Many real-world problems require the creation of robust AI models that include both learning and planning for an agent (or a team of agents) in interaction with adversaries in a multi-agent environment. In such a complex setting, it is important to predict strategic behavior of the adversaries, as well as to anticipate potential adversarial manipulations that could deteriorate the learning outcomes and the decision quality of our agents. In this talk, I will discuss the challenges of modeling adversaries’ decision making and the security of machine learning in data-driven multi-agent competitive environments. I will present our algorithms to address these challenges that explore techniques in reinforcement learning, game theory, and optimization research. In addition, I will introduce some of the real-world applications of our algorithms in the domains of wildlife protection and public health.   Bio: Thanh Nguyen is an Assistant Professor in the Computer Science department at the University of Oregon (UO). Prior to UO, she was a postdoc at the University of Michigan and earned her PhD in Computer Science from the University of Southern California. Thanh’s work in the field of Artificial Intelligence is motivated by real-world societal problems, particularly in the areas of Public Safety and Security, Conservation, and Public Health. She brings together techniques from multi-agent systems, reinforcement learning, and game theory to solve problems in those areas, with the focus on studying adversary behavioral learning and deception in competitive multi-agent environments. Thanh’s work has been recognized by multiple awards, including the IAAI-16 Deployed Application Award, and the AAMAS-16 Runner-up of the Best Innovative Application Paper Award. Her works in wildlife protection and public health were evaluated and/or deployed in multiple countries around the world.