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Announcements

2024/10/21 Paper accepted: Gu, D., Liu, Q. and Zhang, X., (2024), Model Averaging under Flexible Loss Functions. INFORMS Journal on Computing.
2024/04/01 Paper accepted: Liu, Q. and Feng, Y., (2024), Machine Collaboration. stat. PDF
Yuta Tokita (2022 Seminar Member) SIGNATE Competition Ranked 7th (2023).
2024/04/01 Seminar Recruitment PDF Slides Video Liu Seminar is recruiting 3rd-year students.
2022/04/03 Seminar Recruitment PDF Slides Video Liu Seminar is recruiting 3rd-year students.
2022/04/03 Lab website launched.

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Research Seminar Series

Transdisciplinary Econometrics & Data Science Seminar (Transdisciplinary Econometrics & Data Science Seminar)

The 2025 Workshop on Statistical Network Analysis and Beyond (SNAB 2025)

ACIEK(winter)-IMIP2024

Opening Speech by Liu Qingfeng
ACIEK_01 ACIEK_02

Faculty Member

Prof. Qingfeng Liu Professor:   Qingfeng Liu  Personal Website    CV
Place of Birth:   Inner Mongolia, China   
Degree:   Ph.D. in Economics, Kyoto University   
Hobbies:   Basketball, Go   
E-mail:   qliu[at]hosei.ac.jp

Professor Qingfeng Liu conducts research in the fields of data science, econometrics, and machine learning (one of the fundamental technologies of artificial intelligence). He is currently a professor in the Faculty of Science and Engineering at Hosei University and also serves as a Research Affiliate at the New York University School of Global Public Health. He obtained his Ph.D. in Economics from Kyoto University in 2007 and later moved to the United States, where he worked as a postdoctoral researcher at Princeton University. He has also worked as a visiting professor or visiting scholar at institutions such as Kyoto University and Columbia University in the U.S. His research has been published in international journals such as the *Journal of Business & Economic Statistics* and *Econometric Reviews*, and he also serves as an editorial board member for international journals such as *Economics* and *Open Economics*. His awards include the Niigata University Economic Society Prize, the Outstanding Contribution Award at ACIEK (Winter)-IMIP 2024, and the Best Conference Award at IEOM 2024 Tokyo. In recent years, he has been integrating machine learning with econometrics, exploring a new privacy-preserving machine learning approach for economic analysis called Federated Machine Learning. His research has been supported by the Japan Society for the Promotion of Science (JSPS) and the Academic Research Grant from the National Banking Academic Research Promotion Foundation. Professor Liu aims to develop machine learning and econometric methods for solving economic problems and advancing industrial technologies. He leverages IoT, big data analytics, and fintech to contribute to the Fourth Industrial Revolution and Society 5.0.
Belief   Once I decide to do something, I do it with enjoyment, and if I do it, I will master it.
Prophecy   The Japanese economy will revive with the spirit of craftsmanship.   
Principle   Rather than opposing, I welcome others as my companions.   

Three types of Ensemble Learning

abc

Machine Collaboration of two base machines

Awards

ACIEK_01ACIEK_02

Qingfeng Liu ACIEK(Winter)-IMIP2024
Outstanding Contribution Award

ACIEK_01ACIEK_02

Qingfeng Liu IEOM 2024 Tokyo
Outstanding Conference Award

tokita_compe

Yuta Tokita (Joined in 2022) SIGNATE Competition Ranked 7th (2023)
TechnoPro Design Company Food Package Image Analysis Challenge (General & Student Divisions) Classify Food Packages into Food & Beverages!

Undergraduate Thesis

Graduates of 2024

[1] Construction and Accuracy Evaluation of a Ranking Prediction Model Using Machine Learning: A Case Study on Horse Racing Prediction, Teruhide Iwasaki, Kosuke Tanaka, Sota Tokumaru, (2025). Abstract
[2] Empirical Analysis of the Relationship Between Technical Indicators and Stock Price Movements, Kota Kubomura, (2025). Abstract
[3] Data Analysis for Strategy Formulation in Soccer, Kanta Shimomura, Masaya Hayashi, (2025). Abstract
[4] Detection and Analysis of Accounting Fraud Using Machine Learning, Toshiki Tajima, Madoka Hirata, (2025). Abstract

Graduates of 2023

[1] Image Classification of Food Packages Considering Generalization Performance, Yuta Tokita, (2024). Abstract Full Text Slides
[2] Feasibility of Introducing Machine Learning Methods for Fraud Detection in Analytical Procedures in Financial Auditing, Junpei Funahashi, (2024).

Graduates of 2022

[1] Stock Price Prediction Using Tweet and Index Data, Chidai Kurosaki, Takeo Yamada, (2023). Abstract
[2] Effects of Telework Promotion and Relocation Support Measures on Population Issues, Chuya Oshima, Tsubasa Maeda, (2023). Abstract
[3] Data Analysis of Factors Influencing Soccer Match Outcomes, Koki Ito, Hiroki Kano, Ryota Kitabayashi, (2023). Abstract

Master's Thesis

2024 Master's Program Graduates

[1] Vertical Federated Learning Using Adversarial Autoencoder, Kai Huang, (Expected Graduation: 2025). Abstract
[2] Calibration of Rough Volatility Models Using Differential Machine Learning, Chidai Kurosaki, (Expected Graduation: 2025). Abstract

Career Destinations of Graduates

2024 Career Destinations of Bachelor's and Master's Graduates

Major audit firms, Metropolitan Police Department, financial advisory firms, major regional banks, major credit card companies, major comprehensive consulting firms, data science divisions of major automobile manufacturers, software development divisions of major IT companies (Job-based recruitment)

PhD Graduates Supervised

Qingsong Yao Dr. Qingsong YAO
Ph.D. in Economics
Current Position: Assistant Professor, Louisiana State University, USA.
Ziyan Zhao Dr. Ziyan ZHAO
Ph.D. in Commerce
Current Position: Research Fellow, Nanyang Technological University, Singapore.


Data Science & Econometrics Laboratory - DSELab



大海原を自由に泳ぐ



E-mail: tedsseminars@gmail.com