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

Selected Paper

  • Yu Zhao and Michiko Tsubaki. An algorithmic marketing approach to analyzing consumer well-being: Incorporating psychological factors in customer loyalty. Journal of Retailing and Consumer Services 84, 104238. Elsevier. [doi] (2025)
  • Yu Zhao and Hiroshi Morita. Estimating Malmquist-type indices with StoNED. Expert Systems with Applications 250, 123877. Elsevier. [doi] (2024)
  • Kazuyuki Sekitani and Yu Zhao. Performance benchmarking of achievements in the Olympics: An application of Data Envelopment Analysis with restricted multipliers. European Journal of Operational Research 294(3), 1202–1212. Elsevier. [doi] (2021)
  • Yu Zhao, Hiroshi Morita, and Yukihiro Maruyama. The measurement of productive performance with consideration for allocative efficiency. Omega 89, 21–39. Elsevier. [doi] (2019)

Selected Conference

  • Yu Zhao. Productivity Change Estimation under Data Uncertainty: A Forest-Based Probabilistic Approach. 2025 INFORMS Annual Meeting. Oct 26–Oct 29, 2025
  • Yu Zhao. Measuring the Malmquist Productivity Index Incorporating Probabilistic Variations in Data. 34th European Conference on Operational Research. Jun 22–Jun 25, 2025
  • Yu Zhao. Sustainable Performance Evaluation and Prediction of the Banking Sector: Opening the Black Box of DEA with Machine Learning and Explainable AI. 17th International Conference on Machine Learning and Computing. Feb 14–Feb 17, 2025
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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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