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Mathematical Data Science Lab
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The lab solves problems in design and data using the theory of mathematical optimization and statistics.
AboutAbout the lab
The mathematics of optimization and statistics, from theory to application.
The Mathematical Data Science Lab studies the theory of mathematical optimization and mathematical statistics, as well as their applications.
In optimization, the lab applies theories such as convex optimization and duality to problems including structural design and contact mechanics.
In statistics, the lab works on computational algebraic statistics and on statistical modeling of rare events and time series data.
The lab also conducts empirical research that uses stochastic processes to analyze high-frequency stock market data and measure risk.
Key terms
- Mathematical optimization
- A method for mathematically finding the solution that makes a target quantity as good as possible under given conditions
- Robust optimization
- An approach to optimization that seeks solutions that are unlikely to give bad results even when the data are uncertain
- Stochastic process
- A mathematical model of a quantity that changes randomly over time
ResearchResearch themes
The lab deepens the theory of mathematical optimization and mathematical statistics, and applies the resulting methods to mechanics and data analysis. Its main pillars are solution methods for optimization, statistical modeling, and the statistical analysis of stochastic processes.
*Illustrative imageContinuous Optimization and Applied Mechanics
The lab studies the theory of continuous optimization, including convex optimization, complementarity, and duality, and applies it to problems in mechanics such as structural optimization, contact mechanics, and elastoplasticity.
*Illustrative imageRobust Optimization
The lab deals with optimization problems that involve uncertain data. It studies robust optimal design methods for structures and methods for quantitatively evaluating robustness.
*Illustrative imageMathematical Statistics and Statistical Modeling
The lab works on computational algebraic statistics, which applies the holonomic gradient method to statistics, on statistical modeling of rare events and time series data, and on modeling with optimal transport maps.
*Illustrative imageStatistical Analysis of Stochastic Processes
The lab studies the theory of estimators for stochastic processes such as diffusion processes and self-exciting point processes, and applies it to modeling high-frequency stock market data and measuring risk.
MembersFaculty
- ProfessorYoshihiro KannoPublications ↗
- ProfessorTomonari SeiPublications ↗
- Associate ProfessorTeppei OgiharaPublications ↗
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: “Rosenbrock function 2” RokerHRO / Public domain (cropped)
- Continuous Optimization and Applied Mechanics: “Bridge of the Gods October 2019 001” King of Hearts / CC BY-SA 4.0 (cropped)
- Robust Optimization: “Himmelblau Function” Simiprof / CC BY-SA 3.0 (cropped)
- Mathematical Statistics and Statistical Modeling: “Symmetric distribution for continuous probability distribution” Data1125 / CC0 (cropped)
- Statistical Analysis of Stochastic Processes: “Asset-Prices-Simulated-using-the-Brownian-Motion-Stochastic-Process-Many” NivedRajeev / CC BY-SA 4.0 (cropped)
- Related lab (統計情報学研究室): “Beta distribution for 3 different prior probability functions - J. Rodal” Dr. J. Rodal / CC BY-SA 3.0 (cropped)
- Related lab (数理生命情報学研究室): “Mouse Embryo Cerebellum ( )” NIH Image Gallery from Bethesda, Maryland, USA / Public domain (cropped)
- Related lab (学習数理情報学研究室(山西研究室)): “CERN Server 03” Florian Hirzinger - www.fh-ap.com / CC BY-SA 3.0 (cropped)
- Related lab (計算情報学研究室): “SocialNetworkAnalysis” Martin Grandjean / CC BY-SA 4.0 (cropped)
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