LI Cheuk Ting

*SHB = Ho Sin Hang Engineering Building, The Chinese University of Hong Kong

Research Interest
  • Delay-constrained communications
  • Distributed computing
  • Machine learning
  • Automated theorem proving
  • Undecidable problems
Courses Taught

  • Information Theory
  • Introduction to Stochastic Processes

LI Cheuk Ting 李卓霆教授

Assistant Professor
MIEEE
BSc, BEng (CUHK), MS, PhD (Stanford)
(852) 3943-5156
Room 807, SHB*
ctli [at] ie.cuhk.edu.hk
Website

Cheuk Ting Li received the B.Sc. degree in mathematics and B.Eng. degree in information engineering from The Chinese University of Hong Kong in 2012, and the M.S. and Ph.D. degree in electrical engineering from Stanford University in 2014 and 2018 respectively. He was a postdoctoral scholar at the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley. He joined the Department of Information Engineering, the Chinese University of Hong Kong in January 2020. He was awarded the 2016 IEEE Jack Keil Wolf ISIT Student Paper Award, and the 2023 Information Theory Society Paper Award.

Cheuk Ting Li is interested in developing new information-theoretic techniques to address problems in delay-constrained communications, automated theorem proving, distributed computing and machine learning.

Recent / Selected Publications
  • Cheuk Ting Li, “Undecidability of Network Coding, Conditional Information Inequalities, and Conditional Independence Implication,” IEEE Transactions on Information Theory, Feb 2023.
  • Cheuk Ting Li and Venkat Anantharam, “Pairwise Near-maximal Grand Coupling of Brownian Motions,” Annales de l’Institut Henri Poincare, Probabilites et Statistiques, vol. 58, no. 3, 2022.
  • Cheuk Ting Li, “The Undecidability of Conditional Affine Information Inequalities and Conditional Independence Implication with a Binary Constraint,” IEEE Transactions on Information Theory, vol. 68, no. 12, pp. 7685-7701, Dec 2022.
  • Cheuk Ting Li, “Efficient Approximate Minimum Entropy Coupling of Multiple Probability Distributions,” IEEE Transactions on Information Theory, vol. 67, no. 8, pp. 5259 – 5268, May 2021.
  • Cheuk Ting Li, “Infinite divisibility of information,” IEEE Transactions on Information Theory, vol. 68, no. 7, pp. 4257-4271, July 2022.
  • Cheuk Ting Li, “An automated theorem proving framework for information-theoretic results,” in 2021 IEEE International Symposium on Information Theory, July 2021.
  • Cheuk Ting Li and Venkat Anantharam, “A Unified Framework for One-shot Achievability via the Poisson Matching Lemma,” IEEE International Symposium on Information Theory 2019.
  • Cheuk Ting Li, Xiugang Wu, Ayfer Özgür and Abbas El Gamal, “Minimax Learning for Remote Prediction,” IEEE International Symposium on Information Theory 2018.
  • Cheuk Ting Li and Abbas El Gamal, “Strong Functional Representation Lemma and Applications to Coding Theorems,” IEEE Transactions on Information Theory, vol. 64, no. 11, pp. 6967-6978, Nov 2018.
  • Cheuk Ting Li and Abbas El Gamal, “Maximal Correlation Secrecy,” IEEE Transactions on Information Theory, vol. 64, no. 5, pp. 3916-3926, May 2018.
  • Cheuk Ting Li and Abbas El Gamal, “A Universal Coding Scheme for Remote Generation of Continuous Random Variables,” IEEE Transactions on Information Theory, vol. 64, no. 4, pp. 2583-2592, Apr 2018.
  • Cheuk Ting Li and Abbas El Gamal, “Distributed Simulation of Continuous Random Variables,” IEEE Transactions on Information Theory, vol. 63, no. 10, pp.6329-6343, Oct 2017.
  • Cheuk Ting Li and Ayfer Özgür, “Channel Diversity Needed for Vector Space Interference Alignment,” IEEE Transactions on Information Theory, vol. 62, no.4, pp.1942-1956, Apr 2016.
  • Cheuk Ting Li and Abbas El Gamal, “An Efficient Feedback Coding Scheme with Low Error Probability for Discrete Memoryless Channels,” IEEE Transactions on Information Theory, vol. 61, issue 6, pp. 2953 – 2963, Jun 2015.
Research Interest

  • Delay-constrained communications
  • Distributed computing
  • Machine learning
  • Automated theorem proving
  • Undecidable problems
Courses Taught

  • Information Theory
  • Introduction to Stochastic Processes