My research group is broadly engaged in information theory, machine learning, computer vision, and wireless communications. Our current research projects include: 1) Learned image/video compression and restoration, 2) Compression of large language models, 3) Integrated sensing and communications, 4) Quantum information theory
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Dr. Chen serves as the Chair of the IEEE Hamilton Section’s Joint Chapter for the Communications, Information Theory, and Signal Processing Societies.
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 Postdoctoral Research Associate with the Coordinated Science Laboratory, University of Illinois at Urbana–Champaign, Urbana, IL, USA, and a Postdoctoral 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. Dr. Chen was a recipient of the Josef Raviv Memorial Postdoctoral Fellowship in 2006, the Early Researcher Award from the Province of Ontario in 2010, the IBM Faculty Award in 2010, the ICC Best Paper Award in 2020, and the JSPS Invitational Fellowship in 2021. He held the title of the Barber-Gennum Chair in Information Technology from 2008 to 2013 and the title of the Joseph Ip Distinguished Engineering Fellow from 2016 to 2018. He was an Associate Editor of the IEEE TRANSACTIONS ON INFORMATION THEORY from 2014 to 2016 and an Editor of the IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING from 2020 to 2021. He is currently serving as an Associate Editor for the IEEE TRANSACTIONS ON INFORMATION THEORY and an Associate Editor for the IEEE TRANSACTIONS ON COMMUNICATIONS.
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
1) Chen, J., Yu, L., Wang, J., Shi, W., Ge, Y., and Tong, W., On the Rate-Distortion-Perception Function, IEEE Journal on Selected Areas in Information Theory, vol. 3, pp. 664-673, Dec. 2022
2) Liu, X., Liu, Y., Chen, J., and Liu, X., PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization, IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, pp. 7505-7517, Nov. 2022
3) Wang, Y., Zibaeenejad, A., Jing, Y., and Chen, J., On the Optimality of the Greedy Policy for Battery Limited Energy Harvesting Communications, IEEE Transactions on Information Theory, vol. 67, pp. 6548-6563, Oct. 2021
4) Tian, C., Sun, H., and Chen, J., Capacity-Achieving Private Information Retrieval Codes with Optimal Message Size and Upload Cost, IEEE Transactions on Information Theory, vol. 65, pp. 7613-7627, Nov. 2019
5) Liu, X., Ma, Y., Shi, Z., and Chen. J., GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing, International Conference on Computer Vision, Oct. 27 – Nov. 2, 2019, Seoul, South Korea, pp. 7314-7323
3 unit(s) Staff A/D conversion; digital modulation; frequency hopping; code-division multiplexing; matched filters; equalization; optimal receiver design; entropy; coding; data compression; capacity of band-limited Gaussian channel.
Instructor
Dr. Jun Chen
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
