[Mathematical Information Platform] Year Started : 2021

Sungrim Seirin-Lee

Creation of systematic mathematical medicine that connects shape and skin diseases

Research Director
Sungrim Seirin-Lee

Professor
Kyoto University Institute for Advanced Study
Kyoto University

Collaborator
Shunsuke Takahagi Associate Professor
Hospital
Hiroshima University
Yuhki Yanase Associate Professor
Graduate School of Biomedical and Health Sciences
Hiroshima University
Outline

Chronic urticaria, which is an intractable skin disease, has no disease animal models so that the pathogenesis of the itself is unknown, and the clinical data to assess its severity are very limited. In this CREST, we solve the problem with the unprecedented idea of mathematically capturing the spatio-temporal changes in the geometric pattern of the eruption that appears on the skin. Through the fusion of mathematical science and information science, we will elucidate the pathophysiology of the occurrence of eruptions and provide new treatments for intractable skin diseases.

Emtiyaz Khan

A new Bayes-Duality principle for adaptive, robust, and life-long learning of AI

Research Director
Emtiyaz Khan

Team Leader
Center for Advanced Intelligence Project
RIKEN

Collaborator
Kennichi Bannai Professor
Faculty of Science and Technology
Keio University
Rio Yokota Associate Professor
Global Scientific Information and Computing Center
Tokyo Institute of Technology
Outline

Our main goal is to develop a new learning paradigm that supports adaptive, robust and life-long learning of AI. We propose to do so by developing a new principle of machine learning, which we call Bayes-Duality. Conceptually, the Bayes-Duality principle hinges on the fundamental idea that an AI should be capable of efficiently preserving and acquiring the relevant knowledge, for a quick adaptation in the future. We apply this theory to representation of the past knowledge, faithful transfer to new situations, and collection of new knowledge whenever necessary. Current DL strategies lack these mechanisms and instead focus on brute-force data collection and training. Bayes-Duality aims to fix these deficiencies.

Tsuyoshi Takagi

Creation and development of mathematical foundations for cryptography required by the post-quantum society

Research Director
Tsuyoshi Takagi

Professor
Graduate School of Information Science and Technology
The University of Tokyo

Collaborator
Noboru Kunihiro Professor
Faculty of Engineering, Information and Systems
University of Tsukuba
Keisuke Tanaka Professor
Graduate School of Information Science and Engineering
Tokyo Institute of Technology
Masato Wakayama Fundamental Mathematics Research Principal
NTT Communication Science Laboratories
Nippon Telegraph and Telephone Corporation
Outline

In this research project, in order to avoid the compromise of cryptography, we consider various possible attackers such as attacks using quantum computers and side-channel attacks by power analysis. We will promote research on mathematical foundations aimed at the realization of cryptographic technology that is resistant to such attacks. Furthermore, we will build a cryptographic system with a decentralized security function using blockchain for large-scale distributed systems.

Kumiko Tanaka-Ishii

Computational Modeling of Natural Language Nonlinearity

Research Director
Kumiko Tanaka-Ishii

Professor
Research Center for Advanced Science and Technology
The University of Tokyo

Collaborator
Koji Mineshima Associate Professor
Faculty of Letters
Keio University
Yusuke Miyao Professor
Graduate School of Information Science and Technology
The University of Tokyo
Outline

Natural language has been known to produce nonlinear behavior across the different scales of corpora, sentence sequences, and individual sentences. In this research project, three groups study the nature of this behavior from the different perspectives of complex systems theory, computational linguistics, and mathematical logic. We describe our findings in the form of computational models that are beneficial for solving natural language engineering problems. By integrating these models, we gain an understanding of the nonlinear nature of natural language.

Nakahiro Yoshida

New developments in statistics for stochastic systems toward data science for large-scale spatiotemporal dependence

Research Director
Nakahiro Yoshida

Professor
Graduate School of Mathematical Sciences
The University of Tokyo

Collaborator
Masayuki Uchida Professor
Graduate School of Engineering Science
Osaka University
Kengo Kamatani Associate Professor
Institute of Statistical Mathematics
Research Organization of Information and Systems
Taiji Suzuki Associate Professor
Graduate School of Information Science and Technology
The University of Tokyo
Hiroki Masuda Professor
Faculty of Mathematics
Kyushu University
Outline

By state-of-the-art mathematical sciences, we create a comprehensive system for statistical modeling and statistical analysis of huge dependent data based on the principles of probability theory and mathematical statistics, and promote research in various fields related to time series data. The fusion of data-driven methods such as machine learning with statistical and simulation techniques for stochastic processes built on rigorous mathematics enables exploration and modeling of dependency that traditional time series analysis could not address, for accurate prediction and stochastic control.

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