• 1989：Massachusetts Institute of Technology, Ph.D.
• 1984：Applied Mathematics, Peking University, BS

Work Experience:

• 2025-present: Shenzhen Loop Area Institute, Executive Dean
• 2015-present: Shenzhen Research Institute of Big Data, Shenzhen, China, Director
• 2014-present: The Chinese University of Hong Kong, Shenzhen, Vice President (Academic), Presidential Chair Professor
• 2003-2014: Dept. of Electrical and Computer Engineering, University of Minnesota, Professor
• 2003-2013: Dept. of Electrical and Computer Engineering, University of Minnesota, Professor and ADC Chair in Digital Technology
• 1989-2004: Dept. of Electrical and Computer Engineering, McMaster University, Department Head, Canada Research Chair (Tier I) in Information Processing

Zhi-Quan (Tom) Luo is the Executive Dean of the Shenzhen Loop Area Institute, Vice President (Academic) and Professor at The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), and founding Director of the Shenzhen Research Institute of Big Data. He received his B.S. in Mathematics from Peking University in 1984 and his Ph.D. in Operations Research from the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology in 1989. He is a Fellow of IEEE and SIAM, a Fellow of the Royal Society of Canada, and a Foreign Member of the Chinese Academy of Engineering. He served as Editor-in-Chief of the IEEE Transactions on Signal Processing (2012–2014).

Professor Luo's research group focuses on the intersection of optimization methods with machine learning, large language models (LLMs), digital communication and signal processing. His research contributions span optimization algorithms and system methodologies for AI and large-scale models — including efficient operators and training/deployment optimization for large-scale learning and inference — as well as transceiver optimization for wireless communications, optimal robust beamforming design, and dynamic spectrum management. His work has been recognized with Best Paper Awards from the IEEE Signal Processing Society (2004, 2009, 2011, and 2015), the IEEE International Conference on Communications (2011), the European Association for Signal Processing, and the International Congress of Chinese Mathematicians (2020).

In recognition of his outstanding contributions to optimization theory, Professor Luo has received numerous prestigious honors, including the INFORMS Farkas Prize in Optimization (2010), the Mathematical Optimization Society Tseng Memorial Lectureship (2018), the inaugural Wang Xuan Applied Mathematics Prize of the China Society for Industrial and Applied Mathematics (2022), the Shenzhen Science and Technology Progress Award (First Prize, 2023), and the inaugural Hua Luogeng Prize at ICCM (2025).

In 2020, addressing fundamental challenges in network efficiency maximization, Professor Luo proposed a data-driven network optimization framework that deeply integrates statistical modeling with artificial intelligence, establishing a new paradigm for intelligent network optimization. This framework has been deployed across more than 30 countries, optimizing over 1.8 million base stations and significantly improving network performance for approximately one-quarter of the global population, while simultaneously reducing operational costs and carbon emissions for network operators — delivering substantial economic and societal impact.

In 2025, in the area of core AI infrastructure, Professor Luo's team developed KernelCat, China's first intelligent operator development and optimization platform targeting domestic AI chips. KernelCat automates model migration, performance tuning, and operator generation, compressing what previously required weeks of low-level development into a matter of hours. Benchmarks on Ascend-series chips demonstrated a 22% reduction in latency and a 30% improvement in throughput, effectively advancing China's domestic AI ecosystem from merely "functional" to genuinely "high-performing."

Professor Luo has graduated dozens of Ph.D. students into faculty roles at top-tier universities (e.g., USC, UIUC, UW-Madison, UMN) and impactful research careers at tech leaders (e.g., Google, Meta, ByteDance, Huawei, LinkedIn, XAI). His graduates have achieved remarkable career outcomes, with some recent ones securing million-dollar compensation packages from leading AI companies upon graduation, underscoring the rigorous research training and strong placement record of his research group.

Current students have interned or received job offers from ByteDance, Alibaba, Kimi, DeepSeek, Huawei, Tencent, XAI, and DeepMind. Some have successfully started their own company in Shenzhen.

1. Y. Qiu, C. Huang, Y. Xue, Z. Jiang, Q. Shi, D. Zhang and Z.-Q. Luo, “Relaxation-Free Min-k-Partition for PCI Assignment in 5G Networks,” IEEE Transactions on Signal Processing, 2025.

2. J. Wang, F. Yin, T. Ding, T.-H. Chang, Z.-Q. Luo, Q. Yan, “ Learning to Gridize: Segment Physical World by Wireless Communication Channel,” in arXiv preprint arXiv:2507.15386, 2025.

3. D. Rybin, Y. Zhang, Z.-Q. Luo, “ XX^t Can Be Faster,” in arXiv preprint arXiv:2505.09814, 2025.

4. Y. Zhang, D. Rybin, Z.-Q. Luo, “ Finite horizon optimization: Framework and applications,” in arXiv preprint arXiv:2412.21068, 2024.

5. K. Li, Y. Li, L. Cheng and Z.-Q. Luo, “Enhancing Multi-Stream Beamforming Through CQIs for 5G NR FDD Massive MIMO Communications: A Tuning-Free Scheme,” in IEEE Transactions on Wireless Communications, vol. 23, no. 11, pp. 17508-17521, Nov. 2024.

6. H. Liang, and Z.-Q. Luo. “Bridging distributional and risk-sensitive reinforcement learning with provable regret bounds.” Journal of Machine Learning Research 25.221 (2024): 1-56.

7. S. Zhang, X. Ning, X. Zheng, Q. Shi, T.-H. Chang and Z.-Q. Luo, “A Physics-Based and Data-Driven Approach for Localized Statistical Channel Modeling,” in IEEE Transactions on Wireless Communications, vol. 23, no. 6, pp. 5409-5424, June 2024.

8. K. Li, Y. Li, L. Cheng, Q. Shi and Z.-Q. Luo, “Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems With Limited Feedback,” in IEEE Transactions on Signal Processing, vol. 72, pp. 1032-1048, 2024.

9. Q. Wu, B. Zheng, C. You, L. Zhu, K. Shen, X. Shao, W. Mei, B. Di, H. Zhang, E. Basar, L. Song, M. D. Renzo, Z.-Q. Luo, Rui Zhang, “Intelligent Surfaces Empowered Wireless Network: Recent Advances and the Road to 6G,” in Proceedings of the IEEE, 2024.

10. X. Zhao, S. Lu, Q. Shi and Z.-Q. Luo, “Rethinking WMMSE: Can Its Complexity Scale Linearly With the Number of BS Antennas?” in IEEE Transactions on Signal Processing, vol. 71, pp. 433-446, 2023.

11. Y.-B. Zhao and Z.-Q. Luo, “Dynamic Orthogonal Matching Pursuit for Sparse Data Reconstruction,” in IEEE Open Journal of Signal Processing, vol. 4, pp. 242-256, 2023.

12. Y. Zhao, Z.-Q. Luo, Improved RIP-based bounds for guaranteed performance of two compressed sensing algorithms. Sci. China Math. 66, 1123–1140 (2023).

13. Z.-Q. Luo, X. Zheng, D. López-Pérez, Q. Yan, X. Chen, N. Wang, Q. Shi, T.-H. Chang, A. Garcia-Rodriguez, “SRCON: A Data-Driven Network Performance Simulator for Real-World Wireless Networks,” in IEEE Communications Magazine, vol. 61, no. 6, pp. 96-102, June 2023.

14. W. Pu, Y.-F. Liu and Z.-Q. Luo, “Efficient Estimation of Sensor Biases for the 3-D Asynchronous Multi-Sensor System,” in IEEE Transactions on Signal Processing, vol. 71, pp. 2420-2433, 2023.

15. C. Chen, L. Shen, W. Liu and Z.-Q. Luo, “Efficient-Adam: Communication-Efficient Distributed Adam,” in IEEE Transactions on Signal Processing, vol. 71, pp. 3257-3266, 2023.

16. Y. Zhang, K. Shen, S. Ren, X. Li, X. Chen and Z.-Q. Luo, “Configuring Intelligent Reflecting Surface With Performance Guarantees: Optimal Beamforming,” in IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 5, pp. 967-979, Aug. 2022.

17. J. Zhang and Z.-Q. Luo, “ A global dual error bound and its application to the analysis of linearly constrained nonconvex optimization,” in SIAM Journal on Optimization, vol. 32, no. 3, pp. 2319–2346, 2022.

18. M. Asgarian, G. Mirjalily and Z.-Q. Luo, “Embedding Multicast Service Function Chains in NFV-Enabled Networks,” in IEEE Communications Letters, vol. 25, no. 4, pp. 1264-1268, April 2021.