AI-Driven Econometrics
We explore new analytical frameworks that connect econometrics and artificial intelligence, integrating prediction, causal inference, and decision support.
Data Science • Econometrics • Machine Learning • AI
Data Science & Econometrics Lab
Data Science and AI at the Core, Creating Knowledge Across the Social and Natural Sciences
This laboratory promotes both theoretical and empirical research based on data science, machine learning, and econometrics, with applications across both the social sciences and the natural sciences. By integrating AI and statistics, we aim to provide analysis, prediction, and decision support for complex real-world problems.
Artificial Intelligence
Data Science
Machine Learning
Econometrics
Causal Inference
Federated Learning
Financial Data Science
Digital Transformation
Research Vision
A Research Hub Connecting Theory, Empirical Analysis, and AI Implementation
We integrate mathematically grounded analysis with intelligent AI-based processing to advance high-level research and education that contributes to the economy, industry, and society.
AI
Machine learning, intelligent models, decision support, and responsible design
DS
Econometric analysis, inference, computation, large-scale data analysis, and empirical research
About the Lab
A Laboratory Centered on Data Science and Econometrics
Professor Qingfeng Liu conducts research in data science, econometrics, and machine learning.
He is currently a Professor in the Faculty of Science and Engineering at Hosei University
and also serves as a research affiliate at the School of Global Public Health, New York University.
After earning his Ph.D. in Economics from Kyoto University, he worked as a postdoctoral researcher
at Princeton University. His research has been published in international journals such as
Journal of Business & Economic Statistics, Econometrics Journal, and Econometric Reviews,
and he serves as an Associate Editor of the Journal of the American Statistical Association
and Asia-Pacific Financial Markets. In recent years, he has advanced the integration of machine learning
and econometrics, exploring new privacy-preserving methods for economic analysis, including federated learning.
Research Areas
Research Areas at the Intersection of Data Science and AI
We treat theory building and applied implementation as one integrated endeavor,
and deeply pursue the dimensions of prediction, explanation, inference, and optimal decision-making.
Machine Learning & Statistical Learning
We develop learning theory and statistical inference methods for high-dimensional data, nonlinearity, and irregular observation structures.
Federated Learning & Privacy
Through collaborative learning in distributed environments, we aim to achieve both privacy protection and high-accuracy analysis.
Data Science for Industry and Society
We apply data-driven machine learning models to finance, industry, medicine, and social systems.
News
News
2026/01/27 Serve as Associate Editor, 2026 ~ , the Journal of the American Statistical Association (JASA)
2024/10/21 Gu, D., Liu, Q. and Zhang, X. (2024), Model Averaging under Flexible Loss Functions, INFORMS Journal on Computing
2024/04/01 Liu, Q. and Feng, Y. (2024), Machine Collaboration, stat PDF
Read more
International Meetings
International Conferences Organized by the Lab
Awards
Awards & Achievements
ACIEK (Winter)-IMIP 2024
Outstanding Contribution Award
Yuta Tokita (joined the seminar in 2022)
Learning Materials
ML & AI Resources
A collection of machine learning and AI materials and code, including lecture slides, tutorials, and implementations, available on GitHub.
Machine Learning Toolkit
A comprehensive repository of machine learning and AI resources, covering theory and implementation.
DX・AI Edge
Slides on digital transformation and AI business applications.
Federated Learning
Lecture slides on the theory and implementation of federated learning.
Why Double Descent?
Theoretical insights into the double descent phenomenon in machine learning.
Writings
Analects & Poems
A collection of poems, philosophical essays, and dialogues with ChatGPT, reflecting diverse creative and intellectual activities.
Analects (New Interpretation)
Modern interpretations and new perspectives on the Analects, blending tradition and innovation.
Poems Collection
A collection of poems on nature, life, and philosophy.
Dialogue on QM with ChatGPT
Philosophical discussions on reconstructing quantum mechanics through dialogues with ChatGPT.
Other Essays
Collection of essays and papers on quantum mechanics, philosophy, and AI.
Projects & Output
Research Output, Degrees, and Career Placement
The laboratory’s achievements are presented in the form of research projects,
undergraduate theses, master’s theses, PhD graduates, and career placement outcomes.
01
02
Undergraduate Theses
Students Expected to Graduate in 2025
[1] Data-Driven Switch Loss Method for High-Dimensional Imbalanced Data, Tomohito Katsuyama, Takahiro Mamuro, (2026).
[2] Development of a Recyclable Resource Classification Application Using Image Recognition AI Models, Sakurako Takiguchi, Rina Nakayama, Karin Nagasawa, (2026).
[3] Evaluation of Transfer Learning Models for AI-Generated Image Recognition and Their Application to Fake Image Detection, Hidetora Imamura, Ryusei Koyanagi, (2026).
[1] Data-Driven Switch Loss Method for High-Dimensional Imbalanced Data, Tomohito Katsuyama, Takahiro Mamuro, (2026).
[2] Development of a Recyclable Resource Classification Application Using Image Recognition AI Models, Sakurako Takiguchi, Rina Nakayama, Karin Nagasawa, (2026).
[3] Evaluation of Transfer Learning Models for AI-Generated Image Recognition and Their Application to Fake Image Detection, Hidetora Imamura, Ryusei Koyanagi, (2026).
2024 Graduates
[1] Construction and Accuracy Evaluation of a Ranking Prediction Model Using Machine Learning: A Case Study of Horse Racing Prediction PDF
[2] Empirical Analysis of the Relationship Between Technical Indicators and Stock Price Fluctuations PDF
[3] Data Analysis for Strategy Formulation in Soccer PDF
[4] Detection and Analysis of Fraudulent Accounting Using Machine Learning PDF
[1] Construction and Accuracy Evaluation of a Ranking Prediction Model Using Machine Learning: A Case Study of Horse Racing Prediction PDF
[2] Empirical Analysis of the Relationship Between Technical Indicators and Stock Price Fluctuations PDF
[3] Data Analysis for Strategy Formulation in Soccer PDF
[4] Detection and Analysis of Fraudulent Accounting Using Machine Learning PDF
03
Master’s Theses
Students Expected to Complete the Master’s Program in 2025
[1] Estimation of the Causal Effects of Corporate Digitalization Factors: Application of the GLDAG Model and Federated Learning, Xingyu Li, (expected completion in 2026).
[1] Estimation of the Causal Effects of Corporate Digitalization Factors: Application of the GLDAG Model and Federated Learning, Xingyu Li, (expected completion in 2026).
04
PhD Graduates
Dr. Qingsong YAO
Ph.D. in Economics
Current position after graduation: Assistant Professor, Louisiana State University, USA.
Ph.D. in Economics
Current position after graduation: Assistant Professor, Louisiana State University, USA.
Dr. Ziyan ZHAO
Ph.D. in Commerce
Current position after graduation: Research Fellow, Nanyang Technological University, Singapore.
Ph.D. in Commerce
Current position after graduation: Research Fellow, Nanyang Technological University, Singapore.
05
Career Placement
Career destinations of graduates and master’s degree recipients
Major audit firms, Tokyo Metropolitan Police Department, financial advisory firms, major regional banks, major credit card companies, major consulting firms, data science divisions of major automobile manufacturers, software development divisions of major IT firms (job-based hiring), and secondary school teachers.
Major audit firms, Tokyo Metropolitan Police Department, financial advisory firms, major regional banks, major credit card companies, major consulting firms, data science divisions of major automobile manufacturers, software development divisions of major IT firms (job-based hiring), and secondary school teachers.
Contact
Join Research That Shapes the Future
We welcome students, researchers, and collaborative partners who are interested in
Data Science, AI, machine learning, causal inference, econometrics, optimization,
distributed learning, and related fields.
Lab Identity
Data. Intelligence. Insight.
This laboratory promotes research grounded in Data Science, Artificial Intelligence,
and Econometrics, integrating theory, empirical analysis, and implementation.
By combining mathematically rigorous analysis with advanced AI-based intelligent processing,
it explores new approaches to complex problems in the economy, industry, and society.
At the same time, it values both academic contribution and social impact through collaboration
with international research networks.
- Integrated research and education in Data Science, AI, and Econometrics
- A balance between theoretical research and real-data analysis
- Active promotion of international collaboration and academic dissemination
- Student-led research projects and implementation-oriented education
Creed Enjoy what you decide to do, and master what you do.
Prophecy Japan's economy will be revived by the spirit of craftsmanship.
Principle Rather than confrontation, welcome others as colleagues.
Prophecy Japan's economy will be revived by the spirit of craftsmanship.
Principle Rather than confrontation, welcome others as colleagues.
