Zhao Laboratory

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Creating the Future through Research

“Tackling social challenges with data science.”

“Advancing the next era with mathematics and logic.”

About the Lab Faculty

About the Lab

In today’s society, flexible and accurate decision-making driven by data is essential. Our laboratory addresses challenges in business and society through mathematical approaches. We develop new analytical methods that combine evaluation techniques such as Data Envelopment Analysis (DEA) with statistics, optimization, and machine learning.

Seminar Highlights

  • A research policy integrating theory and practice
  • Topic selection tailored to students’ interests
  • Hands-on analysis using real-world data
  • Active participation in conferences and publications
  • Support for graduate studies and career development
Laboratory image
A learning environment with diverse members

Our Research

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Intelligent Algorithms & Machine Learning

We develop cutting-edge algorithms that integrate optimization and learning theory to support decision-making.

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Data Science for Management & Marketing

Using real data, we explore prediction and causal inference for managerial and market analysis.

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Mathematical Evaluation for Decision Support

With DEA and related methods, we aim to support optimal choices in multi-criteria environments.

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Publications

International Conferences

  • Mar 28–30, 2025: The 11th International Conference on Information Management (ICIM 2025)
  • Feb 14–17, 2025: 17th International Conference on Machine Learning and Computing
  • Dec 20–22, 2024: 7th International Conference on Data Science and Information Technology
  • Nov 23–24, 2024: The 20th Annual Meeting & International Conference of OR Society of TAIWAN
  • Nov 18–20, 2024: International Conference on Data Envelopment Analysis

Journals

  • Sekitani, K., & Zhao, Y. (2025). Closest targets in Russell graph measure of strongly monotonic efficiency for an extended facet production possibility set. Journal of the Operational Research Society, 1–19.
  • Zhao, Y., & Tsubaki, M. (2025). An algorithmic marketing approach to analyzing consumer well-being: Incorporating psychological factors in customer loyalty. Journal of Retailing and Consumer Services, 84, 104238.
  • Zhao, Y. (2024). A Density-Weighted Information Gain Tree for Clustering Mixed-Type Data. In 2024 7th International Conference on Data Science and Information Technology (DSIT) (pp. 1–6). IEEE.
  • Zhao, Y., & Morita, H. (2024). Estimating Malmquist-type indices with StoNED. Expert Systems with Applications, 250, 123877.
  • Sekitani, K., & Zhao, Y. (2023). Least-distance approach for efficiency analysis: A framework for nonlinear DEA models. European Journal of Operational Research, 306(3), 1296–1310.
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Profile

Yu Zhao

Yu Zhao

Junior Associate Professor, Department of Management, School of Management, Tokyo University of Science

Visiting Lecturer, The Institute of Statistical Mathematics

Fields: Marketing Science, Machine Learning, Mathematical Optimization, Efficiency & Productivity Analysis

Striving daily toward a sustainable society, we aim to generate outcomes that benefit the world through data science. Together with students, we embrace new discoveries with curiosity and joy.

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