hli30@tulane.edu +1(504)8927193
Department of Computer Science
School of Science and Engineering
309 Stanley Thomas Hall
New Orleans, LA 70118
About Me
[LinkedIn] [Google Scholar] [GitHub] [Resume]
Hi, I'm a Ph.D. Candidate in the computer science department of Tulane University, advised by Prof. Zizhan Zheng.
My research interests include:
Cybersecurity, Game Theory, and Reinforcement Learning
Welcome to my webpage!
Publications
Henger Li, Xiaolin Sun, and Zizhan Zheng, “Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework,” Conference on Neural Information Processing Systems (NeurIPS), Dec. 2022 (Acceptance Rate = 25.6%, selected for scholar award) [paper][code]
Henger Li and Zizhan Zheng, “Robust Moving Target Defense against Unknown Attacks: A Meta-Reinforcement Learning Approach,” Conference on Decision and Game Theory for Security (GameSec), Oct. 2022. [paper][code]
Henger Li, Wen Shen, and Zizhan Zheng, “Spatial-Temporal Moving Target Defense: A Markov Stackelberg Game Model,” International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), May 2020 (long paper).(Acceptance Rate = 23%) [paper][code]
Wen Shen, Henger Li, and Zizhan Zheng, “Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach,” ICLR 2021 Workshop on Security and Safety in Machine Learning Systems (SecML), May 2021 (selected for travel award). [paper]
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Wen Shen, Henger Li, and Zizhan Zheng, “Learning to Attack Distributionally Robust Federated Learning,” NeurIPS-20 Workshop on Scalability, Privacy, and Security in Federated Learning (NeurIPS-SpicyFL), Dec. 2020 (selected for oral presentation). [paper]
Henger Li and Zizhan Zheng, “Optimal Timing of Moving Target Defense: A Stackelberg Game Model,” Military Communications Conference (MILCOM), Nov. 2019. [paper]
Presentations&Talks
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Tulane Computer Science Department. Oral qualifying presentation: Adaptive Attack and Proactive Defense in Cybersecurity, Virtual 2021. [slide]
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AAMAS 2020. Oral presentation: Spatial-Temporal Moving Target Defense: A Markov Stackelberg Game Model, Virtual 2020. [slide]
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Tulane Computer Science Department. Interdisciplinary project presentation: Learning to pool: Adaptive Group Testing for COVID-19, New Orleans, LA 2020.
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MILCOM 2019. Oral presentation: Optimal Timing of Moving Target Defense: A Stackelberg Game Model, Norfolk, VA, 2019. [slide]
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Education
Ph.D. Candidate in Computer Science (Jun 2021 - present)
Department of Computer Science
Tulane University, New Orleans, USA
Ph.D. Student in Computer Science (Mar 2018 - Jun 2021)
Department of Computer Science
Tulane University, New Orleans, USA
M.S. degree in Computer Science (Sep 2015 - Dec 2016)
Department of Computer Science
University of Liverpool, Liverpool, UK
B.S. degree in Applied Mathematics (Sep 2011 - Jun 2015)
Department of Applied Mathematics
Xi’an Jiaotong-Liverpool University, SuZhou, China & Liverpool, UK
Experience
Researching Assistant (Sep 2018 - present)
Supervisor: Prof. Zizhan Zheng
Tulane University, New Orleans, USA
Research Topic: Cybersecurity, Game Theory, and Reinforcement Learning
Teaching Assistant (Sep 2021 - May 2022)
Supervisor: Prof. Aaron Maus
Tulane University, New Orleans, USA
Courses:
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Introduction to Computer Science Fall 2021
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Introduction to Computer Science Spring 2022
A game I designed to interact with real human players on Amazon Mechanical Turk. Try to beat our algorithm here!