Faculty Members and Labs in Department of Computer Science
Kobayashi Lab

Professor Naoki Kobayashi

Our research group is studying theoretical foundations for software and their applications such as program verification.
On the one hand, an increasing number of important systems such as transportation systems, medical devices, andbanking systems are now controlled by computer software, and a bug of such software can cause a serious disaster. On
the other hand, such software is becoming more and more complex and larger, and it is difficult to maintain the qualityof software by using traditional software engineering technologies like testing. In view of these situations, we aim to
improve the reliability and efficiency of software by developing automated techniques for program verification andtransformation based on rigorous mathematical methods. To achieve the goal, we also need to study and advance many
research topics in theoretical computer science, such as type theory, formal languages and automata, and automatedtheorem proving. It is a pleasure of our research to find out that deep mathematical results, which initially seem to be
only of theoretical interests, are actually quite useful for the practically-motivated research mentioned above. See
https://www.kb.is.s.u-tokyo.ac.jp for details.
Recent research topics include:
1. Higher-order model checking: This is an extension of traditional model checking that has been successfully applied
to system verification. We have constructed the first higher-order model checker in the world.
2. Automated program verification: By applying the higher-order model checking mentioned above and type theory,
we are developing fully-automated program verification tools for programming languages like OCaml, Rust, and C.
3. Data compression: String and tree data can be compressed in the form of programs that generate them. The higherorder model checking above can be used to transform such compressed data without decompression.
4. Programming language and formal language theories: We are studying type theory, lambda-calculus, higher-orderfixpoint logic, etc. as theoretical foundations for the studies 1-3 mentioned above.
5. Protocol verification: Cryptographic communication protocols are used, for example in Internet shopping, for safely
exchanging confidential data. We are developing a method for automated verification of such protocols.

Igarashi Lab

Professor Takeo Igarashi(1) User Interface: We are working on user interfaces for information appliances ranging
from personal computers, smartphone, robots, and self-driving automobiles. We not only
develop techniques to improve efficiency but also propose new ways of interaction.
- User interfaces for machine learning and artificial intelligence. We
focus on the preparation of training data and interactive learning.
- Interaction techniques for novel appliances such as smartphone,smart watches, AI speakers and smart glasses.
- Interaction techniques for giving directions and controlling realworld systems such as robots and self-driving automobiles.
(2) Graphics: We mainly work on interactive shape modeling. We work on content creation
for digital media such as movies and games. We also work on interaction techniques for
digital fabrication using 3D printers and laser cutters.
- Content creation such as 3D modeling and 2D animation using recent
technologies such as sketching and machine learning. We also work on
interaction techniques for medical imaging.
- Shape modeling for real world objects such as musical instruments,
clothes, and toys leveraging real-time physical simulation.
- Novel techniques for 3D scanning and 3D printing to support personal fabrication.
We can provide opportunity for students to collaborate with research groups in othercountries and productions. http://www-ui.is.s.u-tokyo.ac.jp/

Miyao Lab

Professor Yusuke Miyao

Natural Language Processing, Computational Linguistics, and Human Cognition
The goal of our research group is to enable computers to understand natural language. Humans
communicate with each other using language, while its mechanism is still largely mysterious. We apply
theories and technologies from computer science and linguistics to model the process of understanding
and generating natural language. Specific research themes include: syntactic parsing to compute the latent
structures of sentences, semantic analysis to compute natural language semantics, and the analysis andevaluation of language understanding capabilities of large language models.
Our group is also engaged in developing real-world applications of natural language processing,focusing on applying the abovementioned fundamental technologies. Examples include dialogue systems
for communicating with computers using natural language and data-to-text conversion to describe various
types of non-linguistic information in the world, such as images and numerical data, in natural language.
Refer to our homepage for the details of the research: https://mynlp.is.s.u-tokyo.ac.jp/en/
Syntactic/semantic parsing using
Combinatory Categorial Grammar

Issei Sato Lab

Professor Issei Sato

We humans decide our behaviors on the basis of knowledge learned and abstracted from pastexperiences and current situations. Machine learning is a field of artificial intelligence for improving
the performance and behaviors of a machine through the use of data accumulated from past humanexperiences and current human interaction.
Currently, we are working on the following four main research themes.
1. Generalization and memorization: Generalization is the property of predicting unknown data that
does not exist in the training data. Memorization refers to storing the training data in the memoryin learning algorithms; however, simply memorizing the training data does not lead togeneralization, and over-abstracting the training data reduces the prediction performance.
Analyzing the relationship between generalization and memorization is the most fundamental
problem in machine learning research.
2. Perturbation and uncertainty. How perturbations to training data and model parameters, as wellas uncertainty in predictions, affect learning algorithms.
3. Representation learning. What kind of abstract representation of training data leads togeneralization.
4. Robustness. Property that is necessary for learning appropriately even when the training data or
the data to be predicted are different from what is expected.
Required knowledge.
We use probability & statistics, linear algebra, functional analysis, and optimization as tools toconstruct the theory of machine learning. Our lab. assumes that the students are familiar with thesemathematics. In addition, programming skills, primarily in Python, are also needed, because we
perform exhaustive empirical analysis of the theory.
See https://www.ml.is.s.u-tokyo.ac.jp/ for details.

Kawarabayashi lab

Professor Ken-ichi Kawarabayashi

Our laboratory focuses on "algorithms", "discrete mathematics", and their applications. In particular, we plan to study
the following topics, focusing mainly on "graphs".
1. Develop theoretically fast and accurate algorithms for graph problems or prove NP-hardness for them
2. Develop scalable and accurate algorithms that can be implemented, using theoretical tools such as discrete
mathematics, for graph datasets in the real world.
3. Develop algorithms that can run theoretically (or practically) quite fast (and accurate), when limiting graph family(for example, planar graphs or social networks).
4. Apply knowledge and implementation techniques of graph algorithms to machine learning, especially online learningand deep learning
5. Research on graphs that appear in the fields of natural language processing, machine learning, databases, data mining,
programming languages, etc.
6. Combinatorial optimization and/or discrete mathematics
In our laboratory, we plan to interact with top researchers for theory in overseas for the above "theoretical" research, andjointly work with researchers in Japan and overseas, including industries, for other research topics.
Algorithm innovations such as current information retrieval technology (Google's PageRank) and security technology(Apple's (Local) Different Privacy) have led to the creation of national-scale businesses. What is important here is thatboth PageRank and Differential Privacy are basic and theoretical researches of algorithms and discrete mathematics, notapplication-oriented work from the beginning. In our laboratory as well, we plan to focus on basic and theoreticalresearch, rather than conducting research with applications in mind from the beginning. E-mail: k_keniti@nii.ac.jp

Shinagawa Lab

Professor Takahiro Shinagawa

The research field of our laboratory is System Software, with a focus on operating systems (OS) and virtualization
software. System software plays an important role in connecting computer hardware and applications, and its capabilities
greatly affect the overall system performance. In particular, OS kernels and hypervisors have special privileges to control
the entire computer systems and have different capabilities from other software. Moreover, while system software
research has a scientific aspect of providing abstract and generalized concepts of computers for applications, it also has
an engineering aspect of designing, implementing, and evaluating the software on actual hardware to demonstrate theirusefulness. Therefore, system software is a fascinating field that lies at the boundary of different disciplines.
Specifically, Shinagawa Laboratory has been working on the following topics in recent years.
- Studies on improving security through system software
We are working on studies on secure isolated execution environments for applications. Examples include achieving
lightweight and robust protection with TEEs, such as AMD SEV-SNP and Intel TDX, lightweight VMs exploiting
CHERI researched and developed by Cambridge University, and an FPGA-based RDMA-based OS. We also aim to
improve security in various ways, such as finding vulnerabilities in nested virtualization through Fuzzing and use-afterfree mitigations that utilize the CPU's address mask function.
- Studies on improving system software performance and functionality
We are working on various studies to improve the performance and functionality of system software in general, such as
OS live migration, container startup acceleration, file systems for non-volatile memory, low-latency cluster schedulers,and OS emulators that run Linux binaries on macOS and Windows.
Shinagawa Lab aims to make research interesting and fun. Students who like and/or are good at low-layer system
software (Linux, KVM, BitVisor, etc.) and system programming (C, C++, Rust, etc.) are especially welcome, but if you
are interested in system software at any layer, you will enjoy working with us.
Laboratory homepage: https://www.os.is.s.u-tokyo.ac.jp/en/ E-mail: shina@is.s.u-tokyo.ac.jp

Yoshimoto Lab

Associate Professor Yoshihide Yoshimoto

One of the major motivations to invent electronic computers was the application to science and technology. After theinvention, the performance of the computers improved dramatically with the exponential development of semiconductor
technology: Moore’s law. Computational science, which advances science with computation, has benefited greatly fromthe development. Nowadays, however, because the limitation of semiconductor technology is coming up to the surface,
the complexity of computer systems such as parallelization etc. is so increasing that both computational science andcomputer science have to cooperate once again for the further progress. This laboratory was set up under the above
background to perform education and research which connects computational science and computer science.
(https:/www.cp.is.s.u-tokyo.ac.jp/ )Yoshimoto himself is specialized in solid-state physics which
elucidates the properties of materials such as semiconductors,
metals, dielectrics, and magnets. Especially, he is specialized in
the first-principles electronic structure calculations which
accurately simulates the quantum mechanics of electrons which
dominates most of the properties of materials. He has developed
and opened to the public a program package xTAPP for thispurpose. The right-hand side figures are examples ofvisualization of an electronic structure calculation.
( http://xtapp.cp.is.s.u-tokyo.ac.jp/ )From the electronic structure calculation as a hometown, the aim of the laboratory set up between computational
science and computer science is as follows:1.
Perform mutual exchange between wide range of fields in computational science by re-interpretation of themethods developed individually in each field from viewpoints of computer science.
2.
Understand needs of computational science from viewpoints of computer science and propose more essential
solutions.

Takamaeda Lab

Associate Professor Shinya Takamaeda-Yamazaki

CASYS: Laboratory for Computer Architecture and Systems

Our main research interest is the future computer architecture,including (1) custom computing using FPGA and specialized LSI, (2)algorithm/hardware co-design for machine learning, (3) high-level
synthesis compiler for hardware design. We are also pursuing
software researches on compilers, programming models, andframeworks for user/programmer friendly computers. We havevarious active collaborations with companies. We tackle real
problems with our innovative architectural technologies.

QUEST: DNN Processor

Recent research topics: (new topics are welcome)◼ Secure and high-performance CPU architecture and memory system◼ Open-source hardware design language and compiler◼ Probabilistic computer architecture based on stochastic
thermodynamics◼ Processing-in-Memory circuit and architecture◼ Federated learning for trusted AI◼ High-performance and low-power AI chip
◼ Machine learning algorithm for embedded hardware
NNgen: DNN-HW◼ Fast and energy-efficient FPGA accelerator
Synthesis Compiler
Lab WEB: https://sites.google.com/view/casys/
GitHub: https://github.com/casys-utokyo/ https://github.com/PyHDI/ https://github.com/NNgen/Ma Lab (Momentum Lab)

Associate Professor Lei Ma

Since the past decade, data-driven AI is gradually eating software, which leverages the data-driven programming
paradigm to develop software by learning from data. As real-world intelligent systems are often complex that contains
both traditional software and AI components, designing fundamental methodology and engineering techniques forbuilding trustworthy intelligent software could be a key challenge and theme for both research community and industries.
(1) Our lab focuses on both sides of software theory
and software engineering, to design novel
methodology, engineering techniques and toolchains
towards building trustworthy intelligent softwaresystems.
(2) We also actively adapt and adopt intelligent
software techniques to diverse cyber-physicalsystems and cyber-cyber systems across industrial
domains to solve real-world challenges.
Our lab particularly encourages impactful research, and collaboration with researchers around the world to tackle
challenging industrial problems with trustworthy software-driven techniques for social good. More details and concrete
topic examples of our research can be found at: https://www.malei.org/lab.html , Email: malei@is.s.u-tokyo.ac.jp

Yanaka Lab

Associate Professor Hitomi Yanaka

Language allows people to communicate with great precision, and most of the information in the world is composed oflanguage. With the development of information technology, natural language processing (NLP) applications based onmachine learning models with large corpora, such as information retrieval and machine translation, have been widelyused in our daily lives. However, it is not clear to what extent these NLP applications understand the meanings ofnatural language like humans. We are working on theoretical and empirical research with various domestic andinternational researchers in diverse fields to realize human-like natural language understanding systems.
(1) Analyzing language models from multiple perspectives of theoretical linguistics, cognitive science, and philosophy
Do current language models understand the various meanings of natural language? Recent work has pointed out
trustworthiness issues, where language models learn unexpected biases and heuristics from data. In this laboratory, we
analyze language models from multiple perspectives of theoretical linguistics, cognitive science, and philosophy with
relevant researchers.
(2) Developing inference systems by integrating machine learning-based approaches and logic-based approaches
Machine learning approaches with large corpora have been used to represent the meanings of sentences and calculate
their semantic relatedness. However, whether such end-to-end approaches can handle logical meanings of sentencessuch as negation and quantifiers remains unclear. By contrast, logic-based approaches have been successful inrepresenting such logical meanings as logical formulas and performing inference in an interpretable way. To haveadvantages over both logic-based and machine learning-based approaches, we are studying hybrid semantic parsing
and inference systems by integrating machine learning-based and logic-based approaches.
(3) NLP applications through interaction between humans and inference systems
Interaction between humans and inference systems is expected to improve the efficiency of data checking, such as
medical texts and internal documents, for which quality assurance is required. Through joint research with companies,
we are developing methods to convert human tacit knowledge into formal knowledge and store it, as well as applied
technologies for inference systems that perform inference based on various data types and usages.
See https://ylab.mystrikingly.com/ for details.

Sugiyama and Ishida Lab

Professor Masashi Sugiyama
Lecturer Takashi Ishida

With the dramatic performance improvement of information and communication technology, intelligent information
processing that was done only by humans is becoming possible also by computers. Under the theme of "howintelligent can computers be?", our laboratory is working on various research topics related to intelligent data analysis,
called machine learning, in the field of artificial intelligence.
(1) Construction of Learning Theory
Generalization is the ability to cope with unknown
situations, and is indispensable for computers to behave
intelligently. We are theoretically investigating the mechanismof acquiring the generalization capability based mainly on
probability and statistics.
(2) Development of Learning Algorithms
Machine learning involves various subjects such as
supervised learning (learning from input-output paired data),
unsupervised learning (learning from input-only data), andreinforcement learning (learning through interaction with anenvironment). We are developing practical and theoreticallymotivated machine learning algorithms.
(3) Application of Machine Learning Technologies to RealWorld
Growth and spread of the Internet and sensor technologies
allow us to collect a huge amount of data in engineering andfundamental sciences such as documents, audio, images,
movies, e-commerce, electric power, medicine, and biology.
We are collaborating with industry partners and applying
state-of-the-art machine learning technologies to solving realworld challenging problems.
http://www.ms.k.u-tokyo.ac.jp/

Yokoya Lab

Associate Professor Naoto Yokoya

Our lab works on challenges in the acquisition and understanding of visual information based on image processing andmachine learning. In particular, we focus on developing technologies for automatically constructing digital twins ofcities from remote sensing data.
(1) Image Inverse Problem
By integrating sensing and computation, we can obtain information that cannot be obtained from hardware alone,overcoming limitations such as resolution and noise. We are working to develop mathematical models and algorithms
based on machine learning, optimization, and image processing to reconstruct original signals from incomplete
observational data.
(2) Scene Understanding
We are exploring methods to integrate different types of
sensor data, such as optical images and LiDAR data, tocomprehensively understand the semantic and 3D
information of scenes with greater detail and accuracy.
In addition, we are exploring approaches to build
machine learning models from limited training data andimprove computational efficiency.
(3) Remote Sensing
With the goal of automatically constructing digital twinsof cities, we are developing techniques to extract map
information such as land cover and elevation models
from spaceborne and airborne remote sensing data
through intelligent information processing.

Nakai and Park Lab

Professor Kenta Nakai
Associate Professor Sung-Joon Park

Our lab is one of the laboratories specializing inbioinformatics on the Shirokanedai campus(https://fais.hgc.jp/). The basic motivation of our research
activities is a naive wish to understand how biologicalinformation is encoded as DNA sequences, which are thedata type of a simple one-dimensional string. Particularly,
we try to understand the regulatory modes of genes that are
encoded in the genome with various approaches fromcomputer science. In other words, we aim to understand how
the common genome DNA sequence can specify a variety ofcell types that constitute our body.

A model for predicting RNA splice sites from given entire pre-RNA
sequences, proposed by a lab student（Miyachi, unpublished）

For example, we challenge to characterize the higher-order genome structure that is responsible for the regulation ofgene expression, to analyze the temporal and spatial patterns of gene expression at the single-cell resolution, to clarify
the mechanism of RNA splicing that occurs post-transcriptionally, and so on. All these studies are designed andperformed on the supercomputer system, typically using a combination of natural language models and deep learningtechniques. We also pursue collaborations with experimental researchers to verify our in silico prediction results and tocontribute to finding ways to treat uncurable diseases and boost regenerative medicine. Thus, we not only analyze data
and/or develop algorithms but also construct and maintain necessary databases that are open to the public.
Our lab members have a variety of research and international backgrounds, but they all share the common mindset ofadvanced computational genomics. Although they pursue their own interests and goals, where more emphasis is placedon biological importance, they are interested in the research topics of other members and help each other. Thus, we try
to respect each student's research interests and motivation as much as possible when she/he chooses her/his own
research topic.

Imoto , Katayama and Zhang Lab

Professor Seiya Imoto
Associate Professor Kotoe Katayama
Associate Professor Yaozhong Zhang

This is a laboratory of data science in the field of life sciences. We develop statistical data analysis techniques for awide variety of genome-related big data such as whole genome, transcriptome, epigenome, metagenome of intestinal
microflora, and large-scale health and medical data with spatial and temporal axes such as medical images, receipt
information, specific health checkups, wearable devices, and so on. In cooperation with various domestic andinternational companies and researchers in diverse fields, we are promoting social implementation of big data analysis
for health and medicine. Our major research themes are listed below (but not limited):
Research Theme 1: Development of Genomic Data Analysis Methods. Our lab designs and develops machinelearning models that are specifically tailored for genomic data. With the rapid advancement of sequencing
technologies, resulting in an enormous volume of data. This surge in data necessitates highly efficient processing
pipelines. However, the successful application of deep learning models in other fields does not directly translate to
genomics without careful consideration of the unique properties of genomic data. Recently, our focus has been on
foundational models for nucleotide and protein sequences, which includes their training, deployment, and applications.
Additionally, we are involved in the development of machine learning models for the detection of structural variants,
nanopore methylation with long read sequencing, and the representation of cell types with single cell analysis.
Research theme 2: Development of Modeling Technology for Biological Systems. We are developing modelingand simulation technology for biological systems formed by tens of thousands of human and commensal
microorganism derived molecules. We are developing mathematical analysis techniques for predicting the effects ofdrugs and the future progress of diseases.
Research theme 3: Development of Artificial Intelligence for Realizing Genomic Medicine. In the analysis ofcancer genomes, thousands to tens of thousands of genomic mutations are found from the whole genome sequence
data. To identify cancer-causing (pathogenic) genomic mutations from among these, information published as
scientific papers is used. However, the number of papers in the life science field will exceed 36 million now, and it isalready beyond the capability of human intelligence to cover all of them. To solve this problem using artificial
intelligence, we are conducting research involving medical practice at the Institute of Medical Science Hospital.
Our laboratory's web page (https://www.hm-intelligence.com/) introduces recent activities and research topics(including many themes other than those mentioned above). Please take a look if you are interested.

Shibuya Lab

Professor Tetsuo Shibuya

Medical Science is now dramatically changing. It is mainly due to the advent of next generation sequencers that cansequence DNA with incredibly high speed and low cost. State-of-the-art computer science techniques are required todeal with the enormous amount of medical big data. We aim to develop fundamental big data technologies that could
drive medical science to the next stage.
Research topics in our lab:
(1) Big data indexing / searching
Development of faster, more accurate, and scalable indexing/searching technologies are highly desired in the era ofindividual genomic medicine, where we might need to deal with tremendous country-wide individual genomic data.
(2) Privacy preserving computation
Genome is the ultimate individual information to be protected. We need to develop privacy preserving computation
technologies, such as differential privacy and secret computation, to meet ethical requirements from genome data.
(3) Machine learning / AI algorithms for medical science data
Development of accurate prediction algorithms for medical big data is always required.
(4) Application to clinical sequencing
We need to apply our results to actual applied medicine, such as clinical sequencing.
Our theme is not restricted to the above. We think we have infinite possibilities to change the world of medical science,molecular biology, drug discovery, etc., by developing novel big data technologies for them.
Home page: http://shibuyalab.hgc.jp

Kumasaka Lab

Professor Natsuhiko Kumasaka

A genome-wide association study(GWAS) is an incredibly powerful
method for identifying genetic variants
and related genes involved in themolecular mechanisms of common
complex traits, such as diabetes (refer toFig. top panel). Kumasaka Lab is
identifying these genetic associations
and uncovering their molecularmechanisms
using
cutting-edge
molecular biology assays and integrated
mathematical and statistical approaches.
Currently, we have two major projects
underway: (1) discovering the genetic
determinants that influence child healthand development through GWAS of various child traits; and (2) developing a novel in-vitro approach to validate andelucidate the underlying molecular mechanisms of genetic associations discovered through GWAS.
These projects require the use of cutting-edge statistical and machine-learning approaches to analyze large-scale humanomics (genetic, epigenetic and transcriptomic) datasets, consisting of tens of thousands of samples. As an example, we
recently generated neuronal progenitor cells from human iPS/ES cells to investigate neuropsychiatric disorders, such as
schizophrenia (refer to Fig. bottom-left panel). To extract the target cell differentiation trajectory from single-cell RNAseq data, we employed a cutting-edge Gaussian Process Latent Variable Model (GPLVM) (refer to Fig. bottom-middle
panel), which has been actively developed on the SHIROKANE supercomputer at the Human Genome Center (refer toFig. bottom-right panel).
Our mission is to contribute to medical science from a computational perspective by generating and analyzing an
enormous amount of omics data ahead of the world. Visit our website for more details: https://www.ims.utokyo.ac.jp/imsut/en/lab/hgclink/page_00344.html

Aizawa Lab

Professor Akiko Aizawa*Please note that applicants for the Doctoral program who wish to enroll in October 2025 cannot
select Prof. Aizawa as a prospective supervisor*
Our laboratory’s research is focused on natural language technologies to assist human intelligent activities. Our major
challenges include the following subjects in text and media studies that are based on machine learning including deeplearning, statistical modeling and analysis, or annotation and corpus analysis.
(1) Text mining: document structure analysis and information analysis; information identification and entity
disambiguation; automatic construction of linguistic resources(2) Machine reading comprehension: Exploiting the semantic structure of natural language; extracting knowledge
and information from natural language(3) Modeling human language activities: Measuring and analyzing language activities in text media
Our recent research topics are as follows: machine reading comprehension, design and analysis of natural language
understanding tasks, real document processing, retrieval and understanding of mathematical expressions, information
identification and linkage, and recommendation and English writing assistance for academic researchers. We also
welcome new research topic proposals related to natural language processing and information retrieval.
Our research facilities include variety of information resources and a large-scale computation platform at NII. Students
are encouraged to act as independent researchers/engineers by being allowed to join seminars and discussion groups
with interdisciplinary and international researchers and to participate in related joint research activities at NII.
http://www-al.nii.ac.jp/en/

Imari Sato Lab

Professor Imari Sato

Analyzing Light Transport for Scene Understanding
The appearance of an object changes significantly depending
on its shapes, surface reflectance properties, and the lighting
condition of the scene in which it is placed. Given an
unknown picture, it might not so difficult for us to obtain
different kinds of information about the objects in the image.
However, it turns out to be an difficult task for a computer tofigure out. In the past, techniques for automatically modeling Visualization of bacteria growth (right) and classification
and analyzing the photometric and geometric information of a of liquids (middle) by the fluorescence excitationscene have been studied in the fields of both computer vision emission characteristics. Recovering the original surface
color and the degree of wetness (left).
and computer graphics research.
Furthermore, some researcher showed that the spectral reflectance andemission of objects provides innate information about material properties thathave proven useful in applications such as classification, synthetic relighting,and medical imaging to name a few. In our lab, we analyze different types of
optical phenomena including spectral reflectance, absorption, subsurface
scattering, and fluorescence emission for modeling and understanding realscenes.
Blood vessels analysis
by machine learning.

MessageSeeing is believing: the goal of computer vision is to understand how humans process and use visual information
for understanding the surrounding world. There are so much excitement in analyzing real scenes using cuttingedge technologies. Please join us and share a feeling of accomplishment with our lab members!
http://research.nii.ac.jp/pbv/index.html