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8 labs · 0 student voices
Other departments in Institute of Mathematics for Industry ›Faculties at Kyushu University ›
Kei Hirose, Professor
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Kei Hirose's lab conducts research and development on new statistical analysis methods, based on mathematical statistics. Analyzing large-scale data such as genetic data requires statistical models with tens of thousands to hundreds of millions of parameters. The lab develops new sparse estimation methods, which extract useful information by estimating most of a huge number of parameters as zero, within multivariate analysis such as factor analysis and graphical models. The lab's website introduces it under the name "Kei Hirose Lab."
Hien Duy Nguyen
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Professor Hien Duy Nguyen, who holds concurrent faculty appointments at Kyushu University and La Trobe University in Australia, studies computational statistics and its connections to machine learning and AI. The work centers on analyzing data that accumulate gradually and data too large for ordinary computers to handle, addressing these difficulties with methods such as stochastic optimization and Bayesian inference. Professor Nguyen is also interested in tools for model selection when data are complex, methods for testing the validity of that selection, and ways of showing the quality of a decision together with a measure of confidence. Professor Nguyen's methods are used in applied research in neuroimaging, agriculture, genetics and genomics, quantum computation, economics, and other areas.
Masayo Hirose, Associate Professor
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Associate Professor Masayo Hirose studies small area estimation, a statistical method that estimates the characteristic values of each small area from survey data. The lab works on both the theory and the applications of methods that can improve the accuracy of estimation through statistical models. The lab believes that using such methods to produce high-quality data and connecting it to useful planning can strengthen expectations for solving social issues. The research keywords are statistical science, small area estimation, and mixed-effects models.
Satoru Tokuda, Associate Professor
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Tokuda Lab works to establish guiding principles for understanding all kinds of physical phenomena unambiguously, based on data. Combining mathematical modeling with statistical inference, the lab studies methods for comparing measurement data against models and selecting the model that best fits reality. The foundation is Bayesian statistics, and the lab also conducts joint research with researchers in various sciences and in industry. On the lab's website, it is introduced under the name "Tokuda Lab."
Kei Hirose, Professor
0 student voices
The Hirose Lab researches and develops new statistical analysis methods based on mathematical statistics. It also incorporates ideas from methods used in machine learning. Analyzing large-scale data such as genetic data requires statistical models with tens of thousands to hundreds of millions of parameters. The lab is therefore working on new methods of sparse estimation, which extract useful information by estimating most of a large number of parameters as exactly zero.
Hien Duy Nguyen, Professor
0 student voices
Professor Hien Duy Nguyen's lab studies computational statistics, which analyzes large volumes of data that accumulate gradually and data too large for ordinary computers to handle. It uses methods such as stochastic optimization and Bayesian inference to deal with the difficulties that come with such data. The lab is also interested in statistical tools for model selection when data are complex, methods for verifying whether that selection is valid, and ways of expressing the confidence that shows how reliable an inference is. The professor also concurrently serves as an Associate Professor at La Trobe University in Australia, and is responsible for building collaboration between the two universities.
Masayo Hirose, Associate Professor
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Associate Professor Masayo Hirose's lab studies statistical small area estimation, which estimates characteristic values for each small subdivision from survey data. The lab studies, from both theoretical and applied perspectives, estimation methods that use statistical models to help improve statistical accuracy. In recent years, the importance of EBPM has been recognized, and expectations are also growing for planning based on an understanding of actual conditions in each subdivision. The research keywords are statistical science, small area estimation, and mixed-effects models.
Satoru Tokuda, Associate Professor
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The Tokuda Lab aims to establish guiding principles for understanding physical phenomena from measurement data without ambiguity. The lab combines mathematical modeling, which represents data with simple equations, and statistical inference, which selects models in light of the data. While exploring the mathematics of Bayesian statistics as the foundation, the lab also conducts joint research with researchers in various sciences and in industry. The lab is recruiting fourth-year students from the Department of Mathematics, Faculty of Science who wish to join, as well as incoming graduate students of the Graduate School of Mathematics and the Joint Graduate School of Mathematics for Innovation.
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