Systems Science Course

New Frontier in Informaticsand 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 thestudy 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 “hyperbolicspace.”
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, Departmentof Systems Science, Graduate School of Informatics, Kyoto University.

Graduate School of Informatics

42Systems Science CourseOutline■Group and Teaching StaffGroupTeaching Staff

Mechanical SystemsControlShun-ichi Azuma/Professor

Human Systems

Manabu Kano/Professor Shota Kato/Assistant Professor

Integrated Dynamical
SystemsToshiyuki Ohtsuka/Professor Kazunori Sakurama/Associate Professor
Kenta Hoshino/Assistant Professor

Mobility Research

Osamu Nishihara/Associate Professor

Mathematical Information
SystemsToshiyuki 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 
nd2 
st1 

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 themotion 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 thefuture. 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 thosewho 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 ourmethodologies 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 stabilitycontrols, which have found practical applications in active safety systems for reducing car accidents,
energy efficiency improvements, and collision avoidance systems. These research topics are beinginvestigated 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, whichaddresses 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 desiredmulti-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