Ruihao Zhu

Ruihao Zhu 

Assistant Professor of Operations, Technology, and Information Management
Cornell Nolan School of Hotel Administration
Cornell SC Johnson College of Business

Contact

Email: ruihao.zhu[AT]cornell.edu

Education Background

Ph.D. in Controls and Statistics, Massachusetts Institute of Technology, 2021 (Advisor: David Simchi-Levi)
B.Eng. in Electrical and Computer Engineering, Shanghai Jiao Tong University, 2015
B.Eng. in Electrical Engineering and Computer Science, University of Michigan, 2015

Research Interests

I work on developing novel algorithms for machine learning and sequential decision-making (e.g., multi-armed bandits and reinforcement learning) to address fundamental and practical challenges in revenue management, supply chain, and service operations. My works have been recognized by the following awards:
Honorable Mention, INFORMS George B. Dantzig Dissertation Award 2022
2nd Place, INFORMS Innovative Applications in Analytics Award 2022
Finalist, INFORMS Service Science Best Cluster Paper Award 2021
Honorable Mention, INFORMS George E. Nicholson Student Paper Competition 2019
Finalist, POMS-JD.com Best Data-Driven Research Paper Competition 2019

Selected Working Papers

Phase Transitions in Learning and Earning under Price Protection Guarantee (with Qing Feng and Stefanus Jasin)
Preprint

Learning to Price Supply Chain Contracts against a Learning Retailer (with Xuejun Zhao and William B. Haskell)
Preprint

Risk-Aware Linear Bandits: Theory and Applications in Smart Order Routing (with Jingwei Ji and Renyuan Xu)
Preprint
◇ Preliminary version: Proceedings of the 3rd ACM International Conference on AI in Finance (ICAIF 2022), INFORMS Workshop on Data Science 2022

Safe Data Collection for Offline and Online Policy Learning (with Branislav Kveton)
Preprint
◇ In collaboration with Amazon
◇ Preliminary version: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), MIT 2021 Conference on Digital Experimentation (CODE@MIT), INFORMS Workshop on Data Science 2022

Non-Stationary Reinforcement Learning: The Blessing of (More) Optimism (with Wang Chi Cheung and David Simchi-Levi)
Minor Revision, Management Science
◇ Preliminary version: Proceedings of the 37th International Conference on Machine Learning (ICML 2020)

Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control (with Weichao Mao, Kaiqing Zhang, David Simchi-Levi, and Tamer Basar)
Reject & Resubmit, Management Science
◇ Preliminary version: Proceedings of the 38th International Conference on Machine Learning (ICML 2021)

Journal Publications

Calibrating Sales Forecast in a Pandemic Using Competitive Online Non-Parametric Regression (with David Simchi-Levi, Rui Sun, and Michelle X. Wu)
Management Science (Accepted)
◇ In collaboration with AB InBev
◇ Preliminary version: (Oral Presentation) KDD 2021 Workshop on Machine Learning for Consumers and Markets (MLCM at KDD 2021)
2nd Place, INFORMS Innovative Applications in Analytics Award 2022
Finalist, INFORMS Service Science Section Best Cluster Paper Award 2021
Supply Chain Management SIG Meeting, MSOM Conference 2021

Joint Patient Selection and Scheduling under No-Shows: Theory and Application in Proton Therapy (with Soroush Saghafian, Nikolaos Trichakis, and Helen Shih)
Production and Operations Management [Journal]
◇ In collaboration with Massachusetts General Hospital

Meta Dynamic Pricing: Transfer Learning Across Experiments (with Hamsa Bastani and David Simchi-Levi)
Management Science 68(3): 1865-1881 (2021) [Journal]
Spotlight Track, INFORMS RM&P Conference 2019

Hedging the Drift: Learning to Optimize under Non-Stationarity (with Wang Chi Cheung and David Simchi-Levi)
Management Science 68(3): 1696-1713 (2021) [Journal]
◇ Preliminary version: Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019).
Honorable Mention, INFORMS George E. Nicholson Student Paper Competition 2019
Finalist, POMS-JD.com Best Data-Driven Research Paper Competition 2019
Service Operations SIG Meeting, MSOM Conference 2019

Selected Conference Publications

Risk-Aware Linear Bandits with Application in Smart Order Routing (with Jingwei Ji and Renyuan Xu)
Proceedings of the 3rd ACM International Conference on AI in Finance (ICAIF 2022)

Safe Optimal Design with Applications in Off-Policy Learning (with Branislav Kveton)
Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022)

Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs (with Weichao Mao, Kaiqing Zhang, David Simchi-Levi, and Tamer Basar)
Proceedings of the 38th International Conference on Machine Learning (ICML 2021)

Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism (with Wang Chi Cheung and David Simchi-Levi)
Proceedings of the 37th International Conference on Machine Learning (ICML 2020)

Learning to Optimize under Non-Stationarity (with Wang Chi Cheung and David Simchi-Levi)
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)

Coresets for Differentially Private K-Means Clustering and Applications to Privacy in Mobile Sensor Networks (with Dan Feldman, Chongyuan Xiang, and Daniela Rus)
Proceedings of the 26th International Conference on Information Processing in Sensor Networks (IPSN 2017)

Threshold Bandits, With and Without Censored Feedback (with Jacob Abernethy and Kareem Amin)
Advances in Neural Information Processing Systems 29 (NIPS 2016)

Differentially Private and Strategy-Proof Spectrum Auction with Approximate Revenue Maximization (with Kang G. Shin)
Proceedings of the 2015 IEEE International Conference on Computer Communications (INFOCOM 2015)

Differentially Private Spectrum Auction with Approximate Revenue Maximization (with Zhijing Li, Fan Wu, Kang G. Shin, and Guihai Chen)
Proceedings of the 15th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2014)