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9 labs · 0 student voices
Faculties at Tokyo University of Science ›
Nobuhiko Terui, Professor
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The Terui Lab studies modeling that derives optimal business decisions from diverse, large-scale information. Using Bayesian statistics, machine learning, text analysis, and more, it analyzes data on consumer behavior. In its introduction, the lab invites people to take on new data analysis that makes use of business data, a large part of the big data electronically accumulated in an IoT society. Example themes include statistical modeling of large-scale data, network analysis of social media, and structural modeling of consumer behavior.
Masahiro Nozawa, Professor
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The Nozawa Lab researches how to improve and develop statistical data analysis methods and apply them to real-world problems. When making a decision, the data essential to that decision is rarely fully available. Statistical data analysis is one tool for inferring the whole picture from partial data and making decisions with as few errors as possible. The university's researcher profile lists "variable selection in multivariate analysis" as a research topic.
Shin Ando, Professor
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The Ando Lab researches artificial intelligence and data science, centered on data mining and machine learning. Machine learning is a method of learning, from large amounts of information, knowledge and models that can be used for problem solving. In graduation research projects, students design and improve such methods for practical problem solving and decision-making. Example topics include anomaly detection using large vision-language models, extracting vision-language features from social media posts to predict performance, and visualizing human mobility prediction using Transformers.
Masashi Ieda, Associate Professor
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The Ieda Lab researches how to invest in assets such as stocks and bonds, using control problems for stochastic differential equations. Pension funds, insurance companies, and similar institutions make such investments to make their future payments more secure. The investment models used in practice are complex, and obtaining answers almost always requires a computer, so the lab also focuses on research into numerical computation methods. Example topics include continuous-time asset management strategies, numerical computation for stochastic control, and risk management of derivatives.
Shinichi Tanigawa, Associate Professor
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The Tanigawa Lab builds mathematical models for a wide range of social, economic, and management problems and develops decision-making methods based on optimization techniques. Its specialties are mathematical optimization and operations research. Aiming for methods that are more reliable and reproducible, the lab also studies the mathematics underlying optimization and techniques for designing algorithms. In the university's lab introduction, example topics include discrete optimization such as resource allocation and delivery routing, optimization in data science such as recommender systems, and mathematical analysis of graphs and networks.
Yuri Imamura, Associate Professor
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The Imamura Lab studies markets and financial and insurance products whose futures are uncertain, using mathematical models of randomly evolving movements over time to determine prices, risks, and optimal investment strategies. In particular, it works on methods for computing derivative prices, risk-controlled asset management, and evaluating the soundness of insurance. To handle complex models close to reality, the lab uses numerical computation and also considers how to verify its correctness from the theoretical side. Its specialties are mathematical finance and stochastic analysis, and it lists derivative theory, actuarial science, and stochastic process theory as keywords.
Ryoichi Suzuki, Associate Professor
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The Suzuki Lab studies mathematical finance, which gives problems in finance a mathematical framework and shows how to solve them. To model uncertainty in financial markets, stochastic analysis, the "calculus" of random phenomena, is indispensable. In particular, using Malliavin calculus for stochastic processes with jumps, the lab works on optimal investment theory in incomplete markets, the pricing of financial products, and the term structure of interest rates. The university's researcher information also states that the professor is engaged in financial econometrics and in research on quantum walks and their correspondence with zeta functions.
Rei Noguchi, Associate Professor
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The Noguchi Lab uses data science to discover new knowledge and hidden patterns that people had not noticed. With data, even the assessments and hypotheses that people hold through experience and intuition can sometimes be turned into explicit knowledge, and the factors behind them can be examined. In addition to ordinary numerical data, the lab combines text data such as news articles and word-of-mouth reviews, conducting research that aims to contribute especially to management and economics in healthcare and manufacturing. In the university's lab introduction, example themes include knowledge discovery from word-of-mouth reviews and similar sources through text mining, extraction of insights from big data with machine learning techniques, and realizing effective healthcare management and policies using data science.
Zhao Zhao, Lecturer
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The Zhao Lab uses mathematical science approaches such as mathematical programming and statistical methods to connect data to the solution of real-world problems in a wide range of fields. In today's information society, production and management settings are required to improve operational efficiency by making full use of diverse kinds of information. Because the data used in analysis contain uncertainty such as observation error and noise, the lab also works on statistical data analysis methods that incorporate this stochastic variation. In the university's lab introduction, example themes include solving real-world problems using statistical learning theory, evaluating management efficiency under uncertain environments, and multi-criteria data analysis methods for system evaluation.
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