Michael Kearns (computer scientist)

Michael Kearns is an American computer scientist specializing in machine learning, algorithmic game theory, and social network analysis. He is the National Center Professor of Management & Technology in the Department of Computer and Information Science at the University of Pennsylvania. He holds secondary appointments in the departments of Economics and Networked and Social Systems Engineering (NETS).

Kearns is known for his contributions to the theoretical foundations of machine learning, particularly in the areas of boosting, statistical query learning, and the Probably Approximately Correct (PAC) learning framework. His work has explored the limits of what can be learned from data efficiently.

In algorithmic game theory, Kearns has studied topics such as the computational complexity of finding Nash equilibria, the price of anarchy, and mechanism design. His research often focuses on situations where incentives and computation interact.

Kearns has also made significant contributions to the field of social network analysis, focusing on developing models for understanding the structure and dynamics of social networks. This work has included models of information diffusion, network formation, and community structure. He is the author of the book "Who's Watching You?: The Dangers of the New Surveillance Society".

Kearns received his Ph.D. in Computer Science from Harvard University in 1989. He is a Fellow of the Association for Computing Machinery (ACM) and the American Academy of Arts and Sciences. He has received numerous awards for his research, including the ACM SIGecom Test of Time Award and the ACM SIGKDD Innovation Award. He has also served on several advisory boards and committees related to technology policy and research.