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Statistical Modeling

Photo/Name Guidance Subjects
Yukito Iba
I am mostly interested in computational methods for highly multivariate, non-Gaussian (or discrete) probability distributions, as well as their applications to statistics, statistical physics, engineering,, machine learning, and combinatorics. Specifically, various applications of Markov chain Monte Carlo and sequential Monte Carlo, and methods for fusion of statistics and simulation science are fields of current interest.

Monte Carlo algorithms and stochastic simulation

Modeling of complex hierarchical structures

Yoshinori Kawasaki
Students will be guided to learn various time series models useful to financial econometrics. Possible topics are unit root, cointegration, conditional heteroscedasticity, Markov switching, conditional duration and so on. Also instructions will be given for statistical aspects of time series models with unobservable components in connection with pricing and prediction of financial assets.

Special Topics in Time Series Analysis Ⅰ

Special Topics in Time Series Analysis Ⅱ

Junji Nakano
We have had several good statistitical software products already. However, according to the dissemination of stataistics and computer in various fields, we still need a statistical analysis environment which is easier to use and more sophisticated than the existing ones. We study and teach techniques to realize and utilize such systems, especially graphical user interface, Internet usage, distributed computing and data visualization.

Computational Statistics Model

Statistical Computing Ⅰ

Tomoko Matsui
The main goal of this course is to introduce the students to influential developments in modern machine learning for communication and multimedia information processing, namely kernel machine,hidden Markov models and graphical models. The following applications to real-world problems with
big data will serve as examples.
(1) speech and speaker recognition
(2) multimedia data classification

Communication Information Processing

Multimedia Information Processing

Genta Ueno
I give students guidance in their studies of methodologies and applications of data assimilation. Data assimilation is a method of time-series analysis for large-scale data set on the basis of a numerical model that simulates the time evolution of the states. Combination of the simulation model and the data set enables us to predict and estimate the states more accurately than with the data or the model alone. Keeping realistic applications in mind, I give guidance in methods of time-series analysis, statistical models, implementation with parallel computing, and methods for analyzing the resultant estimates.

Special Course on Data Assimilation Ⅰ

Special Course on Data Assimilation Ⅱ

Shinsuke Koyama
This course focuses on the stochastic methods for modeling and analyzing complex phenomena observed in natural and social systems.
(1) Analysis of complex networks.
(2) Basic theory of stochastic processes and its applications.

Stochastic systems Ⅰ

Stochastic Modeling Ⅱ

 Zhuang, Jiancang
Theories and application techniques related to stochastic process. In details, the following subjects are focused on: 1) Fundamental theory of point processes, 2) Statistical modeling and forecasts of discrete events in space and time, and 3) Statistical seismology.

Basic theory of Point Processes

Statistical Inferences for Point Processes

Yumi Takizawa
Many of data and signals in nature world and social activity are diverse and complex. To create effective system for these analysis and computation, The basis of digital signal processing will be lectured. Next, the basis of information theory by C. Shannon will be lectured.
Furthermore, neural systems in brain of animal including human are studied as higher function and flexible system of small power consumption. the goal of these studies are to establish a novel methodology for computation, communication, and mesurement.

Digital Signal Processing

Communication and Information Systems

Shin'ya Nakano
My research interests are in analyses of spatio-temporal data and their applications to estimation and prediction of a system. In particular, the main subject is data assimilation, which exploits our knowledge on system dynamics for the estimation. Analyses of spatio-temporal data based on the state space model and statistical modelling for data assimilation and analyses of various spatio-temoral data are also covered in the scope of my interest. Students who are interested in these research subjects are welcome.

Statistical Computing Ⅱ

Spatio-temporal Data Analysis

Kazuhiro Minami
This course covers information security, particulary privacy-preserving techniques managing big data. We focus on recent research in the following topics:  
(1) Privacy-preserving data mining
(2) Privacy-preserving data publishing including annoymization techniques
(3) Access control

Information Security Ⅰ

Information Security Ⅱ

Fumikazu Miwakeich
The main area of research intersts are spatio-temporal analysis and its application to biological signal. Emphasis is on the numerical algorithms of statistical time series mathematical analysis and the development of new algorithms to detect biological signai in time and frequency domain, causality between regions.

Functional Information Theory

Inductive Information Theory

Ryo Yoshida
This class conducts teaching and research guidance about applied statistics on bioinformatics, chemical informatics and systems biology. Related topics involve machine learning, Bayesian statistics and computation, data assimilation, kernel methods for structured data such as molecules and strings, and so on. For students who are concerned with the practice in a specific problem of biosciences, this class provides technical guidance to modeling, computation and programming.

Biological System Analysis Ⅰ

Biological System Analysis Ⅱ

【Assistant Professor】 Ayaka Sakata  / WU STEPHEN  / Momoko Hayamizu

Data Science

Photo/Name Guidance Subjects
Koji Kanefuji
This course is intended to follow on from statistical inference by providing a more specical topics of the applied fields such as biomertics and environmetrics. The focus will be on developing a deeper theoretical understanding of some of the important statistical methods.
(1)Statistical methods for survival data analysis
(2)Analysis of longitudinal data


Environmental Statistics

Yoshiyasu Tamura
Following research advice about theories, methods and applications of time series analysis and computational statistics will be available.
(1) Parallel computations in order to conduct time series analysis
(2) Monte Carlo simulations with hardware random number generators
(3) Applications of nonlinear time series analysis for time series data in the fields of finance, bioscience and so on

Applied Time Series Analysis Ⅰ

Applied Time Series Analysis Ⅱ

Satoshi Yamashita
We focus on the risk validation and the risk control systems of economics, finance, transportation technology and other social science.
Topics in recent years are,
1. Expected loss and recovery rate of credit risk, 2. Handling missing data and outlier data of big financial database, 3. Trip chain model for sightseen behavior.

Financial Statistics Ⅰ

Financial Statistics Ⅱ

Ryozo Yoshino
Under the paradigm of "Data Science", the lecture focuses on the statistical methodologies of cross-national social survey, including questionnaire making,, practical random sampling scheme, data cleaning technique, data analysis, and writing of report in practical survey research. Practical statistics is emphasized, rather than a theory on desk.

On Cross-National Comparability of National Character Ⅰ

On Cross-National Comparability of National Character Ⅱ

Jun Adachi
In this course, students will learn about modeling of molecular evolution and estimating of phylogenetic trees.
Specific content is as follows:
major transitions of evolution;
genomic evolution;
comparison of DNA sequences to calculate genetic distances;
phylogeny reconstruction by distance, likelihood and Bayesian methods;
test of the molecular clock and dating speciation events;
mechanisms of molecular evolution and the neutral theory.

Genomic Data Analysis Ⅰ

Genomic Data Analysis Ⅱ

Kenichiro Shimatani
How to construct spatio-temporal models when spatio-temporal data are given? How to formulate system and observation models by mathematical equations and probability distributions? How to estimate unknown parameters and how to check the goodness-of-fit ? Primarily, spatio-temporal field data about plants and animals are analyzed.

Spatial Statistics

Stochastic Geometry

Hisashi Noma
Methodology and application of biostatistics.
(1) Designs and analyses of clinical and epidemiological researches
(2) Evidence synthesis methods
(3) Prevention and analyses of missing data in medical studies
(4) Analyses of large-scale genomic data, etc.

Special Topics in Biostatistics

Applied Statistics Ⅰ

Ikuko Funatogawa
The aim of this course is to study the statistics in medicine and public health focusing on statistical models such as linear mixed effects models in longitudinal data analysis,  the design such as randomization, and also statistics in actual health problems such as obesity and smoking.

Statistics in Medicine Ⅰ

Statistics in Medicine Ⅱ

Masayuki Henmi
In this course, we mainly do research on modern statistical methods of biostatistics. More concretely, this includes missing-data analysis, statistical causal inference, semiparametric inference, meta analysis and so on. These are used in other areas such as social science, and it is expected that the students have interest in such aspect and will have a wider view through statistical methodology.

Topics of Statistical Inference Ⅰ

Topics of Statistical Inference Ⅱ

Tadahiko Maeda
Taking social survey as a typical example, process from collection of data to statistical analysis of them will be studied. As for data collection, we will review theory and practice of survey sampling and some aspects of survey operation. As for data analysis, we will study on multivariate data analysis including latent variable models. For comparison, we will also take a short look at design of data collection and analysis in the fields other than social survey.

Topics in Sampling Theory Ⅰ

Topics in Social Research

Naomasa Maruyama
Mathematical foundation for lattice packing, coding theory, and arrangement of subspaces. The course will also explore finite geometry and algebraic geometric codes in relation to these topics.

Statistical Computing Ⅲ

Statistical Computing Ⅳ

【Assistant Professor】  Nobuo Shimizu  / Yoosung Park  / Masayo Hirose  / Shunichi Nomura

  Mathematical Analysis and Statistical Inference

Photo/Name Guidance Subjects
Shiro Ikeda
Recently, many fields including statistics, learning theory, statistical physics, and coding theory are developing together as they share similar ideas from Bayesian statistics and optimization theory. My main interest is to understand these ideas from information geometry.

Special Topics in Signal Processing Ⅰ

Special Topics in Signal Processing Ⅱ

Satoshi Ito
We will study optimization theory and its applications. Specific topics include (1) infinite-dimensional optimization and related functional analysis; (2) robust optimization, semi-infinite programming and systems design under uncertainty; (3) semidefinite programming and second-order cone programming; (4) real-world applications of optimization in industry

Systems Optimization Ⅰ

Systems Optimization Ⅱ

Shinto Eguchi
The class focuses on inference and learning for data, in which the goal is to study statistical thinkings with various scientific backgounds. For example, specific themes are statistical inference for risk assessments, causal inference and observational biases, data analysis for genome diversity, robust statistics and information divergence geometry, pattern recognition in machine learning, independent and pricipal component analysis.

Statistical Learning Theory Ⅰ

Information Geometry

Satoshi Kuriki
Theory and application of mathematical statistics.
(1) Statistical inferences and numerical algorithms in multivariate analysis (including
continuous multivariate analysis, contingency table, and graphical models).
(2) Distribution theory for multivariate random variables and random fields.
(3) Mathematical approaches (e.g., integral-differential geometric approach, algebraic methods, discrete-combinatorial mathematics) in statistical science.
(4) Genetic statistics, spatial epidemiology, paired comparisons and network analysis, etc.

Multivariate Statistical Inference Ⅰ

Multivariate Statistical Inference Ⅱ

Akiko Takeda
We will work on modelling optimization problems and devising algorithms to solve those problems.
More specifically, we will study
(1) devising efficient algorithms for solving problems inherently too difficult to solve exactly (e.g., nonconvex problems);
(2) applying optimization methods to practical problems in various research areas; machine learning and energy systems.

Optimization Algorithm Ⅰ

Optimization Algorithm Ⅱ

Kenji Fukumizu
This course discusses theoretical and practical aspects of statistical learning theory and mathematical statistics, aiming at directing research projects of students. Examples of the topics include (1) Kernel methods with positive definite kernels and reproducing kernel Hilbert spaces, (2) Applications of kernel methods to causal inference, (3) Statistical analysis of structured data such as trees and graphs, and its applications to systems biology, (4) Statistical inference with advanced optimization methods. The course focuses new methodologies from broad mathematical viewpoints such as function analysis, geometry, and algebra.

Statistical Machine Learning

Statistical Learning Theory Ⅱ

Hironori Fujisawa
In this course, we study statistical inference theory and data analysis simultaneously. The concrete topics are, e.g., robust statistics, model selection, skew distribution, divergence, gene expression data, and so on.

Theory of Statistical Inference

Special Topics in Data Analysis Ⅰ

Yoshihiko Miyasato
Research instructions are performed on the following main themes; Control Theory I and Control Theory II.
Control Theory I :
Control Theory I provides basic preliminaries in the field of control theory, such as state space representation, controllability and observability, canonical form, state feedback and optimal LQ control, state observer and Kalman filter, and servo control based on internal model principle.
Control Theory II :
Control Theory II focuses on several recent topics in the field of advanced control theory, such as adaptive control (model reference adaptive control and self tuning controller), nonlinear control (exact linearization and backstepping), robust control (robust analysis and H-infinity control), and related system identification methodology (subspace method, recursive estimation method, and closed-loop identification). Control Theory II is based on preceding Control Theory I.

Control Theory Ⅰ

Control Theory Ⅱ

Atsushi Yoshimoto
Teaching and advising focuses on statistical and mathematical modeling for predicting and controlling natural and socio-economic resource change within the deterministic and stochastic frameworks. Through field survey, research study will be conducted on sustainable resource management as a socio-economic system along with risk evaluation and economic analysis.

Applied Probability Ⅰ

Applied Probability Ⅱ

Shogo Kato
This course discusses theory and application of mathematical statistics. Examples of the topics discussed in the course include: (1) statistical models for data which include angular observations, (2) copulas, and (3) theory of probability distributions and its statistical application. The goal of this course is to develop modern statistical techniques and consider their applications.

Regression Analysis

Distribution Theory 

Shuhei Mano
Studying stochastic models for various data which are generated by complex stochastic mechanisms and methodologies for analyzing data by using these models.

Special Topics in Data Analysis Ⅱ

Stochastic Models

Daichi Mochihashi
Theory and practice of statistical natural language processing and machine learning.
Especially, I will focus on
(1) Statistical topic models
(2) More advanced models for statistical natural language processing
(3) Statistical techniques of large-scale Bayesian inference.

Statistical Natural Language Processing

Bayesian Modeling and Sequential Monte Carlo Methods

【Assistant Professor】  Takaaki Shimura  /Teppei Ogihara  /Mirai Tanaka