Modern Applications of AI, Machine Learning, and Computational Game Theory to Security Domains
Tuesday, February 28, 2017
4-5 p.m., Avery 115
3:30 p.m., Avery 348
Hau Chan, Ph.D.Postdoctoral Researcher, Trinity University
Motivated by an increased number of attacks by hackers and terrorists, there has been quite a bit of recent research activity in the general area of game-theoretic models for terrorism settings that aim to understand the behavior of the attackers and the attackers' targets.
In this talk, I will present several important computational-game-theoretic frameworks for real-world settings of security. In addition, I will discuss some computational aspects of computing Nash equilibria (NE) in these games. Finally, I will illustrate the usage of machine-learning techniques that I developed to estimate the parameters and structure of security/vaccination games using the flu vaccination data from Centers for Disease Control and Prevention (CDC).
Hau Chan received his Ph.D. in computer science from Stony Brook University in 2015. He is currently a postdoc in the Computer Science Department at Trinity University. His broad research interests include computational game theory, algorithmic mechanism design, data/graph mining, information network, and discrete mathematics. His work has been published in AAAI, AAMAS, NIPS, IJCAI, UAI, ICDM, ECML/PKDD, SDM, and WINE. Prior to attending Stony Brook University, Hau completed his bachelor degrees in mathematics and computer science at the College of Charleston, where he published a series of journal papers in discrete mathematics (J. of Combinatorial Math. and Combinatorial Comput., Bulletin of ICA, Discrete Math, Ars Combinatoria, Congressus Numerantium). He was a recipient of the NSF Graduate Research Fellowship and the NSF East Asia and Pacific Summer Fellowship. His research works have received best research paper awards at SDM and AAMAS.