2. Research
  3. Researcher's Profile
  4. Kumiko TANAKA-ISHII

Researcher's Profile

  • Professor


April 1997 Electrotechnical Laboratory
April 2000 Lecturer, Interfaculty Initiative in Information Studies, The University of Tokyo (UTokyo)
March 2003 Associate Professor, Information Technology Center, UTokyo
April 2005 Associate Professor, Graduate School of Information Science and Technology, UTokyo
April 2012 Professor, Graduate School of Information Science and Electrical Engineering, Kyushu University
April 2016 Professor, RCAST, UTokyo (-March 2023)

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

Related Articles

    page top