Department of Statistical Modeling conducts researches on modeling of causally, temporally and/or spatially inter-related complex phenomena, including intelligent information processing system. It also conducts researches on model-based statistical inference methodologies.

Spatial and Time Series Modeling Group works on modeling and inference for statistical analysis of time series, spatial and space-time data, and their applications to prediction and control.

Intelligent Information Processing Group works on concepts and methods for extraction, processing and transformation of information in intelligent systems, motivated by vivid interest in practical problems in engineering and science.

Graph Modeling Group works on analyses of the data generated by a system with graph structure and on modeling in order to reconstruct the original system.

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Department of Data Science aims to develop the research on methods for survey, multidimensional data analyses, and computational statistics.

Survey Research Group focuses on research of statistical data collection and data analyses.

Multidimensional Data Analysis Group works at studying methods for analyzing phenomena grasped on multidimensional space and ways for collecting multidimensional data.

Computational Statistics Group studies sophisticated uses of computers in statistical methodology such as computer-intensive data analyses, computational scientific methods and statistical systems.

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Department of Mathematical Analysis and Statistical Inference aims at advancing research into general statistical theory, statistical learning theory, the theory of optimization, and the practice of statistics in science.

Mathematical Statistics Group is concerned with statistical theory and probability theory that has statistical applications.

Learning and Inference Group develops statistical methodologies that enable researchers to learn from data sets and to properly extract information through appropriate inference procedures.

Computational Mathematics Group studies computational algorithms together with mathematical methodologies for statistical modeling in the sciences.

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Prediction and Knowledge Discovery Research Center studies the statistical modeling and inference algorithms to extract useful information from a huge amount of data which complex systems have produced, and thus attacks to solve real world problems in a wide variety of scientific domains, in particular, genome, earth, and space sciences.

Molecular Evolution Research Group researches in the area of molecular phylogenetics, and works to develop statistical methods for inferring evolutionary trees of life using DNA and protein sequences.

Statistical Seismology Research Group is concerned with evaluation of seismicity anomalies, detection of crustal stress changes, their modeling, and probability forecasting of large aftershocks and earthquakes.

Statistical Genome Diversity Research Group aims to build novel methodologies for learning and inference from a variety of data sets in rapid growing areas of bioinformatics.

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Risk Analysis Research Center is pursuing a scientific approach to the uncertainty and risks in society which have increased with the growing globalization of society and the economy, and also the center is constructing a network for risk analysis with the goal of contributing to create a reliable and safe society.

Food and Drug Safety Research Group aims to develop the statistical framework and methodology of quantitative risk evaluation of a substance ingested by human body.

Environmental Risk Research Group studies the statistical methodologies on an environmental risk or environmental monitoring.

Financial Risk and Insurance Research Group explores statistical modeling methods to gauge the risks involved with financial instruments and insurance products.

The Research Group for Reliability and Quality Assurance of Service and Product aims to increase the safety of products and services by developing statistical methods that contribute to quality assurance and reliability and by promoting the adoption of these methods in the industrial world.

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The objective of this center is to establish innovative research in statistical mathematics to keep up with new trends in the academic and real worlds. The center carries out original research projects, ranging over both pure and applied frontiers.

Functional Analytic Inference Research Group aims to develop nonparametric methodology for statistical inference using reproducing kernel Hilbert spaces given by positive definite kernels, and to apply these techniques to causal inference problems.

The Advanced Monte Carlo Algorithm Research Group aims to develop Markov Chain Monte Carlo and Sequential Monte Carlo algorithms and study their applications.

The Speech and Music Information Research Group investigates novel information retrieval methods using machine learning from time series data, including speech, music, and brain data.

Optimization-based Inference Research Group focuses on optimization methodology as a fundamental tool for computational inference and aims to develop new inference techniques in statistical applications.

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Data assimilation is a mathematical technique used to construct precise and predictable models by integrating numerical simulations and observational/experimental data in various fields of science. Research and Development Center for Data Assimilation develops and upgrades foundations of the data assimilation based on the Bayesian statistics, optimizes numerical algorithm designs to deal with practical problems by utilizing high performance computing, and promotes data assimilation to relevant communities. Key issues associated with the data assimilation are: (1) sequential Monte Carlo methods and nonlinear filtering of ultra-high dimensional data, (2) new algorithms to generate random numbers with ultra-high speed and quality by combining pseudo and physical random numbers, (3) applications to various fields in science such as space, earth, life sciences, (4) next-generation industrial science geared towards highly-accurate simulations and highly-sensitive sensors, (5) high performance computing and statistical calculation services in cloud computing environments within the statistical mathematics community, (6) visualization of observation and simulation data towards comprehensive understanding and new knowledge discovery, and (7) establishment of a cooperative network consisting of institutes and universities associated with simulations.

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The main objective of this center is nationwide networking of social survey researchers and related institutes/universities in order to facilitate social contributions and the utilization of survey research based on the development of Survey Science NOE (Network of Excellence. The main projects of this center are currently as follows.

1. Japanese National Character Survey
2. Cross-National Comparative Survey of National Character
3. Social Survey Informatics
4. Nationwide Networking of Social Survey
5. Utilization of Social Survey Data and Meta-data

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Center for Engineering and Technical Support endeavors development of statistical science by managing computer systems for statistical computing infrastructure, public outreach and supporting research activities of both staffs and collaborators.

The Computing Facilities Unit is in charge of managing computer facilities, software for research, networking infrastructure and network security.

The Information Resources Unit is responsible for maintaining an extensive library and an electronic repository, and is in charge of planning statistical education courses to popularize research results.

The Media Development Unit is in charge of the publication and editing of research results and is responsible for public relations.

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