Computer Science Seminar Series: Fall 2017
August 23, 2017, 12:30 pm; Patrick F. Taylor Hall room 3285
Speaker: Luis E. Ortiz, University of Michigan, Dearborn
Title: On Networks and Behavior: Strategic Inference and Machine Learning
Abstract: Studying complex behavior in economic, social, or other similar systems is an important scientific endeavor with potentially direct impact to society via the eventual commercialization of relevant technology. The big-data revolution offers the opportunity to easily collect and process large amounts of data recording system behavior. Yet, our fundamental understanding of real-world complex systems remains slim at best.
Description: In this talk, I will summarize research from my group that takes a modern artificial intelligence (AI), machine learning (ML), and engineering approach to questions about systems in domains where global behavior results from complex local interactions of agents embedded in a network. Our particular interest is interactions resulting from the distributed reasoning and the deliberate decisions of a large number of agents (e.g., a social network). In our work, we seek and provide algorithms that scale polynomially with the number of agents and thus can deal with relatively large systems. I will also illustrate our approach to causal strategic inference and to machine learning from strictly behavioral data in two real-world domains: the U.S. Supreme Court and the U.S. Congress.
Time permitting, I will briefly discuss other applications to policy making within the context of (1) graphical models for competitive networked economies (e.g., based on real-world data on locally available bank interest rates and aggregate loan disbursement amounts to villages from microfinance systems in Bangladesh and Bolivia); and (2) risk analysis in interdependent-security systems (e.g., based on real-world data on flight itineraries, on the Internet network graph, and on publicly-available aggregate data on state-level flu-vaccination rates from the Center for Disease Control and Prevention in the USA).
Biography: Luis E. Ortiz is an assistant professor in the Department of Computer and Information Science at the University of Michigan (UM) - Dearborn. He is affiliated with the Michigan Institute for Data Science (MIDAS), which is part of the broader UM system. Prior to joining UM-Dearborn, Dr. Ortiz was an assistant professor in the Department of Computer Science at Stony Brook University; an assistant professor at the University of Puerto Rico, Mayagüez; a postdoctoral lecturer at MIT; a postdoctoral researcher at the University of Pennsylvania; and a consultant in the field of AI and ML at AT&T Laboratories-Research. He received an Sc.M. degree and a Ph.D. degree in computer science in 1998 and 2001, respectively, both from Brown University. He received a B.S. degree in computer science in 1995 from the University of Minnesota. His main research areas are AI and ML. His current focus is on computational game theory and economics, with applications to the study of influence in strategic, networked, large-population settings, and learning game-theoretic models from data on strategic behavior. Other interests include, game-theoretic models for interdependent security, algorithms for computing equilibrial in games, connections to probabilistic graphical models, and ensemble methods such as AdaBoost. Prof. Ortiz received the NSF CAREER award in 2011. He was a National Physical Science Consortium (NPSC) Ph.D. Fellow and an NSF Minority Graduate Fellow.