Ruihao Zhu
AboutRuihao Zhu is currently a 5th-year candidate for the Interdisciplinary Ph.D. in Statistics (IDPS) at the MIT Institute for Data, Systems, and Society (IDSS) and Laboratory for Information & Decision Systems (LIDS). He has the pleasure of being advised by David Simchi-Levi. Born in Guangzhou, China, Ruihao received his B.Eng. degrees in Electrical Engineering and Computer Science from both 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 Supply Chain Optimization Technologies (SCOT) and Google Research Mountain View. Ruihao's research seeks to help organizations to improve decision-making in uncertain and dynamically changing environments using tools from data analytics, with a focus on applications in revenue management, supply chain management, and fairness & privacy. As part of it, he enjoys collaborating with companies across different industries, such as consumer packaged goods and medical devices 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. Journal PublicationsCalibrating Sales Forecast in a Pandemic Using Online Non-Parametric Regression Model (with David Simchi-Levi, Rui Sun, and Michelle X. Wu) Non-Stationary Reinforcement Learning: The Blessing of (More) Optimism (with Wang Chi Cheung and David Simchi-Levi) Meta Dynamic Pricing: Transfer Learning Across Experiments (with Hamsa Bastani and David Simchi-Levi) Hedging the Drift: Learning to Optimize under Non-Stationarity (with Wang Chi Cheung and David Simchi-Levi) Joint Patient Selection and Scheduling under No-Shows: Theory and Application in Proton Therapy (with Soroush Saghafian, Nikolaos Trichakis, and Helen Shih) Selected Working PapersIs Model-Free Learning Nearly Optimal for Non-Stationary RL? (with Weichao Mao, Kaiqing Zhang, David Simchi-Levi, and Tamer Basar) Learning to Route Efficiently with End-to-End Feedback: The Value of Networked Structure (with Eytan Modiano) Selected Conference PublicationsReinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism (with Wang Chi Cheung and David Simchi-Levi) Learning to Optimize under Non-Stationarity (with Wang Chi Cheung and David Simchi-Levi) Coresets for Differentially Private K-Means Clustering and Applications to Privacy in Mobile Sensor Networks (with Dan Feldman, Chongyuan Xiang, and Daniela Rus) Threshold Bandits, With and Without Censored Feedback (with Jacob Abernethy and Kareem Amin) Differentially Private and Strategy-Proof Spectrum Auction with Approximate Revenue Maximization (with Kang G. Shin) Differentially Private Spectrum Auction with Approximate Revenue Maximization (with Zhijing Li, Fan Wu, Kang G. Shin, and Guihai Chen) Teaching
• MIT 1.S982 Statistical Learning in Operations (Spring 2020)
• MIT Sloan 15.774 The Analytics of Operations Management (Fall 2019) Professional Services• Reviewer for Management Science, Operations Research, 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 - 20, International Conference on Algorithmic Learning Theory (ALT) 2019. • Co-organizer (together with David Simchi-Levi) of MIT Data Science Lab 2019 Workshop on Learning and Optimizing in Operations. [link] • Coordinator of MIT Data Science Lab seminar series (2019 - 21). |