Hi, I'm a Ph.D. student in the computer science department of Tulane University.  My research interests are:

Reinforcement Learning, IoT and Game Theory.

Welcome to my webpage!


Research Project

08/2018 - 06/2021

AI for security

SaTC: CORE: Small: Towards Robust Moving Target Defense: A Game Theoretic and Learning Approach

The proposed research contributes to the emerging field of the science of security via a cross-disciplinary approach that combines techniques from cybersecurity, game theory, and machine learning.

08/2019 - 06/2020

AI for society

Dynamic Mechanisms for a Safer and More Flexible Ride-Sharing System

In this project, we will combine the classic economics models with model-free learning approaches to design more practical and explainable dynamic mechanisms for ride-sharing systems.


AI for business




09/2011 - 06/2015

Xi’an Jiaotong-Liverpool University​

BSc Applied Mathematics

09/2015 - 12/2016

University of Liverpool

MSc Computer Science

03/2018 - present

Tulane University

​Ph.D. Computer Science




Teaching Assistance




Henger Li, Zizhan Zheng

Optimal Timing of Moving Target Defense: A Stackelberg Game Model. Military Communications Conference (MILCOM) 2019

As an effective approach to thwarting advanced attacks, moving target defense (MTD) has been applied to various domains. Previous works on MTD, however, mainly focus on deciding the sequence of system configurations to be used and have largely ignored the equally important timing problem. Given that both the migration cost and attack time vary over system configurations, it is crucial to jointly optimize the spatial and temporal decisions in MTD to better protect the system from persistent threats. In this work, we propose a Stackelberg game model for MTD where the defender commits to a joint migration and timing strategy to cope with configuration-dependent migration cost and attack time distribution. The defender's problem is formulated as a semi-Markovian decision process and a nearly optimal MTD strategy is derived by exploiting the unique structure of the game.


© 2019 By Henger Li.

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