Ruohan Zhan

I am an assistant professor of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology. My primary research interest lies in the understanding and optimization of online marketplaces. I study the economic analysis of the dynamics and interactions among multiple stakeholders, the causal evaluation of marketplace interventions, and the optimization of platform operations, such as recommendation algorithms and digital experimentation. Methodologically, I am interested in causal inference, econometrics, statistical learning and machine learning.

Previously, I received my BS in mathematics from Peking University (2017), my MS in statistics and PhD in computational and applied mathematics from Stanford University (2021), where my doctoral research was advised by Susan Athey. I was a postdoc fellow at Stanford Graduate School of Business in the Golub Capital Social Impact Lab (2022). I have also spent the summers of 2019 and 2020 at Google Research and have worked full-time at Kuaishou Technology during 2021-2022.



Post-Episodic Reinforcement Learning Inference, with Vasilis Syrgkanis. [arxiv]

Journal Publications

Policy Learning with Adaptively Collected Data. [arxiv, software]
Ruohan Zhan, Zhimei Ren, Susan Athey, Zhengyuan Zhou.
Management Science. 2023.

Confidence intervals for policy evaluation in adaptive experiments. [arxiv, software]
Vitor Hadad, David A. Hirshberg, Ruohan Zhan, Stefan Wager, Susan Athey.
Proceedings of the National Academy of Sciences 118.15, 2021.

Conference Publications

Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization. [arxiv]
Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey, Emma Brunskill.
Conference on Neural Information Processing Systems (NeurIPS) 2023.

ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor. [arxiv]
Wanqi Xue, Qingpeng Cai, Ruohan Zhan, Dong Zheng, Peng Jiang, Kun Gai, Bo An.
International Conference on Learning Representations (ICLR) 2023.

Two-Stage Constrained Actor-Critic for Short Video Recommendation. [arxiv]
Qingpeng Cai, Zhenghai Xue, Chi Zhang, Wanqi Xue, Shuchang Liu, Ruohan Zhan, Xueliang Wang, Tianyou Zuo, Wentao Xie, Dong Zheng, Peng Jiang, Kun Gai.
Proceedings of the Web Conference (WWW) 2023.

Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation. [arxiv]
Ruohan Zhan, Changhua Pei, Qiang Su, Jianfeng Wen, Xueliang Wang, Guanyu Mu, Dong Zheng, Peng Jiang.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2022.

Beyond Central Limit Theorem for Higher-Order Inference in Batched Bandits.
Yechan Park, Ruohan Zhan, Nakahiro Yoshida.
NeurIPS Workshop on Causality for Real-world Impact, 2022.

Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits. [arxiv, software]
Ruohan Zhan, Vitor Hadad, David A. Hirshberg, Susan Athey.
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2021.

Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities. [arxiv, software]
Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed Chi, Minmin Chen.
Proceedings of the Web Conference (WWW) 2021.

Earlier years

Distortion Agnostic Deep Watermarking. [paper]
Xiyang Luo, Ruohan Zhan, Huiwen Chang, Feng Yang, Peyman Milanfar.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.

Machine learning for cardiac ultrasound time series data. [paper]
Baichuan Yuan, Sathya R Chitturi, Geoffrey Iyer, Nuoyu Li, Xiaochuan Xu, Ruohan Zhan, Rafael Llerena, Jesse T Yen, Andrea L Bertozzi.
Medical Imaging: Biomedical applications in molecular, structural, and functional imaging (SPIE) 2017.

CT image reconstruction by spatial-radon domain data-driven tight frame regularization. [arxiv]
Ruohan Zhan, Bin Dong.
SIAM Journal on Imaging Sciences 9 (3), 1063-1083, 2016.


IEDA3560: Predictive Analysis.
This course focuses on how companies identify, evaluate, and capture decision analytic opportunities to create value. Basic analytic methods as well as real corporate cases studies will be covered. The analytical methods include ways to use data to develop insights and predictive capabilities using machine learning, data mining, and forecasting techniques. Some aspects of the use of optimization methods to support decision-making in the presence of a large number of alternatives and business constraints will be covered. The concepts learned in this class should help students identify opportunities in which decision analytics can be used to improve performance and support important decisions. This course is usually offered for each spring semester. For current information, see course catalog.

Copyright © 2024, Ruohan Zhan. All Rights Reserved.