Start Global Navigation

  1. Home
  2. About RCAST
  3. Research
  4. Industry-Academia-Government Collaboration
  5. International Collaboration

Start Main Contents

Researcher's Profile


Communication Science


Link of outside open a new window Laboratory Homepage

2018 Research book (PDF: 625KB)


1997.04   Electrotechnical Laboratory 
2000.04   Lecturer, Interfaculty Initiative in Information Studies, The University of Tokyo (UTokyo) 
2003.03   Associate Professor, Information Technology Center, UTokyo 
2005.04   Associate Professor, Graduate School of Information Science and Technology, UTokyo 
2012.04   Professor, Graduate School of Information Science and Electrical Engineering, Kyushu
2016.04   Professor, RCAST, UTokyo

Research Interests

-- Mathematical Exploration of Dynamics underlying Symbolic Systems --
Language, Financial Markets, and Communication

Social activities such as communication and financial market interactions are inherently symbolic. We explore the universal properties underlying dynamics of large-scale real symbolic systems through mathematical models derived by computing with big data obtained from large-scale resources. Using these models, we explore new ways of engineering to aid human social activities.

1) Mathematical informatics of language, financial markets, and communication We study universal properties underlying language, finance, and communication, through computing with various kinds of large-scale data, and we apply our understanding of those properties to engineering. In addition to domain-specific themes, we also explore multi-disciplinary targets. For example, we study financial market analysis by using blogs and other information sources, and we simulate information spread on a large-scale communication network.

2) Symbolic systems and deep learning/machine learning methods We discuss the potential and limitations of deep learning and other machine learning techniques with respect to the nature of symbolic dynamics, and we study directions for improvement. Moreover, we explore unsupervised and semi-supervised methods for state-of-the-art learning techniques.

3) Analysis of real symbolic dynamics by applying complex systems theory Common physical scaling properties are known to hold across various symbolic dynamics. Using real, large-scale data, we study these properties and construct a mathematical model that explains them.

-Analysis methods and application based on complex systems theory
-Deep learning methods for engineering time series and networks
-Mathematical modeling of natural language, computational linguistics
-Financial informatics-Mathematical informatics of communication network


Mathematical Linguistics, Natural Language Processing, Semiotics

Start Site Information

page top

Copyright (c) RCAST, The University of Tokyo