Advisor's Education Content
★Please see
https://soken.cloudsyllabus.com/
Photo/Name 
Guidance  Subjects 

Yukito Iba
（Prof.） iba[at]ism.ac.jp 
I am mostly interested in computational methods for highly multivariate, nonGaussian (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 
Genta Ueno
（Prof.） gen[at]ism.ac.jp 
I give students guidance in their studies of methodologies and applications of data assimilation. Data assimilation is a method of timeseries analysis for largescale 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 timeseries 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 
Yoshinori Kawasaki
（Prof.） kawasaki[at]ism.ac.jp 
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 
Tomoko Matsui
（Prof.） tmatsui[at]ism.ac.jp 
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 realworld problems with big data will serve as examples. (1) speech and speaker recognition (2) multimedia data classification 
Communication Information Processing Multimedia Information Processing 
Yoshihiko Miyasato
（Prof.） miyasato[at]ism.ac.jp 
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 Hinfinity control), and related system identification methodology (subspace method, recursive estimation method, and closedloop identification). Control Theory II is based on preceding Control Theory I. 
Control Theory Ⅰ Control Theory Ⅱ 
Atsushi Yoshimoto
（Prof.） yoshimoa[at]ism.ac.jp 
Teaching and advising focuses on statistical and mathematical modeling for predicting and controlling natural and socioeconomic resource change within the deterministic and stochastic frameworks. Through field survey, research study will be conducted on sustainable resource management as a socioeconomic system along with risk evaluation and economic analysis.  Applied Probability Ⅰ Applied Probability Ⅱ 
Shinsuke Koyama
（Assoc.Prof.） skoyama[at]ism.ac.jp 
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
（Assoc.Prof.） zhuangjc[at]ism.ac.jp 
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
（Assoc.Prof.） takizawa[at]ism.ac.jp 
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
（Assoc.Prof.） shiny[at]ism.ac.jp 
My research interests are in analyses of spatiotemporal 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 spatiotemporal data based on the state space model and statistical modelling for data assimilation and analyses of various spatiotemoral data are also covered in the scope of my interest. Students who are interested in these research subjects are welcome.  Statistical Computing Spatiotemporal Data Analysis 
Hideitsu Hino
（Assoc.Prof.） hino[at]ism.ac.jp 
My major is mathematical engineering, which is a kind of research attitude in which one try to find mathematical structures in engineering problems and develop methods required for engineering problems. I am particularly interested in machine learning and data analysis. My motivation of research is in understanding why certain algorithms work well. There are mainly two approaches to my problem. 1) Be committed to mathematical novelty: I find great value in novelty of problem setting, viewpoint, and mathematical structure in the problem and the algorithm. 2) Be committed to practicality: Choose an appropriate method that has properties required in particular application. It can be computational efficiency, interpretability and robustness. If there is no existing method fulfilling the requirement, we will develop new method. These two approaches are not necessarily conflicting, but in many cases you need to switch heads. I will teach and study with the aim of becoming able to formulate and solve the problem with one of the above explained approaches. 
Special Topics in Statistical Modeling Ⅰ Special Topics in Statistical Modeling Ⅱ 
Kazuhiro Minami
（Assoc.Prof.） kminami[at]ism.ac.jp 
This course covers information security, particulary privacypreserving techniques managing big data. We focus on recent research in the following topics: (1) Privacypreserving data mining (2) Privacypreserving data publishing including annoymization techniques (3) Access control 
Information Security Ⅰ Information Security Ⅱ 
Fumikazu Miwakeichi
（Assoc.Prof.） miwake1[at]ism.ac.jp 
The main area of research intersts are spatiotemporal 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.  Complex Systems Analysis Ⅰ Complex Systems Analysis Ⅱ 
【Assistant Professor】 Shunichi Nomura ／ Momoko Hayamizu

Photo/Name 
Guidance  Subjects 

Yoichi M. ITO
（Prof.） itoym[at]ism.ac.jp 
The aim of this course is to study on the theory and applications of medical statistics. The main fields of application are clinical trials and clinical research of pharmaceutical and medical products, specifically, the methodology of clinical trials in translational research, safety evaluations in the post marketing surveillance, and modeling of the normal tissue complication probability in the radiation therapy. We provide education and research guidance on methodologies related to the development of these pharmaceutical and medical products and evaluation of therapeutic methods.  Medical statistics Ⅰ Medical statistics Ⅱ 
Koji Kanefuji
（Prof.） kanefuji[at]ism.ac.jp 
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 
Biostatistics Environmental Statistics 
Satoshi Yamashita
（Prof.） yamasita[at]ism.ac.jp 
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 
Ryo Yoshida
（Prof.） yoshidar[at]ism.ac.jp 
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 
Jun Adachi
（Assoc.Prof.） adachi[at]ism.ac.jp 
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
（Assoc.Prof.） shimatan[at]ism.ac.jp 
How to construct spatiotemporal models when spatiotemporal 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 goodnessoffit ? Primarily, spatiotemporal field data about plants and animals are analyzed.  Spatial Statistics Stochastic Geometry 
Hisashi Noma
（Assoc.Prof.） noma[at]ism.ac.jp 
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 largescale genomic data, etc. 
Special Topics in Biostatistics Applied Statistics Ⅰ 
Yoosung Park
（Assoc.Prof.） parkys[at]ism.ac.jp 
Practical methodologies related to social surveys on individuals and small groups in organizations or communities. (1) Surveys and statistical analyses on organizational behavior (2) Empirical research and applications for municipality residents survey (3) Experiments and techniques for improving mail survey methodology 
Special Topics in Survey Data Analysis Ⅱ Survey Design 
Ikuko Funatogawa
（Assoc.Prof.） funato[at]ism.ac.jp 
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 
Tadahiko Maeda
（Assoc.Prof.） maeda[at]ism.ac.jp 
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 
【Assistant Professor】 Wu Stephen ／ Nobuo Shimizu ／ Daisuke Murakami

Photo/Name 
Guidance  Subjects 

Shiro Ikeda
（Prof.） shiro[at]ism.ac.jp 
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
（Prof.） sito[at]ism.ac.jp 
We will study optimization theory and its applications. Specific topics include (1) infinitedimensional optimization and related functional analysis; (2) robust optimization, semiinfinite programming and systems design under uncertainty; (3) semidefinite programming and secondorder cone programming; (4) realworld applications of optimization in industry  Systems Optimization Ⅰ Systems Optimization Ⅱ 
Shinto Eguchi
（Prof.） eguchi[at]ism.ac.jp 
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
（Prof.） kuriki[at]ism.ac.jp 
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., integraldifferential geometric approach, algebraic methods, discretecombinatorial mathematics) in statistical science. (4) Genetic statistics, spatial epidemiology, paired comparisons and network analysis, etc. 
Multivariate Statistical Inference Ⅰ Multivariate Statistical Inference Ⅱ 
Yoshiyuki Ninomiya
（Prof.） ninomiya[at]ism.ac.jp 
In this course, we will discuss about irregular statistical models in which conventional statistical asymptotic theory does not hold. Specifically, about (1) changepoint models in which the likelihood cannot be differentiated (2) models with socalled nonidentifiability such as a signal model, a mixture model and a factor model we will treat the asymptotic behavior of the likelihood ratio and model selection based on it. 
Special Topics in Statistical Asymptotic Theory ChangePoint Analysis 
Kenji Fukumizu
（Prof.） fukumizu[at]ism.ac.jp 
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
（Prof.） fujisawa[at]ism.ac.jp 
This course focuses on statistical inference, statistical machine learning, and related data analyses. Topic: Robust Statistics. Divergence. Sparse Modeling. Graphical Modeling. Asymmetry Distribution. Model Selection. Mixed Effects Models. Missing Data Analysis. Multiple Testing. Anomaly Detection. Data: Medical Data. Industrial Data. Genome Data. 
Theory of Statistical Inference Special Topics in Data Analysis Ⅰ 
Shogo Kato
（Assoc.Prof.） skato[at]ism.ac.jp 
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 
Takaaki Shimura
（Assoc.Prof.） shimura[at]ism.ac.jp 
Probability theory is basic mathematics for statistical science. As statistical science develops, higher mathematics is required. I deal with several mathematical topics for statistical science: (1) Infinitely divisible distributions and processes. This distribution and process class is an important generalization of Gaussian and Poisson distributions and processes. (2) Extreme value theory and its applications. Extreme value or order statistics is interesting from both theoretical and practical points of view. These are used for mathematical modelling in financial engineering, insurance, natural disasters, risk control and so on. 
Probability theory and its applications Ⅰ Probability theory and its applications Ⅱ 
Masayuki Henmi
（Assoc.Prof.） henmi[at]ism.ac.jp 
In this course, we mainly do research on modern statistical methods of biostatistics. More concretely, this includes missingdata 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 
Shuhei Mano
（Assoc.Prof.） smano[at]ism.ac.jp 
In this course, we explore models for data generated by complex stochastic mechanisms and mathematical methodologies for analyzing data based on the models. Especially, we study the following topics. (1) discrete stochastic models (2) algebraic algorithms for sampling (3) applications to Bayesian data analysis 
Stochastic Models Special Topics in Data Analysis Ⅱ 
Daichi Mochihashi
（Assoc.Prof.） daichi[at]ism.ac.jp 
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 largescale Bayesian inference. 
Statistical Natural Language Processing Bayesian Modeling and Sequential Monte Carlo Methods 
【Assistant Professor】 Masaaki Imaizumi ／ Ayaka Sakata ／ Mirai Tanaka
