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Contact Info

William H. Sanders
Department Head
ECE ILLINOIS
306 N. Wright Street
Urbana, IL 61801
Ph: (217) 333-2300
Fax: (217) 244-7075
whs@illinois.edu

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Maxim Raginsky

Electrical and Computer Engineering
Maxim  Raginsky
Maxim Raginsky
Assistant Professor
162 Coordinated Science Lab MC 228
1308 W. Main St.
Urbana Illinois 61801
(217) 244-1782

Primary Research Area

  • Communications

Education

Ph.D. in Electrical Engineering, Northwestern University, 2002

Biography

Maxim Raginsky received the B.S. and M.S. degrees in 2000 and the Ph.D. degree in 2002 from Northwestern University, all in Electrical Engineering. He has held research positions with Northwestern, the University of Illinois at Urbana-Champaign (where he was a Beckman Foundation Fellow from 2004 to 2007), and Duke University. In 2012, he has returned to the UIUC, where he is currently an Assistant Professor with the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory.

For more information

Prof. Raginsky's Home Page

Research Statement

Prof. Raginsky is interested in understanding, modeling and analyzing complex systems that have capabilities for sensing, communication, adaptation, and decision-making and can operate effectively in uncertain and dynamic environments. In his research he examines new angles and perspectives at the interface between information theory, learning, optimization, and control. He uses insights and techniques from these disciplines to develop robust and fast schemes for compression, transmission and processing of information that not only deliver the data reliably from one point to another, but must preserve only those features that are relevant for inference, recognition or control tasks. Much of his work is motivated by current theoretical and practical challenges in such fields as computer vision, neuroscience, and machine learning.

Research Interests

  • information processing and decision-making in uncertain environments under resource and complexity constraints
  • information theory
  • statistical machine learning
  • game theory and stochastic control
  • optimization

Research Areas

Monographs

M. Raginsky and I. Sason, "Concentration of measure inequalities in information theory, communications and coding," Foundations and Trends in Communications and Information Theory, vol. 10, issues 1 and 2, pp. 1-246, 2013

Selected Articles in Journals

  • Peng Guan, Maxim Raginsky, and Rebecca Willett, "Online Markov decision processes with Kullback-Leibler control cost," accepted to IEEE Transactions on Automatic Control, 2014
  • Maxim Raginsky, Jorge Silva, Svetlana Lazebnik, and Rebecca Willett, "A recursive procedure for density estimation on the binary hypercube," Electronic Journal of Statistics, vol. 7, pp. 820-858, 2013
  • Maxim Raginsky, "Empirical processes, typical sequences, and coordinated actions in standard Borel spaces," IEEE Transactions on Information Theory, vol. 59, no. 3, pp. 1288-1301, 2013
  • Maxim Raginsky, Rebecca Willett, Corinne Horn, Jorge Silva and Roummel Marcia, "Sequential anomaly detection in the presence of noise and limited feedback," IEEE Transactions on Information Theory, vol. 58, no. 8, pp. 5544-5562, 2012
  • Maxim Raginsky and Alexander Rakhlin, "Information-based complexity, feedback and dynamics in convex programming," IEEE Transactions on Information Theory, vol. 57, no. 10, pp. 7036-7056, 2011
  • Svetlana Lazebnik and Maxim Raginsky, "Supervised learning of quantizer codebooks by information loss minimization," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 7, 1294-1309, 2009
  • Maxim Raginsky, "Joint universal lossy coding and identification of stationary mixing sources with general alphabets," IEEE Transactions on Information Theory, vol. 55, no. 5, pp. 1945-1960, 2009
  • Avon Fernandes, Maxim Raginsky and Todd Coleman, "A low-complexity universal scheme for rate-constrained distributed regression using a wireless sensor network," IEEE Transactions on Signal Processing, vol. 57, no. 5, 1731-1744, 2009
  • Maxim Raginsky, "Joint fixed-rate universal lossy coding and identification of continuous-alphabet memoryless sources," IEEE Transactions on Information Theory, vol. 54, no. 7, pp. 3059-3077, 2008

Articles in Conference Proceedings

  • Maxim Raginsky and Angelia Nedich, "Online discrete optimization in social networks," accepted to American Control Conference, 2014
  • Peng Guan, Maxim Raginsky, and Rebecca Willett, "From minimax value to low-regret algorithms for online Markov decision processes," accepted to American Control Conference, 2014
  • Ehsan Shafieepoorfard and Maxim Raginsky, "Rational inattention in scalar LQG control," Proceedings of IEEE Conference on Decision and Control, 2013
  • Maxim Raginsky, "Learning joint quantizers for reconstruction and prediction," Proceedings of the IEEE Information Theory Workshop, 2013
  • Maxim Raginsky, "Logarithmic Sobolev inequalities and strong data processing theorems for discrete channels," Proceedings of the IEEE International Symposium on Information Theory, 2013
  • Maxim Raginsky and Igal Sason, “Refined bounds on the empirical distribution of good channel codes via concentration inequalities,” Proceedings of the IEEE International Symposium on Information Theory, 2013
  • Ehsan Shafieepoorfard, Maxim Raginsky and Sean Meyn, "Rational inattention in controlled Markov processes," Proceedings of the American Control Conference, 2013
  • Maxim Raginsky and Jake Bouvrie, "Continuous-time stochastic mirror descent on a network: variance reduction, consensus, convergence," Proceedings of IEEE Conference on Decision and Control, 2012
  • Peng Guan, Maxim Raginsky and Rebecca Willett, "Online Markov decision processes with Kullback-Leibler control cost," Proceedings of the American Control Conference, 2012
  • Maxim Raginsky, "Directed information and Pearl's causal calculus," Proceedings of the Forty-Ninth Allerton Conference on Communication, Control, and Computing, 2011
  • Maxim Raginsky and Alexander Rakhlin, "Lower bounds for passive and active learning," Advances in Neural Information Processing 24, pp. 1026-1034, 2011
  • Maxim Raginsky, "Shannon meets Blackwell and Le Cam: channels, codes, and statistical experiments," Proceedings of the IEEE International Symposium on Information Theory, 2011
  • Maxim Raginsky, Nooshin Kiarashi, and Rebecca Willett, "Decentralized online convex programming with local information," Proceedings of the American Control Conference, 2011
  • Maxim Raginsky, "Divergence-based characterization of fundamental limitations of adaptive dynamical systems," Proceedings of the Forty-Eighth Annual Allerton Conference on Communication, Control, and Computing, 2010
  • Maxim Raginsky, Alexander Rakhlin and Serdar Yüksel, "Online convex programming and regularization in adaptive control," Proceedings of the IEEE Conference on Decision and Control, 2010
  • Maxim Raginsky, "Empirical processes and typical sequences," Proceedings of the IEEE International Symposium on Information Theory, 2010
  • Todd Coleman and Maxim Raginsky, "Mutual information saddle points in channels of exponential family type," Proceedings of the IEEE International Symposium on Information Theory, 2010
  • Maxim Raginsky and Svetlana Lazebnik, "Locality-sensitive binary codes from shift-invariant kernels," Advances in Neural Information Processing 22, pp. 1509-1517, 2009
  • Maxim Raginsky, "Achievability results for statistical learning under communication constraints," Proceedings of the IEEE International Symposium on Informaiton Theory, 2009

Journal Editorships

Editorial board member of Foundations and Trends in Communications and Information Theory

Teaching Honors

UIUC List of Teachers Ranked as Excellent (Fall 2013)

Research Honors

NSF CAREER Award (2013)