Haipeng Luo
Haipeng Luo is a computer scientist who works on online learning, bandit algorithms and reinforcement learning for AI systems that learn from sequential interaction. He studied at Peking University and earned a Princeton PhD in 2016 under Rob Schapire, and two papers he coauthored won best paper awards at ICML 2015 and NIPS 2015. After a year as a postdoctoral researcher at Microsoft Research in New York City, he joined the University of Southern California as an assistant professor in 2017, won an NSF CAREER Award in 2020, and won the COLT 2021 best paper award with his student Chen-Yu Wei for a black-box method for reinforcement learning in non-stationary environments. He is an associate professor and IBM Early Career Chair in the Thomas Lord Department of Computer Science at USC, where his research aims to develop the foundations of continually learning AI systems.
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Timeline
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2007–2016
Peking University and Princeton
He studies at Peking University from 2007 to 2011. At Princeton, he is advised by Rob Schapire and works closely with Elad Hazan, and his 2016 PhD dissertation is titled Optimal and Adaptive Online Learning.
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2015
Two best paper awards
In 2015, he coauthors two best-paper winners. Optimal and Adaptive Algorithms for Online Boosting, written with Alina Beygelzimer and Satyen Kale, wins at ICML 2015, and Fast Convergence of Regularized Learning in Games, written with Vasilis Syrgkanis, Alekh Agarwal and Robert Schapire, wins at NIPS 2015.
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2016–2023
Microsoft Research, then USC
He spends a year as a postdoctoral researcher at Microsoft Research in New York City, from July 2016 to June 2017. In July 2017, he joins the Department of Computer Science at the University of Southern California as an assistant professor, a post he holds until February 2023. His research there centers on online learning, bandit algorithms and reinforcement learning, and his PhD student Chen-Yu Wei, who works with him from 2017 to 2022, becomes an assistant professor at the University of Virginia.
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2018
COLT best student paper
In 2018, he wins the COLT Best Student Paper Award with Dylan Foster, Satyen Kale, Mehryar Mohri and Karthik Sridharan for Logistic Regression: The Importance of Being Improper.
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2020–2022
NSF CAREER and Google awards
He receives an NSF CAREER Award and a Google Faculty Research Award in 2020. In 2022, he receives a Google Research Scholar Award.
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2021
A COLT 2021 best paper
In 2021, he and PhD student Chen-Yu Wei win the COLT best paper award for Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach. The method turns any stationary reinforcement learning algorithm into one that learns optimally in a non-stationary environment.
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Since February 2023
Associate professor and IBM chair
He becomes an associate professor at USC in February 2023 and holds the IBM Early Career Chair in the Thomas Lord Department of Computer Science. His research aims to develop the foundations of continually learning AI systems, in which agents acquire knowledge through sequential interaction and adapt to non-stationary or even adversarial environments.
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July 2025–July 2026
Google visiting faculty researcher
He is a Google Visiting Faculty Researcher from July 2025 to July 2026, after serving as an Amazon Visiting Academic from September 2022 to January 2025.
Career
- Since 2017University of Southern CaliforniaAssistant Professor, Associate Professor and IBM Early Career Chair
- 2025–2026GoogleVisiting Faculty Researcher
- 2022–2025AmazonVisiting Academic
- 2016–2017Microsoft ResearchPostdoctoral Researcher
Education
- 2016Princeton UniversityPhD
- 2011Peking University
Sources and credits
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- Haipeng Luo: Biography
- Haipeng Luo
- Haipeng Luo - The Mathematics Genealogy Project
- Awards | ICML Lille
- NIPS 2015 Awards
- Best Paper Award at ICML for Haipeng Luo | CS
- Haipeng Luo - USC Viterbi Faculty Directory
- Haipeng Luo - Simons Institute for the Theory of Computing
- Haipeng Luo: Group Members
- Haipeng Luo: Awards
- Bringing Reinforcement Learning to a Dynamic World: USC Computer Scientists Win Best Paper Award
- COLT 2021 - Paper Awards


