Ruohan ZHAN (詹若涵)
Ruohan ZHAN (詹若涵)
PhD, Stanford University, 2021
Assistant Professor
Tel 2358 7110
Office Room 5559E
Research Interests

Causal Inference; Statistics Inference and Learning; Data-driven Decision Making

Contact Info
Tel 2358 7110
Office Room 5559E
Research Interests

Causal Inference; Statistics Inference and Learning; Data-driven Decision Making

Ruohan ZHAN joined the Department of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology as an Assistant Professor in December 2022. Prior to this position, she was the postdoctoral fellow at the Stanford Graduate School of Business in the Golub Capital Social Impact Lab. She received Ph.D. in Computational and Applied Mathematics and M.S. in Statistics from Stanford University (2021), and B.S. degree in Mathematics from Peking University (2017). 
Honors and Awards
  • TOTAL Innovation Fellowship, 2018, 2019
  • D.E. Shaw Exploration Fellowship, 2019
  • National Scholarship, China, 2016
  • Qualcomm Global Scholarship, 2015
  • First Prize of 2018 Citadel Datathon at Stanford, Spotlight, 2018
  • Finalist of the Mathematical Contest in Modeling (COMAP), 2016
Selected Publications
  1. R. Zhan, Z. Ren, S. Athey, Z. Zhou (2023), Policy Learning with Adaptively Collected DataManagement Science. Accepted.

  2. W. Xue, Q. Cai, R. Zhan, D. Zheng, P. Jiang, K. Gai, B. An (2023), ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual ActorInternational Conference on Learning Representations (ICLR) 2023.

  3. Q. Cai, Z. Xue, C. Zhang, W. Xue, S. Liu, R. Zhan, X. Wang, T. Zuo, W. Xie, D. Zheng, P. Jiang, K. Gai (2023), Two-Stage Constrained Actor-Critic for Short Video RecommendationProceedings of the Web Conference (WWW) 2023.

  4. R. Zhan, C. Pei, Q. Su, J. Wen, X. Wang, G. Mu, D. Zheng & P. Jiang (2022), Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation, In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4472-4481).

  5. Y. Park, R. Zhan, N. Yoshida (2022), Beyond Central Limit Theorem for Higher-Order Inference in Batched BanditsNeurIPS Workshop on Causality for Real-world Impact 2022.

  6. V. Hadad, D. A. Hirshberg, R. Zhan, S. Wager, S. Athey (2021), Confidence Intervals for Policy Evaluation in Adaptive Experiments, Proceedings of the National Academy of Sciences, 118.15.

  7. R. Zhan, V. Hadad, D. A. Hirshberg, S. Athey (2021) Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits, Shorter version accepted in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2021.

  8. R. Zhan, K. Christakopoulou, E. Le, J. Ooi, M. Mladenov, A. Beutel, C. Boutilier, E. Chi, M. Chen (2021), Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities, In Proceedings of the Web Conference 2021.

  9. X. Luo, R. Zhan, H. Chang, F. Yang & P. Milanfar (2020), Distortion Agnostic Deep Watermarking, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13548-13557).

  10. B. Yuan, S. R. Chitturi, G. Iyer, N. Li, X. Xu, R. Zhan, R. Llerena, J.T. Yen, A. L. Bertozzi (2017), Machine Learning for Cardiac Ultrasound Time Series Data, SPIE Medical Imaging 2017.

  11. R. Zhan & B. Dong (2016), CT Image Reconstruction by Spatial-Radon Domain Data-Driven Tight Frame RegularizationSIAM Journal on Imaging Sciences, 9(3), 1063-1083.