My research group is interested in the broad areas of information processing, transmission, and learning. We particularly enjoy analyzing simple models that require mathematical techniques from diverse areas to obtain insights for practical system/algorithm design.
Network Information Theory: A major focus of our work in this area is on the development of new analytical techniques for characterizing the fundamental performance limits of multiterminal networks.
Wireless Communications: Our goal is to establish architectural principles for the design of communication systems through the analysis of the relevant channel models. We are particularly interested in the scenarios where the transmitter side information and the receiver side information are not deterministically related.
Multimedia Signal Processing: We are currently designing a new predictive coding architecture based on the concept of virtual data and exploring its application to video compression and video descriptor compression. We are also interested in developing machine learning techniques for video analysis.
Did you know?
Dr. Chen served as an Associate Editor for the IEEE Transactions on Information Theory from 2014 to 2016
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Jun Chen received the B.E. degree in communication engineering from Shanghai Jiao Tong University, Shanghai, China, in 2001, and the M.S. and Ph.D. degrees in electrical and computer engineering from Cornell University, Ithaca, NY, USA, in 2004 and 2006, respectively.
From September 2005 to July 2006, he was a Post-Doctoral Research Associate with the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA, and a Post-Doctoral Fellow with the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA, from July 2006 to August 2007. Since September 2007, he has been with the Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada, where he is currently a Professor. His research interests include information theory, machine learning, wireless communications, and signal processing.
He received several awards for his research, including the Josef Raviv Memorial Postdoctoral Fellowship in 2006, the Early Researcher Award from the Province of Ontario in 2010, and the IBM Faculty Award in 2010. He served as an Associate Editor for the IEEE Transactions on Information Theory from 2014 to 2016.
B.Eng. (Shanghai Jiao Tong University, China); M.S. (Cornell University, USA); Ph.D. (Cornell University, USA)
Josef Raviv Memorial Postdoctoral Fellowship (2006)
Barber-Gennum Chair in Information Technology (2008 – 2013)
Early Researcher Award (2010)
IBM Faculty Award (2010)
Joseph Ip Distinguished Engineering Fellow (2015-2018)
Dean’s Doctoral Mentoring Platinum Honor Roll
ECE Instructor Award
Recent
Tian, C., Chen, J., Diggavi, S., and Shamai, S., Matched Multiuser Gaussian Source Channel Communications via Uncoded Schemes, IEEE Transactions on Information Theory, vol. 63, pp. 4155-4171, Jul. 2017
Xu, R., Chen, J., Weissman, T., and Zhang, J., When is Noisy State Information at the Encoder as Useless as No Information or as Good as Noise-Free State?, IEEE Transactions on Information Theory, vol. 63, pp. 960-974, Feb. 2017
Zhou, Y., Xu, Y., Yu, W., and Chen, J., On the Optimal Fronthaul Compression and Decoding Strategies for Uplink Cloud Radio Access Networks, IEEE Transactions on Information Theory, vol. 62, pp. 7402-7418, Dec. 2016
Khezeli, K. and Chen, J., A Source-Channel Separation Theorem With Application to the Source Broadcast Problem, IEEE Transactions on Information Theory, vol. 62, pp. 1764-1781, Apr. 2016
Xiao, Z., Chen, J., Li, Y., and Wang, J., Distributed Multilevel Diversity Coding, IEEE Transactions on Information Theory, vol. 61, pp. 6368-6384, Nov. 2015
Khezeli, K. and Chen, J., Outer Bounds on the Admissible Source Region for Broadcast Channels with Correlated Sources, IEEE Transactions on Information Theory, vol. 61, pp. 4616-4629, Sep. 2015
Song, L., Chen, J., and Tian, C., Broadcasting Correlated Vector Gaussians, IEEE Transactions on Information Theory, vol. 61, pp. 2465-2477, May 2015
3 unit(s) S. Hranilovic Entropy and mutual information. Discrete memoryless channels and discrete memoryless sources, capacity-cost functions and rate-distortion functions. The Gaussian channel and source. The source-channel coding theorem. Linear codes.BCH, Goppa, Reed-Solomon, and Golay codes.Convolutional codes.Variable-length source coding.
Review of continuous-time signals and systems; amplitude modulation, phase and frequency modulation schemes; digital modulation; stochastic processes; noise performance. Three lectures, one tutorial, one lab every other week; second term Prerequisite(s): ELECENG 3TP4 or ENGPHYS 3W04; One of ELECENG 3TQ4, 3TQ3 or STATS 3Y03; or ENGPHYS 3W04 A/B
