Center of Wireless Communications
California Institute for Telecommunications and Information Technology
Information theory, data compression, communications, machine learning, speech recognition, signal processing
Alon's research concerns concrete scenarios in communication, machine learning, and modeling. He is most intrigued by problems that high-schoolers could pose but Ph.D.'s might need to resolve. Following are two examples of problems he has studied.
Communication: How many bits must two parties transmit in order to convey or jointly process their information, and how does this number change when the parties can interact? Sample results: Transmitting information using a single message (one-way communication) may be exponentially wasteful but two messages are always within a factor of four and four messages are always within a factor of two from optimal.
Data compression: Can information over large alphabets be optimally compressed when the underlying distribution is unknown? Sample results: While it was long known that as the alphabet size increases, the compression penalty for not knowing the underlying distribution goes to infinity even for iid or limited-state hidden markov distributions, the patterns of these distributions can always be efficiently compressed, and at least for English text, so can the remaining information.
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 at 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.
Alon is a recipient of the 1981 ITT international fellowship, the 1992 IEEE W.R.G. Baker best-paper award, and the 2006 IEEE Information Theory paper award (with Prasad Santhanam and Junan Zhang). He is a Fellow of the IEEE.