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While Professor Orlitsky is a theorist, his research deals with concrete communication scenarios. Orlitsky is actively studying interactive communication: if sender and receiver interact, they can reduce the number of bits that must be transmitted. That's essentially what happens when you view a Web page for which you have downloaded a previous version; the server doesn't have to send all the bits each time. Orlitsky also studies "communication complexity." How many bits must two communicators exchange to determine if their versions of a file are identical? Or two seismic stations to determine the location of an earthquake? His work in speech processing and recognition is of both practical and theoretical interest. Orlitsky is looking at information-theoretic approaches to speech processing, touching on issues including recognition, compression, and synthesis. His work in learning theory is primarily about online learning, and he maintains an interest in investment theory based on the work he did as a quantitative analyst for a Wall Street investment firm (see bio).Orlitsky teaches information theory and algebraic coding in ECE, and discrete mathematics in the Computer Science and Engineering department.

Capsule Bio:

Alon Orlitsky joined the UCSD faculty in 1997. He is the founding director of UCSD's Information Theory and Applications Center and holds the QUALCOMM Endowed Chair in Information Theory and Its Applications. Before joining UCSD, Orlitsky served two years as a quantitative analyst for D.E. Shaw and Co. From 1986-1996, he was a member of the technical staff at AT&T Bell Labs' Mathematical Sciences Research Center. Orlitsky received his Ph.D. in Electrical Engineering from Stanford University in 1986, and his M.Sc., also from Stanford, in 1982. He did his undergraduate work in mathematics (B.Sc. 1980) and Electrical Engineering (B.Sc. 1981) at Israel's Ben-Gurion University. His honors include an ITT International Fellowship in 1982, and an IEEE W.R.G. Baker best-paper award in 1992. Orlitsky co-edited a book on "Theoretical advances in neural computation and learning," (1994, Kluwer Academic Publishing).