Labs in Kyushu University, Institute of Mathematics for Industry

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A lab that tackles industrial and social challenges with mathematical technologies and artificial intelligence.

Professor Hirokazu Anai's lab conducts research that tackles challenges facing industry and society using mathematical technologies and artificial intelligence such as machine learning. The lab aims for innovation in the mathematical technologies themselves, which is necessary for solving problems and creating new value. Centered on research into algorithms and applications of computer algebra, the lab has also continued work on automatically solving mathematical problems. The lab aims to discover mathematics within applications, feed it back to mathematics, and create new mathematics.

#Computational algebra #Machine learning #AI for Math

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A mathematics lab studying representations of Lie groups and their branching rules.

Associate Professor Masatoshi Kitagawa studies the representation theory of Lie groups and Lie algebras. The problem of studying how a representation of a group behaves when restricted to a subgroup is called the branching problem. This problem is approached using the structure of the spaces on which representations are realized and the algebras that act on them.

#Branching laws #Representations of Lie groups #D-modules

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A lab researching statistical analysis methods for extracting useful information from large-scale data.

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."

#Sparse estimation #Factor analysis #Multivariate analysis

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A lab exploring the intersection of computational statistics and machine learning.

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.

#Computational statistics #Stochastic optimization #Bayesian inference

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A lab that studies small area estimation, a statistical method for estimating the characteristic values of each small area.

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.

#Statistical science #Small area estimation #Mixed-effects models

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A lab that uses Bayesian statistics to understand physical phenomena from measurement data without ambiguity.

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."

#Bayesian statistics #Uncertainty quantification #Mathematical modeling

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〈科学技術・イノベーション政策〉 (introduction coming soon)

Science, technology, and innovation policy; industry–academia collaboration; standards and certification; promoting applications of the mathematical sciences and building frameworks for industry–academia collaboration

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A lab that uses dynamical systems and verified numerical computation to uncover the singular behavior of differential equations.

The lab of Professor Kaname Matsue studies applied mathematics, grounded in dynamical systems theory, differential equations, and numerical analysis. Its basic spirit is to unravel the fundamental properties of the "strange" or "singular" behavior hidden in phenomena generated by differential equations and the like. With applied mathematics at its core, the lab also tackles problems arising from combustion and related energy issues.

#Finite-time singularities #Verified numerical computation #Dynamical systems

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The lab studies machine learning and explainable AI.

Professor Akihiro Yamaguchi's research themes are machine learning, explainable AI (XAI), interpretability, and time-series data mining. The lab studies methods that make the decisions of AI systems that classify time-series data interpretable to humans. The professor belongs to the Liaison Strategy Division at the Institute of Mathematics for Industry, Kyushu University.

#Explainable AI #Machine learning #Time series data mining

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The lab studies social mathematics, using mathematical optimization and game theory to design social systems and policies.

Associate Professor Akifumi Kira specializes in mathematical optimization and studies problems that require repeated decision-making and problems that involve uncertainty. The lab works on the theory and applications of dynamic programming, a method for solving such problems efficiently. It also works on social mathematics: research that uses mathematical techniques such as optimization and game theory to design fair systems and policies that people find acceptable. The lab has developed technology in collaboration with people on the front lines of challenges such as logistics and the allocation of places at daycare centers.

#Mathematical social science #Dynamic programming #Ride-sharing matching