Maxim Raginsky Research Scientist Department of Electrical and Computer Engineering Edmund T. Pratt School of Engineering Duke University 3424 CIEMAS Box 90291 Durham, NC 27708 Phone: 919 613 9123 E-mail: m.raginsky [at] duke [dot] edu

I am interested in various problems at the intersection of signal processing, statistical learning, and information theory. Broadly speaking, signal processing is concerned with design and analysis of efficiently implementable and robust schemes for extraction of information from data; statistical learning deals with building automated systems capable of discovering relationships among and patterns in data they observe with very little prior knowledge and using these discoveries to continually refine their performance; and information theory is aimed at characterizing the fundamental limitations of systems for transmission and processing of information, as well as at devising schemes that come close to these limits. Many problems we are facing today involve processing large volumes of data under constraints on various resources (such as computation or communication) in uncertain and dynamically changing environments. This calls for innovative solutions that integrate the techniques and insights provided by the above three disciplines.

Take a look at my publications.

Maxim Raginsky received the B.S. and M.S. degrees in 2000 and the Ph.D. degree in 2002 from Northwestern University, Evanston, IL, all in Electrical Engineering. From 2002 to 2004 he was a Postdoctoral Researcher at the Center for Photonic Communication and Computing at Northwestern University, where he pursued work on quantum cryptography and quantum communication and information theory. From 2004 to 2007 he was a Beckman Foundation Postdoctoral Fellow at the University of Illinois in Urbana-Champaign, where he carried out research on information theory, statistical learning and computational neuroscience. He has joined the Department of Electrical and Computer Engineering at Duke University in the Fall of 2007 as a research scientist.