Michael Kearns (computer scientist) Michael Kearns is an American computer scientist , professor and National Center Chair at the University of Pennsylvania , the founding director of Penn's Singh Program in Networked & Social Systems Engineering, the founding director of , and also holds secondary appointments in Penn's Wharton School and department of Economics. He is a leading researcher in computational learning theory and algorithmic game theory , and interested in machine learning , artificial intelligence , computational finance , algorithmic trading , computational social science and social networks. He leads the Advisory and Research function in Morgan Stanley's Artificial Intelligence Center of Excellence team.Biography Kearns was born into an academic family, where his father is Professor Emeritus at University of California, San Diego in chemistry , who won Guggenheim Fellowship in 1969, and his uncle is Professor Emeritus at Amherst College in Philosophy and Law, Jurisprudence, and Social Thought . His paternal grandfather Clyde W. Kearns was a pioneer in insecticide toxicology and was a professor at University of Illinois at Urbana–Champaign in Entomology, and his maternal grandfather was a professor at Pomona College in history and literature , who was born in Canton into a family noted for their scholarship and educational leadership. In the growth and development of Asian Studies on the West Coast, the Claremont Colleges and Professor Chen occupy a leading place. Kearns received his B.S. degree at the University of California at Berkeley in math and computer science in 1985, and Ph.D. in computer science from Harvard University in 1989, under the supervision of Turing award winner Leslie Valiant . His doctoral dissertation was , later published by MIT press as part of the ACM in 1990. Before joining AT&T Bell Labs in 1991, he continued with postdoctoral positions at the Laboratory for Computer Science at MIT hosted by Ronald Rivest , and at the International Computer Science Institute in UC Berkeley hosted by Richard M. Karp, both of whom are Turing award winners. Kearns is currently a full professor and National Center Chair at the University of Pennsylvania, where his appointment is split across the Department of Computer and Information Science , and Statistics and Operations and Information Management in the Wharton School. Prior to joining the Penn faculty in 2002, he spent a decade in AT&T Labs and Bell Labs , including as head of the AI department with colleagues including Michael L. Littman, David A. McAllester, and Richard S. Sutton; Secure Systems Research department; and Machine Learning department with members such as Michael Collins and the leader . Other AT&T Labs colleagues in included Yoav Freund , Ronald Graham , Mehryar Mohri , Robert Schapire , and Peter Shor , as well as Sebastian Seung , Yann LeCun , Corinna Cortes , and Vladimir Vapnik . Kearns was named Fellow of the Association for Computing Machinery for contributions to machine learning, and a fellow of the American Academy of Arts and Sciences . His former graduate students and postdoctoral visitors include and John Langford . Kearns' work has been reported by media, such as MIT Technology Review , Bloomberg News and NPR audio .Academic life Computational learning theory Kearns and Umesh Vazirani published An introduction to computational learning theory , which has been a standard text on computational learning theory since it was published in 1994.Weak learnability and the origin of Boosting algorithms The question "is weakly learnability equivalent to strong learnability?" posed by Kearns and Valiant is the origin of boosting machine learning algorithms , which got a positive answer by Robert Schapire and Yoav Freund and then they developed the practical AdaBoost , an adaptive boosting algorithm that won the prestigious Gödel Prize .Honors and awards 2014. ACM Fellow. 2012. American Academy of Arts and Sciences Fellow.Selected works 1994. An introduction to computational learning theory .. MIT press. 1990. . MIT press. 1989. limitations on learning Boolean formulae and finite automata . Proceedings of the .
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