LI Cheuk Ting

Assistant Professor,
Department of Information Engineering,
The Chinese University of Hong Kong

Education: BSc, BEng (CUHK), MS, PhD (Stanford)

contact

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.

His research interests include simulation of random sources and channels, one-shot and finite-blocklength schemes in information theory, network information theory, and automated theorem proving.

I am looking for students interested in conducting research in information theory. Please send me an email if you are interested.

Current Ph.D. Students

Chih Wei Ling

Yanxiao Liu

Research Interests

One-shot and finite-blocklength coding schemes via Poisson processes
- Presentation slides for AICIT2024 (modified from the slides for the invited talk at ITA2024)
- Video recording of the invited talk at Beyond IID 2021
- Publications:
  - Cheuk Ting Li and Venkat Anantharam, “A Unified Framework for One-shot Achievability via the Poisson Matching Lemma,” IEEE Transactions on Information Theory, vol. 67, no. 5, pp. 2624-2651, May 2021. Preprint (2023 Information Theory Society Paper Award)
  - 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. Preprint

Automated theorem proving
- Python Symbolic Information Theoretic Inequality Prover (PSITIP), a computer algebra system for information theory
- Publications:
  - Cheuk Ting Li, “An Automated Theorem Proving Framework for Information-Theoretic Results,” IEEE Transactions on Information Theory, Nov. 2023. Preprint
  - Raymond W. Yeung and Cheuk Ting Li, “Machine-Proving of Entropy Inequalities,” IEEE BITS the Information Theory Magazine, vol. 1, no. 1, pp. 12-22, 1 Sept. 2021.

Undecidable problems in information theory
- Presentation slides for the invited talk at Dagstuhl Seminar 24111
- Video recording of the invited talk at Stanford University
- Video recording of the invited talk at Kolmogorov Seminar
- Publications:
  - Cheuk Ting Li, “Undecidability of Network Coding, Conditional Information Inequalities, and Conditional Independence Implication,” IEEE Transactions on Information Theory, Feb 2023. Preprint
  - 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. Preprint
  - Cheuk Ting Li, “The Undecidability of Network Coding with Some Fixed-Size Messages and Edges,” IEEE International Symposium on Information Theory 2022. Preprint

Channel simulation
- Publications:
  - 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. Preprint
  - Mahmoud Hegazy and Cheuk Ting Li, “Randomized Quantization with Exact Error Distribution,” 2022 IEEE Information Theory Workshop (ITW), pp. 350-355, 2022.
  - Mahmoud Hegazy, Rémi Leluc, Cheuk Ting Li and Aymeric Dieuleveut, “Compression with Exact Error Distribution for Federated Learning,” Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 2024.
  - Ali Moradi Shahmiri, Chih Wei Ling and Cheuk Ting Li, “Communication-efficient Laplace mechanism for differential privacy via random quantization,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024.
  - Chih Wei Ling and Cheuk Ting Li, “Vector Quantization With Error Uniformly Distributed Over an Arbitrary Set,” IEEE Transactions on Information Theory, July 2024. Preprint
  - Chak Fung Choi and Cheuk Ting Li, “Multiple-Output Channel Simulation and Lossy Compression of Probability Distributions,” IEEE Information Theory Workshop, 2021. Preprint
  - 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. Preprint
  - 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. Preprint (2016 IEEE Jack Keil Wolf ISIT Student Paper Award)

Selected Publications

Chih Wei Ling, Yanxiao Liu and Cheuk Ting Li, “Weighted Parity-Check Codes for Channels with State and Asymmetric Channels,” IEEE Transactions on Information Theory, 2024. Preprint
Chih Wei Ling and Cheuk Ting Li, “Vector Quantization With Error Uniformly Distributed Over an Arbitrary Set,” IEEE Transactions on Information Theory, July 2024. Preprint
Cheuk Ting Li, Jingwei Zhang and Farzan Farnia, “On Convergence in Wasserstein Distance and f-divergence Minimization Problems,” Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 2024.
Mahmoud Hegazy, Rémi Leluc, Cheuk Ting Li and Aymeric Dieuleveut, “Compression with Exact Error Distribution for Federated Learning,” Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 2024.
Ali Moradi Shahmiri, Chih Wei Ling and Cheuk Ting Li, “Communication-efficient Laplace mechanism for differential privacy via random quantization,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024.
Mohammad Jalali, Cheuk Ting Li and Farzan Farnia, “An Information-Theoretic Evaluation of Generative Models in Learning Multi-modal Distributions,” Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023.
Cheuk Ting Li, “First-Order Theory of Probabilistic Independence and Single-Letter Characterizations of Capacity Regions,” IEEE Transactions on Information Theory, Dec. 2023. Preprint
Cheuk Ting Li, “An Automated Theorem Proving Framework for Information-Theoretic Results,” IEEE Transactions on Information Theory, Nov. 2023. Preprint
Cheuk Ting Li, “Undecidability of Network Coding, Conditional Information Inequalities, and Conditional Independence Implication,” IEEE Transactions on Information Theory, Feb 2023. Preprint
Mahmoud Hegazy and Cheuk Ting Li, “Randomized Quantization with Exact Error Distribution,” 2022 IEEE Information Theory Workshop (ITW), pp. 350-355, 2022.
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. Preprint
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. Preprint
Cheuk Ting Li, “The Undecidability of Network Coding with Some Fixed-Size Messages and Edges,” IEEE International Symposium on Information Theory 2022. Preprint
Sijie Li and Cheuk Ting Li, “Arithmetic Network Coding for Secret Sum Computation,” IEEE International Symposium on Information Theory 2022. Preprint
Raymond W. Yeung and Cheuk Ting Li, “Machine-Proving of Entropy Inequalities,” IEEE BITS the Information Theory Magazine, vol. 1, no. 1, pp. 12-22, 1 Sept. 2021.
Chak Fung Choi and Cheuk Ting Li, “Multiple-Output Channel Simulation and Lossy Compression of Probability Distributions,” IEEE Information Theory Workshop, 2021. Preprint
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. Preprint
Cheuk Ting Li, “Infinite divisibility of information,” IEEE Transactions on Information Theory, vol. 68, no. 7, pp. 4257-4271, July 2022. Preprint
Cheuk Ting Li, “Asymptotically Scale-invariant Multi-resolution Quantization,” IEEE Transactions on Information Theory, 2021. Preprint
Cheuk Ting Li and Venkat Anantharam, “A Unified Framework for One-shot Achievability via the Poisson Matching Lemma,” IEEE Transactions on Information Theory, vol. 67, no. 5, pp. 2624-2651, May 2021. Preprint (2023 Information Theory Society Paper Award)
Cheuk Ting Li, Xiugang Wu, Ayfer Ozgur and Abbas El Gamal, “Minimax Learning for Distributed Inference,” IEEE Transactions on Information Theory, vol. 66, no. 12, pp. 7929-7938, Dec. 2020. Preprint
Cheuk Ting Li and Venkat Anantharam, “One-Shot Variable-Length Secret Key Agreement Approaching Mutual Information,” IEEE Transactions on Information Theory, vol. 67, no. 8, pp. 5509-5525, Aug. 2021. Preprint
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. Preprint
Cheuk Ting Li and Abbas El Gamal, “Extended Gray-Wyner System with Complementary Causal Side Information,” IEEE Transactions on Information Theory, vol. 64, no. 8, pp. 5862-5878, Aug 2018. Preprint
Cheuk Ting Li and Abbas El Gamal, “Maximal Correlation Secrecy,” IEEE Transactions on Information Theory, vol. 64, no. 5, pp. 3916-3926, May 2018. Preprint
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. Preprint
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. Preprint (2016 IEEE Jack Keil Wolf ISIT Student Paper Award)
Cheuk Ting Li and Ayfer Ozgur, “Channel Diversity Needed for Vector Space Interference Alignment,” IEEE Transactions on Information Theory, vol. 62, no.4, pp.1942-1956, Apr 2016. Preprint
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. Preprint
Gowtham Ramani Kumar, Cheuk Ting Li and Abbas El Gamal, “Exact common information,” IEEE International Symposium on Information Theory 2014. Preprint

Software

Python Symbolic Information Theoretic Inequality Prover (PSITIP), a computer algebra system for information theory
- Run PSITIP on browser