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Course Introduction

Fundamental Data Science Course

Diversification and complication of information are creating problems that cannot be solved by any single existing academic field. In response to that, this course at the forefront of data science studies formulates a new methodology of data science dealing with culture, beyond the frameworks of mathematics, information and statistics and based on the knowledge accumulated in the respective fields.

Faculty Members

HATANO Kenji

HATANO Kenji

Professor

Research Field
data engineering, Internet information science, library and information science

Research Topic
Development of technology for effective storage, search and use of big data

Today’s advanced information society is not only seeing overflow of data but also its complexity. People solve various problems by collecting information from such data and acquiring new knowledge through using them. I teach students the skill to store, search and use appropriate data for solving the problems they deal with.

KAWASAKI Kohkichi

KAWASAKI Kohkichi

Professor

Research Field
mathematical biology

Research Topic
Method of numerical analyses of mathematical models

I conduct research on numerical analyses of differential equations describing cultural phenomena. I also work on the modeling of evolution of cooperation in the prisoner’s dilemma game on lattice space using the game theory and its computer simulation analysis. I teach students various types of mathematical modeling and simulation modeling concerning culture and society.

URABE Jiichiro

URABE Jiichiro

Professor

Research Field
partial differential equation (theory)

Research Topic
Study of direct and inverse problems of differential equation

I examine the structures of cultural and social phenomena by applying direct and inverse problems of differential equation to describing them. I encourage students to find rules inherent in various cultural and social phenomena and review the data science method and the mathematical analytical way of thinking to approach its mechanism, in addition to teaching them these mathematical foundations.

YADOHISA Hiroshi

YADOHISA Hiroshi

Professor

Research Field
statistical science

Research Topic
Theoretical characterization of multivariate data analysis methods and development of a new method

My research interests include multivariate data analysis methods, social network analysis, sports data analysis, matrix decomposition-type multivariate analysis methods and symbolic data analysis methods. I teach students the theory and application of various methods of multivariate data analyses and provide them with the practical problem-solving skill using statistical science.

HARA Hisayuki

HARA Hisayuki

Associate Professor

Research Field
mathematical statistics, econometrics

Research Topic
Development of statistical theory in multivariate models and its application to practical problems.

My current research interest is theory of statistical inference of network structures in high-dimensional models, such as graphical models, social network models and structural equation models. Recently I am especially interested in inference of causal structure of social phenomena. Further, I also work on the application of these theories to practical problems.

FUKAGAWA Daiji

FUKAGAWA Daiji

Assistant Professor

Research Field
algorithms

Research Topic
Theoretical analyses of algorithms and their practical implementation.

My research interests focus on the theory of algorithms and computational complexity of mathematically formalized problems, in particular, combinatorial optimization problems such as tree pattern matching and graph data mining. Currently I am also interested in how to easily integrate such theoretical formalization, or models, with empirical knowledge and techniques. I teach students how to evaluate, improve, and renovate algorithms and how to realize them into a running software.

TAMATANI Mitsuru

TAMATANI Mitsuru

Assistant Professor

Research Field
statistical asymptotic theory, mathematical statistics

Research Topic
Statistical methods for high dimension low sample size data analysis

In high dimension low sample size data situations, where the dimension is much larger than the sample size, I study the asymptotic behavior of discriminant vectors and misclassification rate. I also propose new feature selection methods specifically for multi-class discriminant function. To analyze for practical data, I provide students with the fundamental knowledge of mathematics, probability and statistics.

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