The symposium showcases research and encourages industry interactions through doctoral student and faculty presentations, focused discussion groups, and networking activities. For details, see www.seas.upenn.edu/research-at-penn/

COMPUTER SCIENCE, ECONOMICS, AND THE EFFECTS OF NETWORK STRUCTURE
The increasing instrumentation of human and organizational behaviors, via technologies such as the Internet and instant messaging, has led to revolutions of measurement and theory in both computer science and the social sciences. It has also led to an accelerated convergence of interests between the two communities. Areas such as social network theory contain a healthy mix of researchers with computational and sociological concerns, and have resulted in mathematical models of network formation equally applicable to the growth of technological, biological, and human systems.

Many of the most compelling pieces of this convergence lie at the intersection of computer science and economics. The flow of ideas here is notably bi-directional, with computer science contributing new modeling and algorithmic tools to the study of complex game-theoretic and economic systems, and economics providing powerful new ways of thinking about computational issues such as spam and resource management in the Internet. At the core of such efforts is the realization that many of our most beloved and reviled technological systems are, at heart, economic systems, and show all of the associated behaviors: mixtures of competition and cooperation, adaptivity, free riding and tragedies of the commons, and many others.

In this talk I will survey the main trends in this line of thought, and illustrate with several examples, including modeling the effects of network structure on economic equality, and viewing Internet routing and airline baggage security as large-population games.

MICHAEL KEARNS
Dr. Michael Kearns received his Ph.D. in Computer Science from Harvard University in 1989, where his dissertation, “The Computational Complexity of Machine Learning,” won a Distinguished Dissertation Award from the Association for Computing Machinery and was published by the MIT Press. Following postdoctoral fellowships at MIT and the University of California at Berkeley, he spent a decade in basic research at Bell Laboratories and AT&T Laboratories, where he headed the Artificial Intelligence and Machine Learning research department.

He joined the Penn faculty in Computer and Information Science in 2002. He is co-director of Penn’s Institute for Research in Cognitive Science, and holds a secondary appointment in the Operations and Information Management department of the Wharton School. He has recently been named the National Center Professor of Resource Management and Technology.

Dr. Kearns’ main research interests lie in artificial intelligence, machine learning, and theoretical computer science. For the past several years he has been particularly active in research at the intersection of computer science, economics, and game theory, as well as in topics in computational finance. His recent work has been concerned with network structure in large economic and strategic models of interaction, including how such structure can be exploited algorithmically, and how it influences equilibrium outcomes. In addition to mathematical results, he has begun applying the resulting methods to numerical models derived from interesting real-world data sets.

Dr. Kearns has recently designed the new undergraduate course, “Networked Life,” open to all majors and levels at Penn, which examines a rich mixture of topics in social network theory, economics, mathematics, and computer science. He is co-author with U.V. Vazirani of the book, An Introduction to Computational Learning Theory, published by the MIT Press in 1994.

Dr. Kearns is a Fellow of the American Association for Artificial Intelligence (AAAI), has served as program chair for many of the major international conferences on AI and machine learning, and has served on the editorial boards of the Journal of the ACM, SIAM Journal on Computing, Mathematics of Operations Research, Machine Learning, and many other journals.

GEORGE H. HEILMEIER FACULTY AWARD FOR EXCELLENCE IN RESEARCH
The School of Engineering and Applied Science established this award for the purpose of encouraging and recognizing excellence in scholarly activities of the faculty. Named in honor of George H. Heilmeier, it recognizes his extraordinary research career, his leadership in technical innovation and public service, and his loyal and steadfast support of Penn Engineering.

PREVIOUS AWARD WINNERS
Raymond J. Gorte
David E. Luzzi
Dennis E. Discher and Daniel A. Hammer