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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 equilibria 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.