Renyu (Philip) Zhang 张任宇

About Me

As an AI builder, researcher, and educator, I study how AI and data science reshape decision-making on online platforms and marketplaces.

My research develops large language models, machine learning, causal inference, and data-driven optimization, to evaluate and optimize operations strategies for online platforms and marketplaces, sharing economy, and social networks, especially their recommendation, advertising, pricing, and matching policies. My work has been published in Management Science, Operations Research, and Manufacturing & Service Operations Management, and recognized by various research awards of the INFORMS, POMS, and CSAMSE communities.

I serve as an Associate Editor for Operations Research and Manufacturing & Service Operations Management, and a Senior Editor for Production and Operations Management. My projects have been funded by HK RGC Research Fellow Scheme, NSFC Excellent Young Scientist Program (优青), Tencent, Alibaba Cloud and Didi.

Beyond academia, I am an economist and Tech Lead at Kuaishou, where I develop and implement economics and data science frameworks to optimize the ecosystem of a large-scale online video-sharing and live-streaming platform.

Prior to joining CUHK, I was an Assistant Professor at NYU Shanghai (2016-2022), affiliated with NYU Stern. I obtained my PhD in Operations Management from Olin Business School, Washington University in St. Louis (advised by Nan Yang and Fuqiang Zhang), and my B.S. in Mathematics from Peking University.

Selected Publications

• Deep Learning Based Causal Inference for Large-Scale Combinatorial Experiments
  Management Science, forthcoming, 2025
  Abstract

  We propose a deep learning framework for estimating causal effects in large-scale combinatorial experiments where treatments have complex interactions. We provide theoretical guarantees and validate the approach with data from a major online platform.

• Nonprogressive Diffusion on Social Networks: Approximation and Applications
  Management Science, forthcoming, 2025
  Abstract

  We study nonprogressive diffusion processes on social networks where individuals can switch between adoption and non-adoption states. We develop approximation algorithms with provable performance guarantees and apply them to content promotion and public health interventions.

• Online Advertisement Allocation under Customer Choices and Algorithmic Fairness
  Management Science, 71(1), 2025
  Abstract

  We study online advertisement allocation that balances customer choice behavior with algorithmic fairness constraints. We develop efficient algorithms and characterize the trade-off between platform revenue and fairness across advertisers.

• Cold Start to Improve Market Thickness on Online Advertising Platforms
  Management Science, 69(7), 2023
  Abstract

  We propose data-driven algorithms to address the cold-start problem on online advertising platforms by improving market thickness for new advertisers. We validate the approach through large-scale field experiments on a major advertising platform.

• Data Aggregation and Demand Prediction
  Operations Research, 70(5), 2022
  Abstract

  We study how aggregating data across products and locations can improve demand prediction accuracy. We develop a data aggregation and combination framework and demonstrate its effectiveness through extensive experiments in retail settings.

Full list of publications →

Work With Me

• I am hiring 1~2 PhD students and 1~2 post-doctoral fellows in Operations Management and Information Systems. Please read this document and this Zhihu post (both in Chinese). I also occasionally take RAs and research interns who share general research interests (AI and data science to empower business decision making) with me. [Email Me for Questions] [Apply Here]

News

• I am organizing the INFORMS Service Science Online Forum Series along with Yuqian Xu. [Sign Up Here into the Email List.]
• AI Courses for Business PhDs: Spring 2026, Spring 2025, Fall 2024 Reading Group, Summer 2024, Spring 2024.
• Prospective PhD students in Operations Management at WashU Olin: Please read this document or this Zhihu post (in Chinese). [Email Me for Questions]

Contact

Email: philipzhang@cuhk.edu.hk

Department of Decisions, Operations and Technology
CUHK Business School
The Chinese University of Hong Kong
Cheng Yu Tung Building
12 Chak Cheung Street
Sha Tin, N.T., Hong Kong, China.

Quotations

"切勿要求胜利，只应要求一往无前的勇气。因为从坚忍不拔的奋斗中，你将为自己带来荣誉。但更重要的，你将为全人类带来光荣。"---古希腊奥运会格言，见Hung-Hsi Wu (伍鸿熙)《黎曼几何初步》致读者的话。

"God, give us Grace to accept with Serenity the things that cannot be changed, Courage to change the things which should be changed, and Wisdom to distinguish one from the other."---Reinhold Niebuhr, the Serenity Prayer.