the
motion of dynamic systems. In this lab, we develop innovative
control theory and deploy the results to leading-edge scientificand industrial applications. We also challenge ourselves tocreate dynamic systems that open up new possibilities for the
future. Through their research activities, students cultivate theresearch skills needed to become a systems control leader inacademia and industry.

Control of conveyor robots

Control of a swarm robot system[Shun-ichi Azuma]

Human Systems
Aiming to develop human-centered system design methodology

Systems Science Course

The society that values humankind is called fornow. The situation is similar in the latest industrial
science and technology, thus novel system design
methodology is required from various positions
such as those who develop technology and those
who use technology. We perform basic research on
developing human-centered system design
methodology through understanding themechanism of human recognition and action. In
order to contribute our results to our society, we
also perform applied research in various industries
such as semiconductor, pharmaceutical, steel,Development of AI for automatically developing physical models from literature information
chemical, and automobile. Furthermore, throughthese studies, we conduct the education that aims at training talented people to take a broad view of things and have high aims.

[Manabu Kano , Shota Kato]

Integrated Dynamical Systems
Toward harmonious coexistence of a diversity of systems
For analysis and design of novel systems to realize symbiosis
and synergy of various objects including humans, machines,
societies, and environments, it is essential to find out universal
principles in modeling, analysis, design, and control ofdynamical systems. To this end, we conduct researches on novel
methodologies to deal with nonlinearities, uncertainties,
dynamic optimization, and distributed control, which are often
fundamental difficulties in various problems. We also apply our
methodologies to a wide range of fields, aiming practical as wellas theoretical education and research.

[Toshiyuki Ohtsuka , Kazunori Sakurama , Kenta Hoshino]

Graduate School of Informatics

44

Systems Science Course

OutlineMobility Research
Optimizations for design and operation
The research interest of this group mostly lies in mobility technologies, such as vehicle stability
controls, which have found practical applications in active safety systems for reducing car accidents,
energy efficiency improvements, and collision avoidance systems. These research topics are being
investigated with a view toward precise optimization within the constraints of their dynamical properties.
[Osamu Nishihara]

Mathematical Information Systems
The mathematics of large-scale probability models
We aim to develop a mathematical, cross-disciplinary
understanding of a diversity of problems related to
information processing under environments withuncertainty. Many problems in information processing today
can be formulated with large-scale probability modeling,
and using the regularities that arise thanks to the scale ofthese probability models is the key to achieving advanced
information processing. We are working on topics in areas
including statistical mechanics of information, which
addresses information mathematics of large-scale
probability models through analogy with statisticalmechanics, and theoretical problems related to statisticalmachine learning, deep learning, and data science.

Digital communications as data science: the key torealizing high-performance digital communications
lies in extracting the required information from “data”
in which numerous signals interfere.

[Toshiyuki Tanaka , Tomoyuki Obuchi]

Statistical Intelligence
Statistics and machine learning: Theory and applications
Statistics is playing important roles as a theoreticalframework for fast-moving fields such as big data, data
mining, and artificial intelligence. Statistics provide
methodologies for inductive inference from data withconsideration of randomness. Recent years have seen rapid
progress in machine learning, from information extraction
from a massive quantity of data to decision-making with asmall quantity of data, on the basis of this approach.
Through addressing real-world data, we are developing new
statistical methods with emphasis on mathematics andprogramming skills.
[Hidetoshi Shimodaira , Junya Honda]

45

Multiveriate analysis of multiple domains.
Dimensionality reduction via graph-embeddings andbidirectional search between images and tags.

Systems Science Course

Learning Machines
Toward the realization of learning machines with bodies
The fusion field of artificial intelligence andthat can operate in a dynamically changing open

robotics is expected to be a field that will produce

environment, we conduct education and research

the basic industrial technologies of the future. We

on basic technologies for robot motion learning

will explore methodologies to enable machines

methods, mathematical models ofwith bodies to learn skillfully and produce desired

multi-degree-of-freedom robots, and humanbehaviors from limited experience and data, justmotion intention estimation.
[Jun Morimoto , Satoshi Yagi]

like humans. In order to realize learning machines

Robot motion learning

Constructing models of information processing in life and intelligent systems
Intelligence (the brain) and life are complex

applied research, such as the application of thesesystems that adapt to uncertain and changing

principles in the building of robots that haveenvironments. Aiming at elucidating the principles

adaptive information processing mechanisms thatof information processing in those complicated

we have learned about through our studies of living

systems, we are focusing on researches in theorganisms.

areas of computational neuroscience, systems
biology, and bioinformatics, while conducting

Systems Science Course

Integrated Systems Biology

We conduct interdisciplinary education and

research on life systems.

[Shin Ishii , Hideaki Shimazaki , PARMAS Paavo, HWANG Jaepyung]!"#$%&'"(#!)'$(Left) A deep learning-based image conversion technology, GAN-SID, converts “natural” images into “non-natural” images,preserving the parts that draw a person’s attention (gaze)(Right) Brain activity when viewing “natural” and “non-natural” images differs

Graduate School of Informatics

46

Systems Science Course

OutlineBiomedical Engineering
Learning bout the functions and physical characteristics of human bodies
Information systems such as data processing
systems used in genetic analysis, diagnostic
imaging systems are key technologies of modern
medicine. Interdisciplinary collaboration isessential to further progress of medical systems’
research that combines the two keywords of “bio”
and “information.” We carry out joint research

projects with other research organizations indifferent fields including the Faculty of Medicine todevelop surgical navigation systems, medical
imaging techniques, and innovative methods tomeasure biological characteristics of humanbodies.
[Hirohiko Imai]

Medical image processing and modeling for diagnosis and treatment

Computational Neuroscience Adjunct Unit (In collaboration with
Advanced Telecommunications Research Institute International)■Knowing the brain by building the brain(a) Connecting the brain and artificial intelligence
Humans and other animals are known to becapable of learning from a small sample. On
the other hand, deep neural networks and otherforms of current artificial intelligence today
require learning samples around the same sizeas that of the parameters. Metacognition,
multilayered generative and analytical models,and synchronization of neural activity arethought to be some of the secrets the brain
holds. We aim to comprehend these and othercharacteristics of the brain and apply them inthe development of the next generation of
artificial intelligence.

(b) Brain-machine interface
Brain-machine interface technology that
directly connects the human brain withmachines is attracting attention as a type of“brain tech” that can improve the capabilities
not only of people with impaired sensory,
mobility, and central nervous functions, butalso the ablebodied. Specifically, it involves aprocess of applying decoding techniques to
non-invasively measured brain activity data,and in return providing the subject withdecoded neuro-feedback that induces activity
p a t t e r n s i n s p e c i fi c p a r t s o f t h e b r a i n i n
response to certain information. The goal is toutilize this method in the treatment of
psychiatric disorders and establishment of acausal approach to neuroscience.

[Advanced Telecommunications
Research Institute International :Mitsuo Kawato]47

Systems Science Course

Computational Neuroscience Adjunct Unit
(In collaboration with RIKEN)■Exploring the mechanism for emergence ofintelligence from neural circuits
How do the neural cells and synaptic
couplings that constitute the brain achieve thekind of superior intelligence we find in living
creatures? With a view to solving this mystery,
we apply mathematical approaches such as
dynamic theory, Bayesian statistics, machinelearning, and the free energy principle toconstruct a universal theory of the brain. We

focus particularly on topics such as internal
model learning in the cerebral cortex and themechanisms of memory and spatial awarenessin the hippocampus. We aim to apply ourfindings in the development of new artificial
intelligence algorithms, psychiatric disordermodels and the like.
[RIKEN: Takuya Isomura, Louis Kang]

Neural circuit dynamics and plasticity potentially apply Bayesian inference■Shedding light on the computational theories
of action learning and the learning systemsof the brain
Humans and other animals can acquire new
behaviors in line with various environments.
What workings of the brain enable suchacquisition? Answering this question requires acomplementary approach to developing
computational theories and algorithms onaction learning in diverse environments andexplicating the mechanisms of neural cells andmolecular networks in the brain. Our lab brings
researchers from a variety of different fields
and countries together on a campus adjacentto the ocean in Okinawa to pursue research on
topics including development of new
algorithms for reinforcement learning andBayesian inference, using those algorithms inaction learning by robots and applications tobioinformatics, experiments in measurement
and control of activity in the cerebral cortex,
basal ganglia, and serotonin neurons of rats
and mice, explication of human action learningand brain activity, and evolution of group
behavior objectives and learning processes inrobots.
[Okinawa Institute of Science and Technology:
Kenji Doya]

Systems Science Course

Computational Neuroscience Adjunct Unit
(In collaboration with Okinawa Institute of Science and Technology)

The neural circuit of the basal ganglia and its functions inreinforcement learning

Graduate School of Informatics

48

Communications and Computer Engineering Course
49

Towards the Establishment ofFundamental Technologies in
the Information Age
Advanced information processing and communications are indispensablefor our society to prosper in the 21st century.
Information processing devices typified by computers are called upon toachieve high performance and be highly functional and compact.
In communications, we should be able to enjoy high-speed, reliabletransmission of a vast amount of multimedia data anytime, anywhere.
The Course of Communications and Computer Engineering supports thedevelopment of future technologies in the fields of information processing
devices and digital communications.

Communications and Computer Engineering Course

Constructing Information and Communication
Infrastructure for an Innovative Society

Communications and Computer Engineering Course

Dramatic changes in socioeconomic conditions and globalization have given rise to a mountain of
new and increasingly complex social challenges involving multiple different domains and fields. To
address these challenges, information and communication “platforms” that take advantage ofreal-time data from the real (physical) world, as well as massive quantities of accumulated data fromthe virtual (cyber) space of servers and clouds are being used.
This information and communication infrastructure will generate new value and resolve social
issues by using various sensing technologies to collect different kinds of information that are
ubiquitous in the physical world, storing this information in cyberspace over a wide area using
communication technologies, and then processing it using information technologies for data
organization, analysis, feature extraction, and prediction, and finally, feeding the results ofprocessing back into the physical world using communication technologies to share the results for avariety of purposes.
This kind of infrastructure requires various kinds of devices built from integrated circuits, taking
into consideration size and power consumption to enable high-speed data collection and processing,computers (hardware and software) for high-speed, high-efficiency processing of data stored in
virtual spaces, and a communication system that can reliably connect an extremely large number ofdevices across cyberspace at high speed. The R&D on these devices, computers, and communication
systems needs to be integrated, rather than conducted separately.
The Communication and Computer Engineering Course provides education and research on
computers, communications, and integrated systems, the fundamental technologies for building thisplatform, all within a single course. Research findings in this field are at the forefront of shaping theworld, as they are implemented in Japan and internationally through joint research with industry,
international standardization, and other means, to support a wide range of social and economic
systems. Come to study with us in the Communications and Computer Engineering Course andhelp to generate new value for the benefit of society, through innovations in essential informationand communication technologies and systems.

Hiroshi Harada

Professor, Communications and Computer Engineering Course
He received his Ph.D. degree from the Graduate School of Engineering, Osaka University in 1995. He
joined the Communications Research Laboratory, Ministry of Posts and Communications, (currently
National Institute of Information and Communications Technology: NICT). After working as a
researcher at Delft University of Technology, the Netherlands, a research manager and research
director at NICT, and laboratory director at the Singapore Laboratory of NICT, he became a professor
at Graduate School of Informatics, Kyoto University in 2014. Since 1995, he has been involved in the
research, development, international standardization, and commercialization of mobile wirelesscommunication systems and wireless communication systems for the Internet of Things.
He received the Commendation for Science and Technology from the Minister of Education, Culture,
Sports, Science and Technology in 2014, the Achievement Awards from IEICE in 2006 and 2018, and theWorking Group Chair Awards from IEEE Standard Association for the contributions of five international
standards on wireless communication

Graduate School of Informatics

50

Communications and Computer Engineering Course

Outline■Group and Teaching Staff
Group

Teaching Staff

Computer Algorithms

Shin-ichi Minato/Professor Jun Kawahara/Associate Professor
Jesper Jansson/Associate Professor Yuni Iwamasa/Assistant Professor

Computer Architecture

Naofumi Takagi/Professor Ryota Yasudo/Assistant Professor

Computer Software

Atsushi Igarashi/Professor Kohei Suenaga/Associate Professor
Masaki Waga/Assistant Professor Mirai Ikebuchi/Assistant Professor

Digital Communications

Hiroshi Harada/Professor Keiichi Mizutani/Associate Professor
Yusuke Koda/Assistant Professor

Integrated-Media
Communications
Intelligent
Communication Networks
Processor Architectureand Systems Synthesis

Eiji Oki/Professor Takehiro Sato/Associate Professor Ryuta Shiraki/Assistant Professor
Takashi Sato/Professor Hiromitsu Awano/Associate Professor

Integrated Circuits
Design Engineering

Kiichi Niitsu/Professor

Advanced
Signal Processing

Masatoshi Hashimoto/Professor Ryo Shirai/Assistant Professor

Remote Sensing
Engineering

Mamoru Yamamoto/Professor Tatsuhiro Yokoyama/Associate Professor

Atmospheric Observations

Hiroyuki Hashiguchi/Professor Koji Nishimura/Associate Professor

Supercomputing

Keiichiro Fukazawa/Associate Professor

Multimedia andSecure Networking

Yasuo Okabe/Professor Daisuke Kotani/Assistant Professor■Curriculum of Communications
and Computer
CourseCurriculum of Communications
andEngineering
Computer Engineering
CourseDoctoral Program (Informatics)
Doctoral Thesis

rd

3

nd

2

st

1

Subjects provided by the Course (total 6 credits including 4 credits from seminars)

Seminar on Communications and Computer Engineering, Adv. A, B E (1 credit each)Seminar on Computer Engineering, Adv. A, B E, Seminar on Communication Systems Engineering, Adv. A, B E, Seminar on Integrated Systems
Engineering, Adv. A, B E, Seminar on Radio Atmospheric Science, Adv. A, B E, Seminar on Communication and Computer System, Adv. A, B E(2 credits each)Research Guidance

Master’s Program (Informatics)
Master’s Thesisnd2

Subjects provided by the Course (optional 12 credits or more, including recommended subjects provided by otherCourses)

Seminars and exercisesfor Master’s thesis

Advanced Subjects(Mandatory 10 credits)- 9 -Design in ICT, Theory of Computational Complexity E, Parallel Computer Architecture, Hardware
Algorithm, System Verification�E, Formal Semantics of Computer Programs, Transmission MediaEngineering, Adv., Integrated System Architecture and Synthesis, SystemͲLevel Design Methodology
for SoCs E, Atmospheric Measurement Techniques E, Remote Sensing Engineering, Computer
Network, Advanced, Supercomputing, Advanced (2 credits each)st

1

Basic Subjects

Language Information Processing, Adv. E (IST),
Biosphere Informatics E (SI)Cryptography and Information Society (SI)
(2 credits each)

Interdisciplinary subjects of the Perspectives in Informatics(Mandatory 2 credits or more, up to 4 credits)
Perspectives in Informatics 1 Perspectives in Informatics 2�
Perspectives in Informatics 3E Perspectives in Informatics 4E�
Perspectives in Informatics 5E(2 credits each)

Prior toadmissionCommunications andRadio Engineering

Logic and Integrated
Circuits Engineering

Perspectives in Platform Studies (2 credits), Computational Science,
Introduction (2 credits), Computational Science, Exercise A (1 credit),
Information and Intellectual Property (2 credits), Innovation and Information(2 credits), Information Analysis and Management (2 credit), Information
Analysis and Management, Exercise (1 credit), Social Contributions throughInformatics E (1 credit), Internship in the Field of Informatics E (1 credit)

Computer Engineering- 9 -Advanced Study inCommunications andComputer Engineering 2E(Assigned to M2, 5 credits)
Advanced Study inCommunications andComputer Engineering 1E(Assigned to M1, 5 credits

Theory of Discrete Algorithms, Introduction to Algorithms and Informatics, Digital Communications
Engineering, Information Networks, Integrated Circuits Engineering, Adv. (2 credits each)

General Subjects provided by the school

51

Recommended subjects provided byother Course (4 credits, Mandatory)

Theoretical Computer
ScienceSpecific Subjects
provided by theschoolRequired to earn more than 2 credits
from the four subjects on the left

Note:�Subjects�marked�with�the�letter�“E”�will�be�provided�in�English.

Communications and Computer Engineering Course

Computer Algorithms
The theory and art of algorithms, with real-life applications
A computer system consists of hardware and software. Both parts work
according to a logical procedure: “algorithm.” The art of algorithms andcomplexity theory are core areas in computer science, and needless to say have amultitude of applications. We investigate fundamental theory, state-of-the-art
techniques, and real-life applications of “algorithms,” a keyword of ourlaboratory. We aim to enable computers to make increasingly significant
contributions to society.
[Shin-ichi Minato , Jun Kawahara , Jesper Jansson , Yuni Iwamasa]

Computer Archtecture
Advanced computing mechanisms and design technologies
We conduct education and research on new computing
mechanisms and their design technologies for the post-Moore
era in which exponential improvement in computer
performance is difficult to achieve. Our main research topics
Communications and Computer Engineering Course

include computing methods utilizing FPGA which is areconfigurable hardware and GPU which enables high-speed
parallel computing, high-capacity memory systems based on
packet transfer, systems for uniform handling of combinational
optimization, and design of superconductive digital circuits anddevelopment of their design support technologies.
[Naofumi Takagi , Ryota Yasudo]

Computer Software
Theory and practice for efficient and dependable software
Centering around programming languages, we conduct research andeducation on theory and practice for building highly efficient anddependable software. Our main focuses are on theory of program
verification techniques based on mathematical logic, such as type�������� ��������������������������
������������ ���� ���������������

�����������
������������� ������� �������� ��������� ��������� ����theory and model checking, and the design and implementation ofhigh-level programming languages, backed by rigorous foundations.
[Atsushi Igarashi , Kohei Suenaga , Masaki Waga , Mirai Ikebuchi]������������������������
��������������������������
������������������������������
��������������������� �������

Graduate School of Informatics

52

Communications and Computer Engineering Course

OutlineDigital Communications
Toward ubiquitous wireless information networks
Wireless communication networks, accelerated by cellularradio together with short-range wireless communications andRFID tag technologies, for instance, have been advancing
significantly towards the goal of so-called ubiquitous networks.
That is, we are on the verge of an era when people can enjoy
various benefits unconsciously from totally connected network
where various equipments, devices, and sensors are closely
connected each other and linked to the Internet via wirelesstechnologies. With wireless distributed self-organizing
information networks which will be expected to play core roles in such a next generation information
networks in mind, we are actively working to conduct education and research on highly efficient radio
resource management techniques including spectrum sharing among multiple wireless systems, andhighly spectrum- efficient signal processing techniques for broadband wireless transmission, etc.
[Hiroshi Harada , Keiichi Mizutani , Yusuke Koda]

Intelligent Communication Networks
Exploring information and communication network paradigms
The advancement of the Internet of Things (IoT)

for the transmission and reception of large

and big data technologies has enabled thevolumes of traffic and computer resources to

networking of all types of devices around us andcollect and analyze data. Our laboratory conducts

the provision of a diverse variety of services

research on high-speed, reliable, and flexible

through data processing on cloud and edge

information communication networks using aplatforms. Establishing these types of systems as

broad range of theoretical and practicalsocial infrastructure will require technologies forapproaches.

the sophisticated design and control of networks[Eiji Oki , Takehiro Sato , Ryuta Shiraki]

Logical networks
High performanceHigh capacity

High reliability

Optical network
Cloud
Edge

53

Communications and Computer Engineering Course

Processor Architecture and Systems synthesis
Architecture design methodology for system LSIs
Architecture design of integrated circuits is a key enabler for
exploiting full potential of advance semiconductor technologies.
Real-time signal processing on media data, extremely low
power operation to prolong battery lifetime, and maximizing
reliability of the system are of utmost importance. We conduct
researches on the following areas: (1) methodologies for circuit
analysis, circuit design techniques, and circuit-performance
optimization, (2) architectural design for processors andreconfigurable devices for system LSI, and (3) hardware andembedded software algorithms for codecs, digitalc o mm un ic a t ion s, im a ge r ec o gn it io n , a n d th e i r d e si gn
methodologies.
[Takashi Sato , Hiromitsu Awano]Integrated Circuits Design Engineering
Circuit and Design Technologies for Large-Scale, High-Performance CMOS LSIs

[Kiichi Niitu]

Power-independent continuous blood glucose monitor contact
equipped with 65 nm CMOS IC and glucose power element

Ultrafast Signal Processing

Communications and Computer Engineering Course

Large-scale integrated (LSI) circuit systems are now essential
elements of the public infrastructure that supports our modern
information society. Our focus is doing fundamental R&D on
design technology for energy-efficient semiconductor LSI
circuits and developing new applications for them. Our approachis to envision a finished product, as well as the services in which
it will be utilized, and even the social impact of those services,and then design and develop the high-energy efficiency LSI
circuits to enable that product. Our R&D work ranges widely,
from drawing up public acceptance scenarios to formulatingnecessary LSI circuit specifications, trial manufacturing of ICs,and creating prototypes in a consistent manner.

Design and application of integrated systems
Our society is increasingly dependent on information system infrastructures,such as AI and Internet of Things (IoT). As they deal with human lives andproperty, information systems are required to offer high reliability. Ultra-low
power, ultra-small volume semiconductor devices have been made possible byminiaturized transistors, and are realizing ambient computing that blends inwith the environment. On the other hand, as the miniaturization of transistors
seems to have reached its limit, efforts are underway to seek computing basedon new principles. Under the banner of "creating computing infrastructure," thisgroup is seeking ways to design reliable, high-performance computers, realize
next-generation computing based on new principles, and define a computing
system that changes our lifestyles.
[Masanori Hashimoto , Ryo Shirai]

Experiment to evaluate the reliability of an
integrated system against cosmic rays

Graduate School of Informatics

54

Communications and Computer Engineering Course

Outline概 要

Remote Sensing Engineering
Exploration of the Earth’s atmosphere through radars
We aim to elucidate various phenomena
observed in the Earth's atmosphere, through thedevelopment of radio remote sensing andcomputer modeling. We are developing radar
technology and/or simulation codes for studying
various atmospheric phenomena (e.g.,turbulence, rain, clouds, plasma) over a wide
altitude range -- from phenomena that occur
near the surface and hence are directly related tohuman activities, to phenomena that occur at theionosphere above 100 km altitude, which is theboundary between the atmosphere and space. We
study phenomena over Japan by the MU radar,and focus on the atmospheric and space weather
phenomena. In addition to an atmospheric radar
located in Indonesia (Equatorial Atmosphere
Radar; EAR), we deploy an observation network in
Southeast Asia under international collaborationin order to understand the atmospheric/ionospheric phenomena near the equator, where

deep cumulus convection is moreprevalent
there than anywhere else.
[Mamoru Yamamoto , Tatsuhiro Yokoyama]

Equatorial Atmosphere Radar in West Sumatra, Indonesia.
Its size is about the same as that of the MU radar.

Atmospheric Observations
Towards developing new observation techniques toobtain atmospheric environmental information
We are developing new techniques to observe
the atmosphere using radio waves, light, andacoustic waves, and conduct research andeducation to collect, process, and disseminate
global observational atmospheric data. Morespecifically, our research topics include profiling of
atmospheric temperature and humidity by using
radio-acoustic sounding and laser radar
techniques, development of radar digital receivers
using software-defined radio for radar imaging
observations of atmospheric turbulence, anddevelopment of adaptive clutter suppression
techniques using the MU radar. We also carry out
atmospheric observations around the world andcombine a variety of techniques such as satellite
data analysis and numerical modeling in order toelucidate various phenomena of the Earth’s
atmosphere, which is a protective coat of thehumanosphere.
[Hiroyuki Hashiguchi , Koji Nishimura]55

MU radar in Shigaraki, Koka City , Shiga Prefecture. The
diameter of the antenna is 103 m.

Communications and Computer Engineering Course

Supercomputing
Aiming to be at the forefront of computing performance
We are involved in research into supercomputers, theirsoftware, and systems that are thousands or tens of
thousands of times more powerful than ordinary personal
computers.
Our focus is on research related to fundamental
technologies to support future high-performance parallel
computing, such as high-performance and highly parallel
program development techniques suitable forstate-of-the-art processors and computers, research on
program scheduling to maximize the computing
performance per unit of power consumption, anddevelopment of applications that can take advantage ofsupercomputers. Much of this research is in the form ofjoint research projects that extend beyond the field ofcomputer science to involve researchers in the fields of
medicine, physics, engineering, and other areas.
[Keiichiro Fukazawa]

Global magnetospheresimulation using massively
parallel computing

Supercomputer at the Academic Center for Computing and Media Studies

Multimedia and Secure Networking
Towards a ubiquitous networking world

Investigating cyberattack trends by observing darknets(unused IP address spaces)

protocols that use such networks, operational
technologies such as automated configuration, platform
technologies that support assorted services such as
identity federation, and security. As the network researchdivision of the Academic Center for Computing and Media
Studies, we also perform numerous empirical studies
using operational networks, both on and off campus.
[Yasuo Okabe , Daisuke Kotani]

Communications and Computer Engineering Course

Society 5.0 is a vision of a future society in which
computers and network functions are embedded into
everything, characterized by a high degree of integration
between cyberspace and physical space through“anytime, anywhere” network connections. As
fundamental technologies to support this kind of society,
we are pursuing research on next-generation Internet
technologies such as programmable networks andLinking contextual information suitable for identity
federation in a Zero Trust system

Network access control using workload identities for cloud operations

Graduate School of Informatics

56

Data Science Course

Data Science and Beyond
Data science has become very important in recent years as a methodology
for inductively constructing models and hypotheses from data.
This trend has also spilled over into other academic disciplines, such as
medicine, education, disaster prevention, agriculture, economics, andlinguistics, leading to a surge in demand for data scientists in industries
spanning agriculture and manufacturing, distribution and retailing, andfinance and entertainment. The new Data Science Course, established in
April 2023 as a center for education and research in mathematics, data
science, and machine learning, also serves to foster top-class data science
experts.
By actively welcoming students and researchers from adjacent disciplines,
we also contribute to the creation of new interdisciplinary academic fields.

57

Data Science Course

Data Science is Drawing a Lot of Attention
Thanks to advances in information and communication technologies, a wide
variety of data are now being collected and stored on an unprecedented scale. A lotof efforts are also being made in trying to inductively draw out knowledge fromsuch data. Various new developments are also emerging in statistical and machinelearning theories, which serve as frameworks to support the process of inductive
inference from data. Such advances are making it possible to systematically deal
with types of data that were previously difficult to handle, as well as to perform
new types of data analysis. Then there is deep learning. Starting with images andtext, varieties of data that are being accumulated in massive quantities, models
with superior capabilities that can be mistaken as the artificial “intelligence” havenow been constructed for different tasks, by appropriately training multilayer
neural networks. These striking achievements in deep learning have in turn beenraising new questions about frameworks for making inductive inferences from
data. Why does deep learning work so well? According to optimization theory, it
should be difficult to optimize the kind of non-convex objective functions thattypically appear in deep learning. Statistical learning theory also states thatcomplex models, such as multilayer neural networks, cannot be expected to have agood generalization ability. In the light of these findings, it is hard to explain why
deep learning works so effectively. In the search for an answer to this conundrum,
new systems and methodologies of deep learning, and by extension, data science,
are likely to emerge.
We invite you to dive with us into this exciting world of data science.
Data Science Course

Toshiyuki Tanaka

Data Science Course

March 1993: Received a doctoral degree from the University of Tokyo. April
1993: Assistant, Department of Electronics Engineering, Faculty of Technology,
Tokyo Metropolitan University. April 2000: Lecturer, Department of ElectricalEngineering, Graduate School of Engineering, Tokyo Metropolitan University.
January 2002: Associate Professor, Department of Electrical Engineering,
Graduate School of Engineering, Tokyo Metropolitan University. April 2005:
Associate Professor, Department of System Design, Faculty of System Design,
Tokyo Metropolitan University. October 2005: Professor, Department of
Systems Science, Graduate School of Informatics, Kyoto University.

Graduate School of Informatics

58

Data Science Course

Outline
概 要■Group and Teaching Staff
Group

Teaching Staff

Statistical Mathematics

Hidetoshi Shimodaira/Professor Junya Honda/Associate Professor

Statistical Inference

Hisayuki Hara/Professor (Secondary appointment : Institute of Liberal Arts and Sciences)

InformationTheoretic Learning

Toshiyuki Tanaka/Professor Tomoyuki Obuchi/Associate Professor

Signal and Information
Processing

Kazunori Hayashi/Professor (Secondary appointment : Institute of Liberal Arts and Sciences)

Applied Machine Learning(Computational Intelligence)

Akihiro Yamamoto/Professor Ichigaku Takigawa/Program-Specific Professor
Brendan John Flanagan /Program-Specific Associate Professor(Institute of Liberal Arts and Sciences)

Applied Machine Learning(Collective Intelligence)

Hisashi Kashima/Professor Koh Takeuchi/Assistant Professor

Medical and Healthcare
Data Science

Hiroshi Tamura/Professor (Secondary appointment : Institute of Liberal Arts and Sciences)

Computational Intelligence
Systems (Adjunct unit)

Toshiyuki Tanaka/Professor Nonori Ueda/Adjunct Professor
Tomoharu Iwata/Adjunct Teaching Staff (NTT Communication Science Laboratories)■Curriculum of Data Science Course
CurriculumofDataScienceCourseDoctoralProgram(Informatics)
DoctoralThesis

rd

3 

nd
2 

st
1 

Subjects provided by the Course (total 6 credits)
SeminaronDataScienceAdv.E 
SeminaronStatisticalModeling,AdvancedA,BE� � SeminaronMachineLearning,AdvancedA,BE�
SeminaronAppliedDataScience,AdvancedA,BE� �(2creditseach)

Master’sProgram(Informatics)



Master’sThesis
nd

2 


Seminarsandexercises
forMaster’sthesis

SubjectsprovidedbytheCourse(includingrecommendedcoursesprovidedbyotherCourses)

AdvancedSubjects

- 10 -



SeminarinDataScience(Master’sprogram,Mandatory2credits)
StatisticalSignalProcessing,ComputationalLearningTheoryE,StatisticalLearningTheoryE,
InformationͲtheoreticSystemsTheory,StatisticalSystemsTheory(2creditseach)



RecommendedSubjects
providedbyOtherCourses
ComputationalIntelligence(SS)

Other28subjects

BasicSubjectsst1 InterdisciplinarysubjectsofthePerspectivesinInformatics
(Mandatory2creditsormore,upto4credits)
PerspectivesinInformatics1 PerspectivesinInformatics2�
PerspectivesinInformatics3E PerspectivesinInformatics4E�
PerspectivesinInformatics5E (2creditseach)

Priorto
admission

Differential and Integral
Calculus

Linear algebra(Mandatory10credits)

AdvancedStudyinData
Science2E
(AssignedtoM2,5credits)
AdvancedStudyinData
Science1E
(AssignedtoM1,5credits)

FoundationofStatisticalScience(2credits)
DigitalTransformation,SecondaryUsageofData(1crediteach)

GeneralSubjectsprovidedbytheschoolPerspectivesinPlatformStudies(2credits),ComputationalScience,Introduction(2credits),ComputationalScience,ExerciseA(1credit),
InformationandIntellectualProperty(2credits),InnovationandInformation
(2credits),InformationAnalysisandManagement(2credit),Information
AnalysisandManagement,Exercise(1credit),SocialContributionsthrough
InformaticsE(1credit),InternshipintheFieldofInformaticsE(1credit)

Probability and statistics- 10 -

59

ResearchGuidance

Specificsubjects
providedbythe
schoolFundamentalbackgroundofthesubjectsprovidedbythe
Course,etc.
Note:�Subjects�marked�with�the�letter�“E”�will�be�provided�in�English.

Data Science Course

Statistical Mathematics
Theory and Application of Statistics and Machine Learning
Statistics plays a vital role as the theoreticalfoundation of the highly influential trends of big
data, data mining, and AI. A key feature of statistics
is that it provides a methodology for making
inductive inferences from data, in a way that
accounts for randomness. On this foundation,
machine learning has developed very rapidly inrecent years, from extracting information fromlarge volumes of data to making decisions utilizing
only small amounts of data. Our aim at this turning
point is to develop new methodologies driven bymathematics and programming to use withreal-world data.
[Hidetoshi Shimodaira , Junya Honda]

Graph embedding using neural nets to
reduce the dimensionality of data andperform image-text mutual search.

Statistical Inference
Mathematics of Statistical Inference in the Age of Big Data and Its Applications
using even small sample sizes. Our research andeducation are focused on the development ofhigh-dimensional inference methods and theirapplication to fields as diverse as archaeology,
anthropology, and cognitive science, by making use
not only of classical inference methods but also
various mathematical techniques such as
combinatorics, algebraic geometry, andoptimization methods.
[Hisayuki Hara]

Data Science Course

With the rise of big data, machine learning, andAI, statistical inference theory and algorithms forstatistical inference in high-dimensional complex
systems are increasingly important. Althoughmassive quantities of data are now readily
accessible, the complexity of the phenomena wewant to understand clearly remains daunting. It istherefore necessary to develop robust inference
methods that allow us to make reliable inferences

Many statistical models can be interpreted as algebraic varieties.
Algebraic quantities can offer valuable insights for statistical inference.

Graduate School of Informatics

60

Data Science Course

OutlineInformation-Theoretic Learning
Information Mathematics for Machine Learning
Over recent years, deep learning has advanced

understood. Our aim is to shed light on theenormously, with a rich variety of ideas and models

mechanisms of these methods via utilizing

generated and proposed. In addition to deep

knowledge from information theory and thelearning, other methods of machine learning havestatistical mechanics of disordered systems.

also been developed for dealing with various[Toshiyuki Tanaka, Tomoyuki Obuchi]

challenges confronted in data science. In somecases, the reasons why such methods are effectiveor the properties of such methods are not even

Signal and Information Processing
Stochastic and Statistical Approaches for Understanding Mathematical Systems
Our focus in this field of signal information processing
is on the construction and the analysis of mathematicalmodels for stochastic and statistical problems thatemerge in various kinds of systems, and on thedevelopment of effective algorithms for practicalapplication of such models. More specifically, we aremaking use of statistical signal processing, an approachto systematizing methodologies for extracting useful
information from observed raw signals and data, as a tool
for tackling problems mainly in communications systems,s u c h a s m o b i l e c o m m u n i c a t i o n s , o p t i c a l fi b e r
communications, and IoT.
[Kazunori Hayashi]

61

Example of super-resolution image by compressedsensing for X-ray fluorescence measurement

Data Science Course

Applied Machine Learning (Computational Intelligence)
Machine Learning and Data Science for Solving Real-world Problems
In our work on applied machine learning

making concrete implementation through scientific(computational intelligence), we conduct practical

discovery and understanding; through integration withresearch on machine learning and data science with ageneral discrete structure processing using symbolic

view to developing solutions to real-world problems

logic, knowledge representation, and algorithm design;and challenges in the fields of natural sciences andand by comparison between human and machine

education. In life sciences and chemistry, discretelearning.

combinatorial structures, such as genome sequences,[Akihiro Yamamoto, Ichigaku Takigawa,
Brendan John Flanagan]molecular structures, andintermolecular interactions,n e e d t o b e r e fl e c t e d i nmachine learning. We aredeveloping technology toaddress these need. In
education, we are working on
the development of an
educational information
platform and methodology to
improve the effectiveness andefficiency of our own learning.
Our goal is to pave the way fora new foundation forcomputational intelligence, by
Data Science Course

Applied Machine Learning (Collective Intelligence)
Pioneering Innovative Applications of Data Analytics
We focus on research and development
in statistical machine learning and data
mining techniques, and design data-driven
solutions that address critical challenges
in various domains, such as healthcare,biotechnology, pharmaceuticals,
education, transportation, and marketing.
By collaborating with industry leaders,
governmental agencies, and otherorganizations, our goal is to break new
ground in the applications of data analytics
and generate impactful solutions forreal-world problems.
[Hisashi Kashima , Koh Takeuchi]

Leveraging advanced analytics to address challenges across various domains.

Graduate School of Informatics

62

Data Science Course

OutlineMedical and Healthcare Data Science

The medical research outlined below is conducted mainly in Kyoto University Hospital.

Research Themes■ Epidemiological research utilizing clinical data including imaging and genetic data
Clinical research consisting mainly of observational studies, in combination with multimodal
imaging and genetic data, elucidating pathophysiology or predicting prognosis.

■ Epidemiological research utilizing claims data, including the National Database of
Health Insurance Claims and Specific Health Checkups of Japan (NDB)
Epidemiological studies based on the secondary use of claims data, which plays a central role in
the Data Health Plan featured in the Japan Revitalization Strategy■ Research on implementation and improvement of hospital information systems for
improving hospital management
Research aimed at improving hospital management by organizing and verifying implementationof issues in the renovation, improvement, and secondary use of hospital information systems
involving new technologies such as IoT and AI■ Research on the development of a gaze
analyzing visual function evaluation device
Study on using a “gaze analyzing visual function
evaluation device” featuring eye tracking and AI for
analysis, to assess multiple visual functions, such as
visual acuity and eye movements, with a focus on thev i s u a l fi e l d . A f u r t h e r g o a l i s t o d e v e l o p m o r eadvanced eye tracking technologies suitable forscreening of cognitive impairment.

63[Hiroshi Tamura]Data Science Course

Computational Intelligence Systems (Adjunct Unit)

Toward Knowledge Creation from Diverse Data
With advances in deep learning, machine learning methods have achieved a high level of performancein image and language processing. However, when a large quantity of good training data is not available,
low performance becomes a problem. To enable the effective use of machine learning in a broader rangeof fields, we are working on machine learning methods that can achieve high performance even under
undesirable conditions, e.g., when there are only a small amount of data available or when high-quality
data cannot be obtained. More specifically, we are working on meta-learning, i.e., the process of learning
how to learn, and on machine learning that takes advantage of inductive bias.
[NTT Communication Science Laboratories: Naonori Ueda, Tomoharu Iwata]

Data Science Course

Graduate School of Informatics

64

Kyoto University School of Platforms (KUSP)The Kyoto University School of Platforms (KUSP) is a five-year doctoral program designed to cultivate students capable ofcreating a "platform that activates society." By making full use of interdisciplinary expertise that encompasses informationand telecommunications network technology, big data analysis/utilization technology, and the humanities, the programaims to reduce social risks involved in various fields such as agriculture, medical science, and disaster management. KUSP
was selected as a FY2020 WISE Program (Doctoral Program for World-leading Innovative & Smart Education) by the
Ministry of Education, Culture, Sports, Science and Technology (MEXT).

)HDWXUHVRI3ODWIRUP6WXGLHV$VSHFWVRI ,QIRUPDWLFV,QWHOOLJHQFHVFLHQFHDQGWHFK6RFLDO,QIRUPDWLFV3K\VLFV0DWKHPDWLFDO6FLHQFHV
6\VWHP6FLHQFHV&RPPXQLFDWLRQ&RPSXWHUHQJLQHHULQJ

6RFLDOLVVXHV
6RFLDOLVVXHV$JULFXOWXUH0HGLFDO
FDUH'LVDVWHU
SUHYHQWLRQ

3UREOHPVLQPRGHUQVRFLHW\‫ق‬$JLQJ%LUWKUDWH,QWHUQDWLRQDOL]DWLRQ‫ك‬,QWHUGLVFLSOLQDU\ FURVV 
FXOWXUHIXVLRQ$VSHFWVRXWVLGH,QIRUPDWLFV&ROODERUDWLRQ DPRQJVFKRROVGHSDUWPHQWV
LQVWLWXWHLQVLGH WKHXQLYHUVLW\$JULFXOWXUH0HGLFDOFDUH
'LVDVWHUSUHYHQWLRQ/DZ(WKLFV3XEOLFSROLF\&LUFXODWLRQ'LVWULEXWLRQ/DZ(WKLFV&LUFXODWLRQ
7KHRU\IRUDFWLYDWLQJ
VRFLHW\*OREDOZDUPLQJ
&2 HPLVVLRQV‫ك‬

7KHRU\IRUDFWLYDWLQJ
V\VWHPV

7HUULWRULDOUHJXODWLRQV

6L[&RPSHWHQFLHV&RUHH[FHOOHQFHPDMRU,QWHUQDWLRQDO&ROODERUDWLRQ
3DUWQHUV,QIRUPDWLFV(QYLURQPHQWDOSUREOHPV

7KHDELOLW\WREXLOG3ODWIRUPV'DWDWKHRU\,QWHUQDWLRQDO&ROODERUDWLRQ
3DUWQHUVQRQ,QIRUPDWLFV

6HPDQWLFV,QWHUSUHWDWLRQ
5HSUHVHQWDWLRQ

Platform
Studies,QIRUPDWLRQIORZWKHRU\'HHSHQLQJPDMRU,QWHJUDWLQJDUWV
DQGVFLHQFHV&RQVWUXFWLQJVNLOO‫ق‬/DQG6HD6N\&RPPXQLW\IRUPDWLRQWKHRU\

3UREOHPVSHFLILF
FRQVWUDLQWV$UFKLWHFWXUH
FRQVWUXFWLRQWKHRU\‫ق‬5HOLDELOLW\7LPH
6SDFH6HFXULW\‫ك‬$QLQWHUGLVFLSOLQDU\SURJUDPLQZKLFKWKHSURPLQHQWSURIHVVRUVDQGFROODERUDWLRQSDUWQHUVLQFKDUJHSURYLGHFRPSOHWHHGXFDWLRQDQGUHVHDUFKFRYHULQJKXPDQLWLHVDQGVFLHQFHVWRGHYHORSn3ODWIRUPV|IRUGULYLQJRXUVRFLHW\

Participating Organizations<<Kyoto University>><<Graduate School of Informatics>> Department of Informatics<<Graduate School of Agriculture>> Agronomy and Horticultural Science, Forest
and Biomaterials Science, Applied Biosciences, Environmental Science andTechnology, Natural Resource Economics
Collaborating Organizations

Kyoto University: Graduate School of Informatics / Graduate School of Agriculture/ Graduate School of Medicine / School of Government / Disaster Prevention
Research InstituteDomestic partner institutions: National Institute of Information andCommunications Technology / RIKEN / Japan Agency for Marine-Earth Science andTechnology / National Agriculture and Food Research Organization /The Institute of
Statistical Mathematics / Meteorological Engineering Center / Agriculture andRural Development Information Center / The Research Institute for Humanity and●Platform Studies for Activating Society

65'ULYLQJ)RUFH

6XVWDLQLQJSRZHU

Nature / Institute for Health Economics and Policy /Advanced Telecommunications Research Institute International / KDDI
Research,Inc / NTT Communication Science Laboratories / Nippon Telegraph andTelephone West Corporation / Yahoo! JAPAN Research / Mitsubishi Electric
Corporation Information Technology R&D Center /Sony Group R&D Center / NEC System Platform Laboratories /Toyota Motor Corporation / Anritsu Corporation / Kadokawa ASCII ResearchLaboratories, Inc. /Ruby Association / Mitsubishi UFJ Research & Consulting Co.,
Ltd. / National Fisheries University / Jichi Medical University
Overseas partner institutions: University of Chicago / University of Illinois /University of Florida / Vienna University of Technology / University of Potsdam /Technical University of Berlin / Technical University of Munich / Delft University ofTechnology / Sorbonne University / Aalborg University / Huazhong Agricultural
University / National Chung Hsing University / National Taiwan University / The
French National Center for Scientific Research / Institute for Infocomm Research,
Agency for Science, Technology and Research (A*STAR)

technologies. Meanwhile, deep learning and machine learning have

Currently, efforts are fast underway to develop and utilize "platforms"

become standard techniques and are often used in black boxes. Yet, it isthat use telecommunications technology to collect information that is

possible to improve the output while reducing costs by correctly

non-uniformly spread throughout our society. This information isunderstanding and interpreting the data's meaning in each specific field

digitally stored in the form of big data, which is then categorized,

and optimizing it. To achieve this, informatics must be combined withanalyzed, and shared so that any outcome of the process may be fed backknowledge of different disciplines such as agriculture, medical science,

to society. The present platforms need a considerable amount of electric

disaster management, and other scientific studies.

power and computational capability to collect and process big data.

The markets for cloud computing and communication networks are

However, we can reduce the power consumption and costs if we consider

swiftly forming, but the reality is that Japan is failing to make its

the processes involved in decentralization, safety, and speed in the data

presence felt sufficiently in the process. One of the reasons for this is

generation and collection process. This requires exceptional informatics

that only engineers are involved in platform development, which results

knowledge, which has as its focal point information and communication

in a lack of international perspectives in the standardization process andbusiness undertakings. For Japan to increase its presence in thislectures and seminars on such fields as informatics, agriculture,

all-important field, in addition to knowledge on informatics andmedical science, and disaster management, which are expected to help

non-informatics science studies, we need to amalgamate the knowledge

them deepen understanding of sub-fields while paying due regard to theof laws, ethics, public policies, data distribution, and other humanities

curriculum of individual students' majors. Also provided are lectures

studies that are required to implement new collective decision-making

and seminars on laws, ethics, data distribution, and other humanities

mechanisms, such as Japan's unique outlook on social ethics andstudies needed to build platforms, which are designed to cultivate thefairness. With this combination of studies thus achieved, it must beability to integrate humanities and sciences. Based on these inputs andapplied to platforms and deployed globally. In this doctoral program, we

research outcomes, students are expected to develop the skills to build

call this interdisciplinary academic field "platform studies," which isplatforms on their own initiatives, to promote/manage projects andessential in building platforms, and we propose to develop the required

implement and expand their results globally, and to standardize andskills for this new academic discipline.

socially implement the results so that they can develop the results in a

●The Six Competencies Required of Platform Builders
To successfully complete this doctoral program on platform studies,one must acquire the following six competencies: ① Outstanding coreexpertise (advanced expert knowledge in the area of the primary major);② Expertise to deepen understanding in core fields (expertise to

explore core fields and the ability to foster sub-fields that complementthem); ③ The ability to build platforms on one's own initiative (theability to design/build platforms that offer solutions to social issues); ④The ability to get things done (the ability to drive/manage large projects
and expand their results globally); ⑤ The ability to sustain momentum

sustainable manner. To assist them in doing so, the program offers
guidance from instructors in multiple fields, research grants, researchinternships, research outcome matching events, international symposia,
and other opportunities. Students will also be given access tostate-of-the-art communication environments and various types of big
data. These opportunities will be provided throughindustry-university-government cooperation among 39 institutions bothwithin and outside of Kyoto University, including the Graduate School ofInformatics, Graduate School of Agriculture, Graduate School of
Medicine, School of Governance, and Disaster Prevention ResearchInstitute.

(the ability to standardize results and develop social implementation in asustainable manner); and ⑥ The ability to integrate humanities and●Website :sciences (knowledge on humanities required to build platforms,

https://www.platforms.ceppings.kyoto-u.ac.jp/

entrepreneurship, and the ability to create communities).

●E-mail :To ensure that students acquire "outstanding core expertise" in theplatforms̲contact@mail2.adm.kyoto-u.ac.jp

areas of their primary major, the program provides students with

Kyoto University Collaborative Graduate Program in Design
To respond to the complex needs of our modern society, Kyoto

Participant Organizations
Graduate School of Education (Division of Educational Studies), Graduate

program featuring an integrated, five-year curriculum for “Design”

School of Engineering (Department of Architecture and Architecturalwhich seeks solutions by calling on knowledge from a variety ofEngineering, Department of Mechanical Engineering and Science,

academic disciplines. This program aims at cultivating students who

Department of Micro Engineering, and Department of Aeronautics anddeepen their expertise in their own disciplines while working together

Astronautics), Graduate School of Informatics (Intelligence Science andwith specialists in other disciplines as well as with stakeholders to deal

Technology Course, Social Informatics Course, Applied Mathematics andwith society’s variegated issues and create a new structure for the

Physics Course, Systems Science Course, and Communications andsociety of tomorrow.

Computer Engineering Course), and Graduate School of Management

Students enrolled in this program conduct their studies around thesix core disciplines of informatics,mechanical engineering, architecture,(Department of Business Administration, Department of Management
Science)

Related Educational Programs

University Collaborative Graduate Program in Design is Japan’s firstmanagement, and psychology, while also engaging in a variety of
training and field work aimed at acquiring the ability to design society. In

Partner Organizations

order to participate in the program, a student must first be admitted into

NEC Corporation, Nippon Telegraph and Telephone Corporation (NTT),

one of the five course in the Graduate School of Informatics: Intelligence

Nomura Research Institute, Panasonic Corporation, Mitsubishi Electric

Science and Technology Course, Social Informatics Course, Applied

Corporation, Mori Building, and about 70 members in Design Innovation

Mathematics and Physics Course, Systems Science Course, andConsortium (OMRON Corporation, Sony Corporation, Takenaka

Communications and Computer Engineering Course, and then can be

Corporation, DMG Mori Corporation, DENTSU Inc., Toray Industries Inc.,

considered for selection as a Preparatory Course student and Regular

Nikken Sekkei, The Japan Research Institute, Nippon Telegraph andstudent in this program. Upon completing the program, students of theTelephone West Corporation (NTT WEST), HAKUHODO Inc., Yamaha

Doctorate Program of Graduate School of Informatics will earn either aMotor, Yokogawa Electric Corporation, etc.)

Doctorate degree (Ph.D.) or a Doctoral Degree in informatics. As for thelatter degree,certificate of the completion of the Program for Leading●Website : https://www.design.kyoto-u.ac.jp

Graduate Schools’Collaborative Graduate Program in Design’ is●E-mail : contact@design.kyoto-u.ac.jp

specified in the diploma.

Graduate School of Informatics

66

International Program at Graduate School of Informatics
Three Courses of Graduate School of Informatics,

through friendly competition with international students.

namely, the Intelligence Science and Technology Course,

Participating universities were called upon to provide

Social Informatics Course, and Communications andquality education according to their respective functions

Computer Engineering Course, have an International

and to create an environment that makes it easier for

Program in their curriculum.

international students to study in Japan. To this end, theStudents of the International Program are taught in

G30 program assisted Japan’s leading universities

Eng lis h, rec eiv e g u id a nc e i n E ng li sh f r o m th e i r

generating a hub of internationalization, including

supervisors, and acquire Master’s and Doctoral degrees

development a system in which degree programs can be

exclusively in English.

offered entirely in English, improving the environment to

These programs are open both international anddomestic students.

accept international students, and promoting of strategicinternational cooperation.

The curriculum was established at Graduate School ofInformatics after Kyoto University was designated as one●International Programs website :of the hub universities for the Project for Establishinghttps://www.i.kyoto-u.ac.jp/introduction/g30.html

Core Universities for Internationalization (Global 30/G30),●Contact : jyoho-kyomu@mail2.adm.kyoto-u.ac.jp

launched in 2009 by MEXT.
The purpose of the G30 Program was to cultivate
top-notch individuals who would play an active role in the
global arena by providing development opportunities

67(Student Aﬀairs Division)

Curriculum Policy

The Guidebook and the electronic syllabuses for all
students contain information about most subjects, together
with grading criteria.

The master’s program offers the curricula shown below
so that academic advising is provided based on thesecurricula.
General Subjects Provided by the School (Compulsory Elective)
The subjects “Perspectives in Informatics” are offered tohelp students acquire insights about the extent of
informatics. Students are required to take at least one ofthese classes according to the requirements specified bytheir course.
Seminars and Exercises for Master's Thesis (Mandatory)
Academic advisers conduct the subjects to provide
research guidance that students need to write their
Master’s thesis. Some of the courses offer opportunities forstudents to receive guidance and advice from faculty
members outside their labs.
Basic Subjects and Subjects Provided by the Course (Optional)
Each of the courses offers the graduate subjects taught in
lectures, workshops, practical training, seminars, and invarious other forms. The objective of the basic subjects is tohave students acquire basic skills and knowledge essential
for learning in the courses where graduate students withdiverse backgrounds enroll. The objective of the specialized
subjects is to impart advanced specialist skills andknowledge in the relevant fields. Some of the courses may
recommend that students take graduate subjects offered byother graduate schools at Kyoto University as electives.
When students choose which of the subjects to take, theiracademic advisers consider their undergraduate majors aswell as their graduate research projects when giving
counsel and advice to ensure that the students take classes
suited to their aptitudes.
Auditing Subjects offered for undergraduate courses
Since informatics spans a range of academic disciplines,
some students, depending on their undergraduate majors,
may need to acquire more basic knowledge and academic
skills essential for the research they plan to conduct. If thisis the case, students are encouraged to take basic
undergraduate subjects the university offers in order toacquire the knowledge and skills they need. The credits
earned from these subjects are processed as additional
credits that do not count toward completing the degrees.
Requirements for Completion of the Master's Program
Students must earn at least 30 credits from the subjects
listed below, according to their course’s requirements. They
are also required to receive the research guidance theyneed, and their Master’s thesis must pass the evaluationand examination.
Seminars and exercises for Master's thesis (Mandatory)
and Master’s thesis (Mandatory)
General subjects provided by the School (Compulsory
elective)
Basic Subjects and Subjects provided by the Course(Including Subjects Provided by Other Courses, Optional)

Academic Programs

Paying due respect to Kyoto University’s principles, Kyoto
University Graduate School of Informatics provides
education that aims to develop students’ leadership skills
that are needed to solve the problems facing aknowledge-based society. As stated in the diploma policy,
we train students as researchers to evolve “the study of
information in a broad sense of the term,” as defined by thegraduate school, and to work as true professionals byacquiring high specialist knowledge in their academic
disciplines and developing considerable expertise. To thisend, our education consists of systematic and tiered
academic programs designed to provide not only advanced
knowledge and insight in each research field but also acomprehensive academic overview of informatics. These
programs also help students acquire the qualities essential
for a successful career in a global society, including highethical standards and a keen sense of responsibility, alongwith communication skills.
To be more specific, this graduate school provides
education designed not only to allow students to study the
specialist field of their choice but also to equip them with abroad range of knowledge that transcends the boundaries
of disciplines. If the systematic, tiered and high professional
education that revolves around each course, coupled withresearch guidance given by academic advisers, is likened to
the warp, the subjects taught across the courses and theresearch guidance that transcends the laboratory are theweft. The warp and weft here are aptly combined into an
intricate educational system. Academic advisers also
provide research guidance that helps students develop
ethical standards, including research integrity, as well as asense of responsibility. Furthermore, the graduate school
offers some classes conducted in English, along with anacademic program in which subjects are available only inEnglish, in order to develop communication skills thatstudents need to be successful in the international
community. Each grade is determined by tallying exam
scores and marks given for class participation, considering
the objectives and characteristics of the subject. Grading isdone strictly, according to the grading policy and the policy
on examining theses. We also work with the university’s
other academic programs to provide educational support
for students with a broad range of interests.
The master’s program, which has a large number oflecture classes, is systematically structured to teach course
subjects that are tiered in a way that enhances learning. In
addition, it has the inter-departmental subjects“Perspectives in Informatics” as required electives, andsome of which are offered in English. The program also
provides guidance to help students with diverse
backgrounds to improve their basic academic skills in
informatics. Except for the required research guidance
subjects, most of the program’s subjects are electives. To
help students choose which subjects to take, academic
advisers provide them with a “course tree for learning”
during discussion-based academic advising, and give advice
based on each student’s academic background and otherlearning-related factors, as well as what the students plan
to do after completing the program. The doctoral program
offers seminar subjects specific to each course, other thanresearch guidance provided by academic advisers to each
student. By receiving advice on research from advisers in
different research fields, students acquire a broad
academic overview.

Requirements for Completion of the Doctoral Program
Students must earn at least six credits from the subjects
offered by Graduate School of Informatics. They are also
required to receive the research guidance they need, andtheir doctoral thesis must pass the evaluation andexamination.
Doctoral Thesis (Mandatory)
Graduate School of Informatics

68

Academic Programs
Graduate School of Informatics believes that its
students should have an understanding of its philosophy
and be committed to acquiring the academic knowledge
and skills needed for “the study of information in the
broad sense of the term” that the school presents. We
seek to have a wide range of talented students who areeager to explore new realms of informatics for the future.
Hence, we accept applicants from Japan or overseas withd iver se b a c k gr o u n ds c o nn e cte d to “th e s tudy o f
information in the broad sense of the term.” Whether theapplicants are in sciences or humanities does not matteras long as they have basic academic skills at a certain
level in the academic fields in which they seek to pursue
their studies and research. We also keep the door wide
open to applicants who already have a career but seek toenthusiastically learn informatics.
To elaborate further, our process of selecting from thetalented and qualified applicants who are eager to learn
basically consists of written and oral examinations. We
accept a wide range of applicants, regardless of theirbackgrounds, who have a keen interest in any academic
fields related to “the study of information in the broad
sense of the term” that Graduate School of Informatics
aims to pursue and who have basic academic skills in
those fields, along with excellent communication skills.
To select applicants for the doctoral program, we review
submitted documents to identify applicants’determination and ability to commit to acquiring the mostadvanced knowledge about “the study of information in
the broad sense of the term,” which will then be built on
their basic academic skills for their specialist fields. Then
we accept those who are committed to the creation ofacademic studies and the research of technologicaldevelopments in order to contribute to the future growthof informatics.
The table in the right column shows the number ofstudents to be admitted.

Each course holds their graduate school entrance
examinations sometime between mid-July and mid-Augusteach year. Some of them do it in mid-December and accept
applications again in mid-February. The courses also admit
students who enroll in October.
Three of the courses, namely Intelligence Science andTechnology, Social Informatics, and Communications andComputer Engineering Courses, accept applications totheir international programs, which allow students totake all courses in English to complete the degree
requirements.
Please see the application guideline for details.
■ Admission Quota

Department of Informatics

Masterʼs Program

Doctoral Program

240

60

■ The Number of Students to be Admitted by Course
Masterʼs Program

Doctoral ProgramIntelligence Science andTechnology Course

42

13Social Informatics Course

50

14

20

6

28

6

Systems Science Course

31

6Communications and Computer
Engineering Course

51

11

Data Science Course

18

4

Advanced Mathematical
Sciences Course
Applied Mathematics andPhysics Course

University Fellowship in Informatics
If you wish to be a proficient researcher who leads
research and development in informatics, you should enroll
in a doctoral program. To lower the financial obstacles in
obtaining a doctoral degree, the Kyoto University Scienceand Technology Innovation Fellowship has been establishedwith support from the Ministry of Education, Culture,
Sports, Science and Technology (MEXT) under its
University Fellowship Program for the Creation of Innovation●Number of fellowship recipients: 25/grade●Stipends: JPY1.8 million per year (in monthly installments of
JPY150,000)●Research fund: JPY300,000 per year (under management bysupervisors)●Selection: Applicants submit a statement of past researchand research plan, as well as a letter of recommendation by
their supervisors, based on which their past academic andresearch achievements and research perspectives after
enrollment will be screened.

69

in Science and Technology starting in 2021. Responsible forthe fields of information science and AI, Graduate School ofInformatics facilitates financial support for students in
doctoral programs, who would lead research in theirrespective areas of specialty.
Recipients of the fellowship are requested to participate inprograms, etc., organized by Kyoto University and Graduate
School of Informatics, so that they can enhance theirresearch prowess and build a career path in diverse
academic and industrial fields after completing the program.
Some of the programs include presentation of research at
the Kyoto University ICT Innovation, participation in a
research internship, or being a teaching assistant in data
science.
●https://www.i.kyoto-u.ac.jp/fellowship/Career path after graduation【Master's students】
Ministry of the Environment
Government of Japan
Ministry of Land, Infrastructure,
Transport and Tourism

SoftBank Corp.

TOYOTA SYSTEMS CORPORATIONBeNEXT Technologies

Sapporo Fire Bureau.

DAIHATSU MOTOR CO., LTD.
Panasonic Corporation Living
Appliances and Solutions Company

Kyoto University Hospital

Future Corporation

MUFG Bank, Ltd.

unerry, Inc.

FreeBit Co., Ltd.

Mitsubishi Research Institute, Inc.

atama plus Inc.

MCKINSEY & COMPANY, INC., JAPANKomatsu Ltd.

GMO GlobalSign K.K.

Miraxia Edge Technology Corporation

Kobe Steel, Ltd.

Huawei Technologies Co., Ltd.

morikatron Inc.

Hitachi, Ltd.

Indeed Japan co., Ltd.

Yahoo Japan Corporation.

Hitachi, Ltd. Central ResearchLaboratory

KDDI CORPORATION

Yamaha Motor Co., Ltd.

Honda R&D Co., Ltd.

LINE Corporation

CINC corp.
JR East Information Systems
Company

Nomura Research Institute, Ltd.

Mc Digital. Inc

Recruit Holdings Co., Ltd.

Hankyu Hanshin Holdings, Inc.

PwC Group

KADOKAWA CORPORATIONTSMC Design Technology Japan, Inc.

MonotaRO Co., Ltd.

Mitsubishi UFJ Information
Technology, Ltd.
Mitsubishi UFJ Morgan Stanley
Securities Co., Ltd.

Accenture Japan Ltd

NTT DOCOMO, INC.

Sumitomo Electric Industries, Ltd.

Amazon Japan G.K.

Infcurion, Inc.

IDEA Consultants, Inc.

Elysium Co. Ltd.

Excite Japan Co., Ltd.

CAPCOM CO., LTD.

MORI BUILDING CO., LTD.
NIPPON TELEGRAPH AND
TELEPHONE CORPORATIONTOKIO MARINE & NICHIDO RISKCONSULTING CO., LTD.

Angel Group Co., Ltd.

Keyence Corporation

Tokyo Gas Co., Ltd.

KOEI TECMO GAMES CO., LTD.
Kiuchi Instruments MaintenanceCorporation

KOEI TECMO HOLDINGS CO., LTD.

NS Solutions Corporation

CyberAgent, Inc.

IBM Japan, Ltd.

Google LLC

SEGA CORPORATIONNEC Corporation

GREE, Inc.

SenseTime Japan Ltd.

NIPPON TELEGRAPH AND
TELEPHONE CORPORATION

Cybozu, Inc.

Sofix CO., LTD.

Nintendo Co., Ltd.

Sharp Corporation

DeNA Co., Ltd.

Honda Motor Co., Ltd.

Sony Group Corporation
Sony Semiconductor Solutions
Corporation

DISCO Corporation

Nomura Asset Management Co., Ltd.

DataGrid Inc.

Nomura Securities Co., Ltd.

Sony Corporation

TechnoPro, Inc. TechnoPro
Engineering Company

Seikei University

Nagoya University

OMRON Corporation

RIKEN
National University Corporation,
Kyoto University.

Yamato University
National Institute of Technology(KOSEN), Sasebo College

ROHM Co., Ltd.

University of Osaka

GMO Pepabo, Inc.

Nomura Securities Co., Ltd.

Academic Programs【Doctoral students】

MonotaRO Co., Ltd.

Graduate School of Informatics

70

List of Departments, Divisions and Groups
Group

Division
Brain and Cognitive Sciences

Neuroinformatics

Psychoinformatics

Cognitive Informatics

Cognitive System

Computational
Intelligence

Collective Intelligence

Conversational
Informatics

Intelligence Media

Language Media
Processing

Speech and Audio
Processing

Computer Vision

Human Sensing

Text Media

Application of Multimedia(collaborative division)
Bio-system Informatics(collaborative division)

Biological Information
Networks

Social Information Model

Distributed Information
Human-Robot Interaction
Systems

Social Information Network

Consensus Informatics

Biosphere Informatics

Bioresource Informatics Environmental Informatics

Regional and DisasterIntegrated Disaster
Management Information Systems
Management Systems(collaborative division)
Medical Informatics(collaborative division)

Regional and Disaster
Management Information

Medical Informatics

Dep
artment of In

Social Informatics Analytics
Infrastructure

Learning and
Educational Technologies

Data Engineering and
Platform Research

Applied Analysis

Analysis of Inverse
Problems

Analysis of Nonlinear
Problems

Nonlinear Physics

Nonlinear Dynamics
and Computational
Statistical Physics

Non-equilibrium Physics andTheoretical Neuroscience

Computational Mechanics Industrial Mathematics

Applied Mathematics

Applied Mathematical
AnalysisDiscrete Mathematics

Applied Mathematical Systems

System Optimization

Control Systems Theory

Mathematical Physics

Physical Statistics

Dynamical Systems

Human Machine Symbiosis

Mechanical Systems
Control

Human Systems

ics

formatApplied Mathematical Sciences

Integrated Dynamical
Systems

Systems Synthesis

Mathematical
Information Systems

Statistical Intelligence

Systems Informatics

Learning Machines

Integrated Systems
Biology

Biomedical Engineering

Computer Engineering

Computer Algorithms

Computer Architecture

Computer Software

Communications Systems
Engineering

Digital Communications

Integrated-Media
Communications

Intelligent Communication
Networks

Integrated Systems Engineering Processor Architecture andSystems Synthesis

Integrated Circuits
Design Engineering

Radio Atmospheric Sciences(Affiliated)

Remote Sensing
Engineering

Information andCommunication Infrastructure Multimedia and Secure
Networking(collaborative division)
Innovative Research and
Education in Data Science71

Disaster Reduction
Information Systems

Social Media Unit

Statistical Inference

Advanced Signal
Processing

Atmospheric Observations
Supercomputing
Signal and Information Medical and Healthcare
Processing
Data Science

Mobility Research

Advanced Information-Education & Digital Education Infrastructure Unit(Center for the Promotion of Interdisciplinary Education and Research(C-PiER))

The Advanced Information-Related Education & Digital
Education Infrastructure Unit was established in Kyoto
University C-PiER to implement the budget request project:
Fostering Global Human Resources by Innovating
Undergraduate/Graduate-level Information-related Education& Digitized Education of Graduate school of informatics, Kyoto

University. It is our hope that students who will shape thefuture of our society will acquire essential skills to utilize
information regardless of their areas of expertise so that theycan enhance their information and communication skills aswell as their global mindset.

■ Innovative Educational Programs
The Program

Conventional Programs

Computer Literacy

Information Literacy / Data Literacy

Computer EngineeringSocial Information Management
Our information-related
educational program

Our information-related
educational program
Conventional
information-related
educational programs

Educating IT
for research

Computer literacy(Educating
how-to-use IT)

Information literacy(acquisition, generation,collecting, analysis,
and presenting of
information)

Conventional
information-related
educational programs
Computer science &engineering (computers, OS,programming Languages,
networking etc.)
programming Languages,
networking etc.)

Social information
management,
IT impacts on society

Education programs
focusing on information
literacy, data literacy,
and social information
management

Innovation by
IT (technology
management,
business model)■ Digitized Education Environment■ BYOD Classroom Learning● Usage of tablets & note PCs in usual classrooms● Active learning classrooms■ Online Lectures● Online Lectures■ Faculties
Akihiro Yamamoto

Professor
Graduate School ofInformatics

Keishi Tajima

Yoshikazu Maegawa

Professor
Institute for Liberal Arts
and Sciences

Professor
Graduate School ofManagement

Hiroyuki Sato

Satoshi Shimada

Huang, Yinjou

Kanae kotigami

Associate Professor
Graduate School ofInformatics

Senior Lecturer
Graduate School ofManagement

Program-Specific
Assistant Professor
Graduate School ofInformatcis

Program-Specific
Assistant Professor
Graduate School ofInformatcis■ Courses[Liberal Arts Courses][Graduate Courses]
Interdisciplinary
Graduate Courses

・Basic Informatics (General)

・Introduction to Information & Intellectual Property

・Informatics Fundamentals & Practice・Innovation and Information

・Information and Enterprise・Information and Society

List of Departments, Divisions and Groups

Unit Leader /Professor
Graduate School ofInformatcis

Nobuo Yamashita

・Information Analysis and Management
・Information Analysis and Management, Exercise
・Computational Science, Introductione
・Exercise on Computational Science A
・Exercise on Computational Science B
・Information and Intellectual Property
・Service Modeling & Applying Strategy
・Computation Science for Big Data・Innovation and Information
・Information Security
・Artificial Intelligence, Advanced■ Contact Yoshida Honmachi, Sakyo, Kyoto 606-8501 Research Building #12, Rooms 110, 112

E-mail : iedu@i.kyoto-u.ac.jp
Graduate School of Informatics

72

The Kyoto University ICT Collaboration Promotion Network

The Kyoto University ICT Collaboration Promotion Network was jointly established by Graduate School ofInformatics and the Academic Center for Computing and Media Studies at Kyoto University in February 2008.
This network serves as a foundation for industry-government-academia, academia-academia, andcommunity-academia collaborations between faculty, researchers, and (graduate) students of Graduate School ofInformatics and Academic Center for Computing and Media Studies and companies, NPOs, local governments, andother entities outside the university.

■ Kyoto University ICT Innovation
At this annual event, faculty, researchers, andresearch focus on concrete results, particularly software

graduate of Graduate School of Informatics andand products. The event will also be organically linked to

Academic Center for Computing and Media Studies offer

guidance for job seekers. This event will be organized inoutlines of their research and development work

collaboration with Kyoto Prefecture, Kyoto City, andrelating to information and communication technologies

other local organizations, with the aim of making it more

(ICT), in the form of posters, demonstrations, and oral

accessible to the general public.

presentations. In addition to presentations on the latest

ICT Innovation is open to everyone, but exhibitors fromtechnologies and products, the event includes theoutside the university can participate only if they are

introduction of collaborative initiatives with industry,members of the ICT Collaboration Promotion Network.

government, and academia.
The main purpose of the annual event is to promoteindustry-academia matching. The presentations of■ Coordination of Industry-Academia Collaboration

1. Industry → Academia
We collect technology development themes from companies participating in the Collaboration Promotion
Network, taking a view directed five to 10 years into the future. Typically, we suggest that applicant companies
describe two or three themes on a few PowerPoint slides. The secretariat then attempts to match our labs withcompanies that have expressed interest, thereby opening the way for joint research or contract research.

2. Academia → Industry
The university collects research findings that show promise for practical implementation and discloses them tocompanies participating in the “Collaboration Promotion Network.”
The secretariat then conducts small seminars for matching with companies that have expressed interest, paving
the way to a joint or contract research project.
A unique feature of this coordination is that the Collaboration Promotion Network Steering Committee
systematically match all the labs of Graduate School of Informatics and the Academic Center for Computing andMedia Studies with the relevant departments of the approximately 50 member companies of the network. This has
already led to the launch of a completely new type of collaborative research project.

For more information, contact us at the email address on the right.
Kyoto University Original Co., Ltd.

73

mail : ictrenkei@kyodai-original.co.jp

Request for Donations to the Fund
■ Graduate School of Informatics Fund
Graduate School of Informatics was established on
three key pillars: interfacing with humans and society,mathematical modeling, and information systems.
The educational goal of the graduate school is to train
researchers to develop the field of informatics andhigh-quality, skilled professionals, by fosteringhigh-level research skills and rich academic learning,to help cultivate outstanding individuals with a broad
vision and the potential to become leaders in solving awide range of real-world problems.
Generating new ideas and innovations that will
support our future society requires the ability to thinktheoretically and abstractly, the ability to clearly
distinguish the essential from the inessential, and theability to build and organize ideas. In all its educationand training activities, the Kyoto University Graduate
School of Informatics has always put a strongemphasis on the cultivation of these fundamental
skills, on top of the acquisition of expert knowledge in
specific fields. The purpose of Graduate School ofInformatics Fund is to help us to enhance our efforts
at cultivating human resources, both in terms of
quality and quantity, and to support original researchwith the potential to create future innovations.

■ Use of the Fund・Supporting the study and research of graduate students
Providing scholarships, mainly for doctoral students
Providing opportunities to send students to study at other universities, research institutes, etc. overseas or in Japan・Supporting young researchers
Providing opportunities to send young researchers to overseas universities or research institutions for medium to long periods
・Supporting research
Supporting the work of researchers affiliated with Graduate School of Informatics※Donations can also be made to a specific research project, or specific faculty member or researcher at the graduate school, or for a specific use. If you would
like to offer a donation, please contact us at the email address below.

■ Benefits for donors
・Receiving PR information about Graduate School of Informatics
・Receiving information on academic events organized by Graduate School of Informatics
・Names of donors are included in PR materials of Graduate School of Informatics
・Priority when registering for lectures or other events organized by Graduate School of Informatics (when places are limited)
・Opportunity to join social gatherings with faculty members of Graduate School of Informatics (about once a year)
If you would prefer not to receive any of the above benefits, please let us know.
For inquiries about Graduate School of Informatics Fund:075-753-5945 infofund@i.kyoto-u.ac.jp■ Donation can be made here.

Kyoto University Fund WEB page

Graduate School of Informatics

74