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Takano Lab
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The lab studies financial engineering and machine learning, centered on mathematical optimization.
AboutAbout the lab
Solving problems in financial engineering and machine learning with mathematical optimization.
The Takano Lab conducts research on mathematical optimization, financial engineering, and machine learning.
The lab works on solving problems in financial engineering and machine learning using mathematical optimization methods.
The basic flow is to survey prior research, propose new mathematical models or computational methods, and verify their effectiveness through computer experiments.
The research requires a certain level of mathematical ability and programming skill, and both are stated as required conditions.
Key terms
- mathematical optimization
- A technique that turns decision-making problems into mathematical models and solves them with mathematical algorithms and computers
- portfolio selection
- The optimization of investment strategies. In financial engineering, decisions are made by balancing profitability and risk.
- machine learning
- A field that improves the learning ability of computers based on past experience (data)
NewsLab news
A paper by master's students and others was accepted at the international conference ICAIF 2026
An explanatory article by graduates and others was published in the journal of the Operations Research Society of Japan
A paper by graduates and others was accepted at the international conference NeurIPS 2026
A master's student received the Student Paper Award of the Operations Research Society of Japan
Master's students and others received the FIT Encouragement Award at FIT2026
ResearchResearch themes
The lab's research is built on three pillars: mathematical optimization, financial engineering, and machine learning. It solves problems in financial engineering and machine learning with mathematical optimization methods, and also works on developing and applying new optimization techniques.
*Illustrative imageMethods and applications of mathematical optimization
Using mathematical optimization, which turns real-world decision-making problems into mathematical models and solves them, the lab tackles problems in financial engineering and machine learning. It also studies the development and application of new optimization models and algorithms.
*Illustrative imageFinancial engineering and portfolio selection
The lab studies decision-making that balances profitability and risk, assuming the uncertainty of financial products whose values change, such as stocks and bonds. The central topic is portfolio selection, which optimizes investment strategies.
*Illustrative imagePredictive performance and computational efficiency in machine learning
The lab aims to improve the learning ability of computers based on past experience (data), and to improve the predictive performance and computational efficiency of learning models. It focuses mainly on statistical model selection and recommender systems.
MembersFaculty
The Takano Lab's members consist of faculty members, visiting researchers, doctoral, master's, and undergraduate students, and administrative assistants.As of 2026
- Associate ProfessorYuichi Takano
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: “NACA Computer at the Lewis Flight Propulsion Laboratory (GRC-1951-C-27174)” NASA Glenn Research Center / Public domain (cropped)
- Methods and applications of mathematical optimization: “Shipping container - Port of Rotterdam - 2014 - 2” AgainErick / CC BY-SA 4.0 (cropped)
- Financial engineering and portfolio selection: “New York Stock Exchange Facade 2015” Jeffrey Zeldman from Manhattan, USA / CC BY 2.0 (cropped)
- Predictive performance and computational efficiency in machine learning: “Virginia Tech - data center” Christopher Bowns / CC BY-SA 2.0 (cropped)
- Related lab (数理最適化・連続最適化
（Liu Tianxiang） ): “Graph of function of 2 variables” Zasdfgbnm / Public domain (cropped) - Related lab (吉瀬研究室): “CLR-2 logistics, Supply, transition through planning 130828-M-ZB219-309” Cpl. Paul Peterson / Public domain (cropped)
- Related lab (繁野研究室): “Flight departure board at Canberra Airport, November 2022” Kgbo / CC BY-SA 4.0 (cropped)
- Related lab (有馬澄佳研究室): “002 Production line - car assembly line in General Motors Manufacturing Poland - Gliwice, Poland” Marek Ślusarczyk (Tupungato) Photo portfolio / CC BY 3.0 (cropped)
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