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Mathematical Systems Science Lab [Graduate School of Information Science and Technology]
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A lab that tackles system optimization and data analysis with mathematical methods.
AboutAbout the lab
Solving real-world problems through systematization technologies
The Mathematical Systems Science Lab studies systematization and its applications using mathematical science approaches such as mathematical programming and statistical methods.
Through modeling of corporate production activities and of social and natural phenomena, and through system analysis based on the science of uncertainty, the lab aims to cultivate systematization skills.
The lab belongs to the Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University, and undergraduate students from the School of Engineering's Department of Applied Science also join as members.
The lab welcomes applicants from other universities and other faculties, and accepts lab visits at any time.
Key terms
- Mathematical programming
- A mathematical method for finding the choice that best meets an objective under given conditions
- Combinatorial optimization
- The problem of finding the best option that satisfies the conditions among a vast number of combinations of choices, and the methods for solving it
- Data envelopment analysis (DEA)
- A method that compares and evaluates the productivity of activities with multiple inputs and outputs, based on data
ResearchResearch themes
The lab works on theoretical advances in optimization methods and data analysis methods. Through modeling of corporate production activities and of social and natural phenomena, it aims to cultivate systematization techniques.
Mathematical programming for system optimization
The lab studies the theory and algorithms of mathematical optimization for system analysis. Its work includes developing and generalizing methods in graph theory, discrete mathematics, and combinatorial optimization, as well as black-box function optimization under uncertainty.
*Illustrative imageData analysis and evaluation methods
Variability and uncertainty are factors to be considered in decision-making. The lab also studies behavior analysis and demand forecasting using machine learning and artificial intelligence techniques. For evaluation under diverse value criteria, it also works on productivity evaluation by data envelopment analysis (DEA).
*Illustrative imageModeling and applications of optimization methods
The lab aims to analyze and improve familiar systems such as production systems and social systems. It systematizes them with mathematical models, pursues efficiency and optimization using mathematical programming, statistical methods, and machine learning, and also tackles real-world problems through industry-academia collaboration.
*Illustrative imageTime-series data and mathematical models
The mathematical models behind time-series data are often described by differential equations or difference equations. Besides research on estimating model equations from data, the lab uses dynamical systems theory and numerical analysis to evaluate the estimated equations.
MembersFaculty
The members of the Mathematical Systems Science Lab consist of faculty, administrative assistants, and doctoral, master's, and undergraduate students.As of 2026
- Associate ProfessorYutaro Yamaguchi
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Frank-Wolfe Algorithm” Stephanie Stutz / CC BY 4.0 (cropped)
- Data analysis and evaluation methods: “Concept of machine learning” Ultrabem / CC BY-SA 4.0 (cropped)
- Modeling and applications of optimization methods: “Model of subsea production systems” jasonwoodhead23 / CC BY 2.0 (cropped)
- Time-series data and mathematical models: “EAI680 differential equation display” Autopilot / CC BY-SA 4.0 (cropped)
- Related lab (Mathematical Planning Lab [Graduate School of Information Science and Technology]): “Hellenic Autonomous Vehicle (Dashboard)” Electrical Eng25 / CC BY 4.0 (cropped)
- Related lab (Information Photonics Lab): “Bose-Einstein condensate, a cloud of super-cooled Rubidium atoms in a Laser trap on an Optical table - In a game of catch & release, the atoms will shoot up a 30 ft. tall Atomic fountain - Stanford” Steve Jurvetson from Menlo Park, USA / CC BY 2.0 (cropped)
- Related lab (Nonlinear Mathematics Lab [Graduate School of Information Science and Technology]): “Lorenz Attractor from Gauss-Legendre” Mgolden96 / CC BY-SA 4.0 (cropped)
- Related lab (Bioprocess Systems Engineering Lab): “Line of culture flask ( )” Jean-Etienne Minh-Duy Poirrier from USA / CC BY-SA 2.0 (cropped)
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