Milind Tambe
Milind Tambe is Helen N. and Emmett H. Jones Professor in Engineering at the University of Southern California(USC) and the Founding Co-Director of CAIS, the USC Center for AI in Society, where his research focuses on "AI for Social Good". He is a fellow of AAAI and ACM, as well as recipient of the ACM/SIGART Autonomous Agents Research Award, Christopher Columbus Fellowship Foundation Homeland security award, INFORMS Wagner prize in Operations Research, Rist Prize of the Military Operations Research Society, IBM Faculty Award, Okawa foundation award, RoboCup scientific challenge award, and other awards including the Orange County Engineering Council Outstanding Project Achievement Award, USC Associates award for creativity in research and USC Viterbi use-inspired research award. Prof. Tambe has contributed several foundational papers in AI in areas such as security games and multiagent systems; these papers have received over a dozen best paper and influential paper awards at conferences such as AAMAS, IJCAI, IAAI and IVA. In addition, Prof. Tambe pioneering real-world deployments of security games has led him and his team to receive meritorious commendations from the US Coast Guard Commandant, LA Airport Police, and the US Federal Air Marshals Service. For his teaching and mentoring Prof. Tambe has received the USC Steven B. Sample Teaching and Mentoring award; to date he has graduated 25 PhD students and mentored 10 postdocs. Prof. Tambe has also co-founded a company based on his research, Avata Intelligence , where he serves as the director of research. Prof. Tambe received his Ph.D. from the School of Computer Science at Carnegie Mellon University.
Multiagent Systems Research for Social Goods: The Role of Bilevel Programming
With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. One key multiagent systems challenge that cuts across multiple of these problem areas is that of effectively deploying limited intervention resources. I will highlight our research advances rooted in computational game theory in addressing this challenge across three key problem areas; and in particular highlight the role of bilevel programming to address Stackelberg game models. First, I will focus on public safety and security, and outline our contribution in introducing and using the Stackelberg security games model for effectively allocating limited security resources. Security games models been used by agencies such as the US Coast Guard, the US Federal Air Marshals Service and others to assist in the protection of ports, airports, flights and other critical infrastructure. Second, I will focus on conservation and illustrate the use of green security games to allocate limited resources in protecting endangered wildlife. Advances in adversary modeling in these games -- models learned from past poaching data -- have helped removal of snares and arrests of poachers in national parks in Uganda, potentially saving endangered animals. Third, for public health, I will outline challenges of using limited resources for spreading health information in low resource communities, and algorithms based on games against nature. Our new algorithms for influence maximization, piloted in homeless shelters in Los Angeles, show significant improvements over traditional methods in harnessing social networks to spread HIV-related information among homeless youth. I will also point to directions for future work, illustrating the significant potential of AI for social good.
