Chee Wei Tan's Homepage
Dr. Tan received the M.A and Ph.D. degrees in Electrical Engineering from Princeton University. Dr. Tan’s research areas include Edge AI, Agentic AI, Machine Learning Systems, AI for Science and Healthcare, Wireless Networking and Network Science. His research has contributed to the areas of AI-enabled networking, AI copilots, privacy-preserving technologies and large-scale optimization. His research work received a few prizes including the 2025 IEEE Software Best Paper Award, 2024 IEEE CAI Honorable Mention Paper Award (Foundation Models and Generative AI Category), the Princeton University Wu Prize for Excellence, Google Faculty Award. He was a postdoctoral scholar in the NetLab Group at Caltech, a senior fellow for Science at Extreme Scales program at the Institute for Pure and Applied Mathematics at UCLA, and was a visiting faculty at Shanghai Jiao Tong University, Tencent AI Lab and Qualcomm R&D (QRC).
Dr. Tan currently leads the CCDS Health Informatics Lab at Nanyang Technological University in Singapore, teaches Deep Learning for Healthcare AI at the LKC School of Medicine, and collaborates with several clinical researchers on AI in medicine. He has served as an Editor for IEEE Transactions on Signal and Information Processing over Networks, IEEE Transactions on Cognitive Communications and Networking, IEEE/ACM Transactions on Networking, IEEE Transactions on Communications, Co-Chair of 2027 IEEE Globecom Symposium on Satellite & Space Communications, 2025 IEEE Globecom Symposium on AI-Enabled Networks, Publication Chair of the 2025 IEEE Conference on Artificial Intelligence, as an IEEE ComSoc Distinguished Lecturer 2020-2023 and on the ACM Learning at Scale Steering Committee. He received the Teaching Excellence Award at City University of Hong Kong, and was selected twice for the U.S. National Academy of Engineering China-America Frontiers of Engineering Symposium.
Recent Publication
X. Tong, C. W. Tan and H. V. Poor, Adversarial Water-Filling: Theory, Algorithms and Foundation Model, arXiv 2605.26163, 2026.
L. Tao, X. Tong and C. W. Tan, Learning to Optimize by Differentiable Programming, arXiv 2605.26163, 2026.
Y. Wang, P.-D. Yu and C. W. Tan, Future-Proofing Programmers: Optimal knowledge tracing for artificial intelligence-assisted personalized education [Special Issue on Artificial Intelligence for Education], IEEE Signal Processing Magazine 43 (1), 69-82, 2026.
Y. Wang, S. Guo and C. W. Tan, From Code Generation to Software Testing: AI Copilot with context-based RAG, IEEE Software 42 (4), 34-42, 2025.
M. Wong and C. W. Tan, Aligning Crowd-sourced Human Feedback for Reinforcement Learning on Code Generation by Large Language Models, IEEE Transactions on Big Data, accepted, 2025.
C. Hang, P.-D. Yu and C. W. Tan, Aligning TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking, IEEE Transactions on Artificial Intelligence, vol. 6, no. 11, pp. 3148-3162, 2025.
C. W. Tan, P.-D. Yu, S. Chen, H. V. Poor, Deeptrace: Learning to Optimize Contact Tracing in Epidemic Networks with Graph Neural Networks, IEEE Transactions on Signal and Information Processing over Networks 11, 97-113, 2025.
D. T. Ng, C. W. Tan and J. Leung Empowering student self‐regulated learning and science education through ChatGPT: A pioneering pilot study, British Journal of Educational Technology 55 (4), 1328-1353, 2024.
C. W. Tan, S. Guo, M. F. Wong, C. Hang: Copilot for Xcode: Exploring AI-Assisted Programming by Prompting Cloud-based Large Language Models, arXiv, 2023. Open Source Code
Md. T. R. Laskar, S. Alqahtani, M. S. Bari, M. Rahman, Md. A. M. Khan, H. Khan, I. Jahan, A. Bhuiyan, C. W. Tan, Md. R. Parvez, E. Hoque, S. Joty, J. X. Huang: A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations, The Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.
Y. Jiang, X. Tong, Z. Liu, X. Zhang, K.-Y. Lam and C. W. Tan, Certifying the right to be forgotten: Primal–dual optimization for sample and label unlearning in vertical federated learning, IEEE Transactions on Information Forensics & Security, vol. 20, pp. 13143-13158, 2025.
Y. Jiang, J. Shen, Z. Liu, C. W. Tan, K.-Y. Lam, Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning, IEEE Transactions on Information Forensics & Security, vol. 20, pp. 2632-2647, 2025.
C. W. Tan and P.-D. Yu: Contagion Source Detection in Epidemic and Infodemic Outbreaks: Mathematical Analysis and Network Algorithms, Foundations and Trends in Networking, Vol. 13, No. 2-3, pp. 107-251, 2023. Open-source Code
C. Hang, P.-D. Yu, S. Chen, C. W. Tan and G. Chen: MEGA: Machine Learning-enhanced Graph Analytics for Infodemic Risk Management, IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 12, 6100-6111, 2023. Open-source Code
S. Ho, L. Ling, C. W. Tan and R. W. Yeung, Proving and Disproving Information Inequalities: Theory and Scalable Algorithms, IEEE Transactions on Information Theory, Vol. 66, No. 9, pp. 5522-5536, 2020. Open-source Code
F. M. Wong, C. W. Tan, S. Sen and M. Chiang, Quantifying political leaning from tweets, retweets, and retweeters, IEEE Transactions on Knowledge and Data Engineering, Vol. 28, No. 8, pp. 2158-2172, 2016. Open-source Code
L. Zheng, C. Joe-Wong, C. W. Tan, M. Chiang and X. Wang, How to Bid the Cloud?, ACM SIGCOMM 2015. Open-source Code
C. W. Tan, Wireless Network Optimization by Perron-Frobenius Theory, Foundations and Trends in Networking, Now Publisher, 9(2-3), pp. 107-218, 2015. Open-source Code