Zheqing (Bill) Zhu
朱哲清
Engineering Manager
Head of Applied Reinforcement Learning
Facebook (Meta) AI
LinkedIn: https://www.linkedin.com/in/zheqingzhubill/
Twitter: https://twitter.com/ZheqingZhu
Facebook (Meta) AI Profile: https://ai.facebook.com/people/zheqing-bill-zhu
Contact:
billzhu@fb.com (For industry-related inquiries)
zheqzhu@stanford.edu (For academic-related inquiries)
Zheqing (Bill) Zhu is an Engineering Manager at Facebook (Meta) AI, where he serves as the Head of Applied Reinforcement Learning. His main interest lies in bringing state-of-the-art reinforcement learning technologies to real-life and bridging the gap between theoretical reinforcement learning and real-world systems. Prior to serving as Head of Applied Reinforcement Learning, he was the engineering manager and tech lead for Facebook (Meta)'s Ads Growth Machine Learning team, where he built the team from scratch and enabled exponential growth in business market for Facebook (Meta).
While working full-time at Facebook (Meta), he is pursuing a PhD degree in Reinforcement Learning at Stanford University, advised by Professor Benjamin Van Roy. His main research focus is to understand theoretical and pratical gaps in existing reinforcement learning algorithms when integrated with real-life recommendation systems. He received Master of Science in Computer Science from Stanford University in 2019, which was also completed while working full-time at Facebook (Meta). He received Bachelor of Science in Computer Science with a Minor in Finance, summa cum laude, from Duke University in 2017 within 3 years. He has been the recipient of the Alex Vasilos Memorial Award and the Highest Distinction Graduate Award from Duke University and Ericsson BUSS Shanghai Quarterly Technical Award.
Professional Experience
Engineering Manager - Head of Applied Reinforcement Learning, Facebook (Meta) AI, 2021 - now
Engineering Manager / Tech Lead, Ads Growth Machine Learning, Facebook (Meta), 2018 - 2021
Machine Learning Engineer, Ads Growth Machine Learning, Facebook (Meta), 2017 - 2018
Selected Publicly Available Research
Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning. ArXiv Link, 2023
Ruiyang Xu*, Jalaj Bhandari*, Dmytro Korenkevych, Fan Liu, Yuchen He, Alex Nikulkov, Zheqing Zhu (PI)IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control. ArXiv Link, 2023
Rohan Chitnis*, Yingchen Xu*, Bobak Hashemi, Lucas Lehnert, Urun Dogan, Zheqing Zhu (Secondary PI), Olivier DelalleauScalable Neural Contextual Bandit for Recommender Systems. (ArXiv Coming Soon), 2023
Zheqing Zhu, Benjamin Van RoyOptimism Based Exploration in Large-Scale Recommender Systems. ArXiv Link, 2023
Hongbo Guo, Ruben Naeff, Alex Nikulkov, Zheqing Zhu (PI)Deep Exploration for Recommendation Systems. ArXiv Link, 2021
Zheqing Zhu, Benjamin Van RoyMulti-Agent Safe Planning with Gaussian Processes. ArXiv Link, IROS 2020
Zheqing Zhu, Erdem Biyik, Dorsa Sadigh
Education
PhD, Reinforcement Learning, Stanford University, 2023 (expected), Advisor: Benjamin Van Roy
MS, Computer Science, Stanford University, 2019
BS, Computer Science, summa cum laude, Duke University, 2017, Advisor: Ronald Parr
Honors
CMO's Highlight Launch List, Facebook (Meta), 2021
Win of Month / Win of Quarter, Ads Growth, Facebook (Meta), 2017-2021 (Multi-time Winner)
Alex Vasilos Memorial Award, Duke University, 2017
Gradudate with Highest Distinction, Duke University, 2017
Ericsson BUSS Shanghai Quarterly Technical Award, 2015
Community Services
Workshop Chair of AAAI 2023 Reinforcement Learning Ready for Production Workshop
Reviewer: NeurIPS, AAAI, MLJ