Recent Results on Pattern Maximum Likelihood
Sep 26, 2011
from 01:00 PM to 02:00 PM
|Where||54-134 Engineering IV Building|
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University of California, San Diego
Pattern maximum likelihood (PML) is a technique for estimating probabilities when the sample size is small relative to the underlying alphabet size. We will review recent results on PML's efficacy, properites, applications, and computation. In the process we will encounter a math-olympiad problem and the Newton-Girard Formulae. The talk is self contained and based on work with Jayadev Acharya, Hirakendu Das, and Shengjun Pan.
Alon Orlitsky received B.Sc. degrees in Mathematics and Electrical Engineering from Ben Gurion University in 1980 and 1981, and M.Sc. and Ph.D. degrees in Electrical Engineering from Stanford University in 1982 and 1986.
From 1986 to 1996 he was with the Communications Analysis Research Department of Bell Laboratories. He spent the following year as a quantitative analyst at D.E. Shaw and Company, an investment firm in New York City. In 1997 he joined the University of California, San Diego, where he is currently a professor of Electrical and Computer Engineering and of Computer Science and Engineering, and directs the Information Theory and Applications Center and the Center for Wireless Communications. His research concerns information theory, statistical modeling, machine learning, and speech recognition.
Alon is a recipient of the 1981 ITT International Fellowship and the 1992 IEEE W.R.G. Baker Paper Award, and co-recipient of the 2006 Information Theory Society Paper Award. He co-authored two papers for which his students received student-paper awards: the 2003 Capocelli Prize and the 2010 ISIT Student Paper Award. He is a fellow of the IEEE, and holds the Qucalcomm Chair for Information Theory and its Applications at UCSD.
For additional information please contact Prof Puneet Gupta (firstname.lastname@example.org), Prof Suhas Diggavi (email@example.com)