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Kin K. Leung

Kin K. Leung is the Tanaka Chair Professor in the Electrical and Electronic Engineering, and Computing Departments at Imperial College, U.K. After completing his Ph.D. degree at University of California, Los Angeles, he worked at AT&T Bell Labs and its succeeding companies in New Jersey for 19 years until he joined Imperial College in 2004.  

His current research interests include machine learning and distributed optimization for design, management and control of large-scale communications, computer and sensor networks.  He has received more than 50 granted U.S. patents and published 325 papers, which have received a total of 17,800 citations with an h-index of 65 in Google Scholar.

For his technical contributions, he has personally received major awards and honors including the membership of Academia Europaea (2012), IEEE Fellow (2001), IET Fellow (2021), Royal Society Wolfson Research Merit Award (2004-09), and the Distinguished Member of Technical Staff Award from AT&T Bell Labs (1994).  Jointly with his co-authors and collaborators, he also received the IEEE Communications Society Leonard G. Abraham Prize (2021), the U.S.–UK Science and Technology Stocktake Award (2021), the Lanchester Prize Honorable Mention Award (1997), and best paper awards at the IEEE ICC 20219, ICDCS 2013 and PIMRC 2012, and IET CCWMC 2009.

He served as a member (2009-11) and the chairman (2012-15) of the IEEE Fellow Evaluation Committee for Communications Society. He has served as guest editor and editor for 10 IEEE and ACM journals, including previously as editor for the IEEE JSAC: Wireless Series, IEEE Trans. on Wireless Communications, and IEEE Trans. on Communications. Currently, he chairs the Steering Committee for the IEEE Trans. on Mobile Computing, and is an editor for the ACM Computing Survey and International Journal on Sensor Networks.  He has served as a leader or a member of program committees for more than 50 conferences.

Lecture Topics
  • Deep Reinforcement Learning for Control and Management of Communication Networks
  • Optimized Resource Allocation and Computation in Wireless Networks
  • Federated Learning for Edge Computing with Resource Constraints
  • Communication and Sensor Networks: From Stochastic Models, Distributed Optimization to Machine Learning
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