Ruihao Zhu

Ruihao Zhu 

Assistant Professor
Purdue Krannert School of Management

Contact

Email: rzhu@mit.edu
LinkedIn   Google Scholar

About

Ruihao Zhu is an Assistant Professor of Supply Chain and Operations Management at Purdue Krannert School of Management. Born in Guangzhou, China, Ruihao received his Interdisciplinary Ph.D. in Statistics from the Massachusetts Institute of Technology and B.Eng. degrees in Electrical Engineering and Computer Science from both the Shanghai Jiao Tong University and the University of Michigan in 2015. He spent the summers of 2020 and 2019 as a research scientist intern with Amazon and Google Research.

Ruihao's research seeks to help organizations to improve decision-making using tools from machine learning and experimental design, with a focus on applications in business analytics, supply chain management, and fairness & privacy. As part of it, he enjoys collaborating with companies across different industries, such as consumer packaged goods and manufacturing. His works have been recognized by an Honorable Mention in INFORMS George E. Nicholson 2019 Student Paper Competition and a Finalist in POMS-JD.com 2019 Best Data-Driven Research Paper Competition.

Selected Working Papers

Calibrating Sales Forecast in a Pandemic Using Competitive Online Non-Parametric Regression (with David Simchi-Levi, Rui Sun, and Michelle X. Wu)
Major revision, Management Science
◇ In collaboration with AB InBev, see here for a summary
◇ Preliminary version appeared (as oral presentation) in KDD 2021 Workshop on Machine Learning for Consumers and Markets (MLCM at KDD 2021).
Supply Chain Management SIG Meeting, MSOM Conference 2021

Non-Stationary Reinforcement Learning: The Blessing of (More) Optimism (with Wang Chi Cheung and David Simchi-Levi)
Major revision, Management Science
◇ Preliminary version appeared in 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)
◇ Preliminary version appeared in Proceedings of the 38th International Conference on Machine Learning (ICML 2021)

Joint Patient Selection and Scheduling under No-Shows: Theory and Application in Proton Therapy (with Soroush Saghafian, Nikolaos Trichakis, and Helen Shih)
Major revision, Production and Operations Management

Learning to Route Efficiently with End-to-End Feedback: The Value of Networked Structure (with Eytan Modiano)
Working paper

Journal Papers

Meta Dynamic Pricing: Transfer Learning Across Experiments (with Hamsa Bastani and David Simchi-Levi)
Management Science (Forthcoming)
Spotlight Track, INFORMS 2019 RM&P Conference

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

Selected Conference Publications

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)

Teaching

• MIT 1.S982 Statistical Learning in Operations (Spring 2020)
    — Enrollment: 15 (primarily PhD student)
    — Teaching Assistant for David Simchi-Levi

• MIT Sloan 15.774 The Analytics of Operations Management (Fall 2019)
    — Enrollment: 61 (primarily Full-Time MBA, Master of Supply Chain Management, and Master of Business Analytics programs)
    — Core requirement for MIT Sloan Business Analytics Certificate
    — Teaching Assistant for Negin Golrezaei

Professional Services

• Reviewer for Management Science, Operations Research, Manufacturing & Service Operations Management, Production and Operations Management, MSOM Service Operations SIG 2020, Journal of Machine Learning Research (JMLR), IEEE Journal on Selected Areas in Information Theory (JSAIT), International Conference on Machine Learning (ICML) 2020 - 21, Conference on Neural Information Processing Systems (NeurIPS) 2019 - 21, International Conference on Algorithmic Learning Theory (ALT) 2019.