Faculty Profile

Maxim Raginsky

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

Administrative Titles

  • William L. Everitt Fellow in Electrical and Computer Engineering

Affiliation

  • Electrical and Computer Engineering

Primary Research Area

  • Control

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

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.

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

  • Control
  • Decentralized and distributed control
  • Dynamic games and decision theory
  • Information theory
  • Machine learning and pattern recognition
  • Random processes
  • Stochastic systems and control

Chapters in Books

  • Aryeh Kontorovich and Maxim Raginsky, "Concentration of measure without independence: a unified approach via the martingale method," in IMA Volume "Concentration, Convexity, and Discrete Structures" (Springer, 2017)

Monographs

  • Maxim Raginsky and Igal 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; 2nd edition, 2014

Selected Articles in Journals

  • Aolin Xu and Maxim Raginsky, "Information-theoretic lower bounds for distributed function computation," IEEE Transactions on Information Theory, vol. 63, no. 4, pp. 2314-2337, 2017
  • Aolin Xu and Maxim Raginsky, "Information-theoretic lower bounds on Bayes risk in decentralized estimation," IEEE Transactions on Information Theory, vol. 63, no. 3, pp. 1580-1600, 2017
  • Soomin Lee, Angelia Nedich, and Maxim Raginsky, "Stochastic dual averaging for decentralized online optimization on time-varying communication graphs," to appear in IEEE Transactions on Automatic Control
  • Soomin Lee, Angelia Nedich, and Maxim Raginsky, "Coordinate dual averaging for decentralized online optimization with nonseparable global objectives," to appear in IEEE Transactions on Control of Networked Systems
  • Maxim Raginsky, "Strong data processing inequalities and Phi-Sobolev inequalities for discrete channels," IEEE Transactions on Information Theory, vol. 62, no. 6, pp. 3355-3389, 2016
  • Ehsan Shafieepoorfard, Maxim Raginsky, and Sean P. Meyn, ￿Rationally inattentive control of Markov processes,￿ SIAM Journal on Control and Optimization, vol. 54, no. 2, pp. 987-1016, 2016
  • Maxim Raginsky and Angelia Nedich, ￿Online discrete optimization in social networks in the presence of Knightian uncertainty,￿ Operations Research, vol. 64, no. 3, pp. 662-679, 2016 (special issue on Information and Decisions in Social and Economic Networks)
  • Mehmet A. Donmez, Maxim Raginsky, and Andrew C. Singer, "Online optimization under adversarial perturbations,￿ IEEE Journal on Selected Topics in Signal Processing, vol. 10, no. 2, pp. 256￿269, 2016
  • Richard S. Laugesen, Prashant G. Mehta, Sean P. Meyn, and Maxim Raginsky, ￿Poisson￿s equation in nonlinear filtering,￿ SIAM Journal on Control and Optimization, vol. 53, no. 1, pp. 501￿525, 2015
  • Peng Guan, Maxim Raginsky, and Rebecca Willett, "Online Markov decision processes with Kullback-Leibler control cost," IEEE Transactions on Automatic Control, vol. 59, no. 6, pp. 1423￿1438, 2014
  • 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 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

Articles in Conference Proceedings

  • Yanina Shkel, Maxim Raginsky, and Sergio Verd￿niversal lossy compression under logarithmic loss," accepted to IEEE International Symposium on Information Theory, 2017
  • Naci Saldi, Tamer Basar, and Maxim Raginsky, "Markov-Nash equilibria in mean-field games with discounted cost," accepted to American Control Conference, 2017
  • Ehsan Shafieepoorfard and Maxim Raginsky, "Sequential empirical coordination under an output entropy constraint," IEEE Conference on Decision and Control, 2016
  • Peng Guan, Maxim Raginsky, Rebecca Willett, and Daphney-Stavroula Zois, "Regret minimization algorithms for single-controller zero-sum stochastic games," IEEE Conference on Decision and Control, 2016
  • Mehmet Donmez, Maxim Raginsky, Andrew Singer, and Lav R. Varshney, "Cost-performance tradeoffs in unreliable computation architectures," Asilomar Conference on Signals, Systems, and Computers, 2016
  • Daphney-Stavroula Zois and Maxim Raginsky, "Active object detection on graphs via locally informative trees," IEEE International Workshop on Machine Learning for Signal Processing, 2016
  • Maxim Raginsky, Alexander Rakhlin, Matthew Tsao, Yihong Wu, and Aolin Xu, "Information-theoretic analysis of stability and bias of learning algorithms," IEEE Information Theory Workshop, 2016
  • Maxim Raginsky, "Channel polarization and Blackwell measures," IEEE International Symposium on Information Theory, 2016
  • Jaeho Lee, Maxim Raginsky, and Pierre Moulin, ￿On MMSE estimation from quantized observations in the nonasymptotic regime,￿ IEEE International Symposium on Information Theory, 2015
  • Peng Guan, Maxim Raginsky, and Rebecca Willett, "From minimax value to low-regret algorithms for online Markov decision processes," Proceedings of the American Control Conference, 2014
  • 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
  • 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
  • Maxim Raginsky and Alexander Rakhlin, "Lower bounds for passive and active learning," Advances in Neural Information Processing 24, pp. 1026-1034, 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 Y￿ "Online convex programming and regularization in adaptive control," Proceedings of the IEEE Conference on Decision and Control, 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

  • Associate editor for IEEE Transactions on Network Science and Engineering
  • Editorial board member of Foundations and Trends in Communications and Information Theory

Teaching Honors

  • UIUC List of Teachers Ranked as Excellent (Fall 2013, Fall 2014, Fall 2016)

Research Honors

  • NSF CAREER Award (2013)

Other Honors

  • William L. Everitt Fellow in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (2017-present)