Shi Yuanming
Associate Professor
Graduated School: The Hong Kong University of Science and Technology
Tel: 021-20685374
Office: 2-302F
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Shi Yuanming is an Associate Professor in the School of Information Science and Technology, currently serving as the Director of the Intelligent Networks Center at the university.


Dr. Shi Yuanming received his Bachelor's degree in Electronic Engineering from Tsinghua University in July 2011, and was trained in the Mathematical Physics Fundamental Science Class from 2007 to 2009. He obtained his Ph.D. in Electronic and Computer Engineering from the Hong Kong University of Science and Technology in August 2015, under the supervision of Professor Khaled B. Letaief. He joined the School of Information Science and Technology at ShanghaiTech University as an Assistant Professor in September 2015, and was promoted to Associate Professor in January 2019. In the fall semester of 2016, he served as a visiting professor at the University of California, Berkeley, visiting Professor Martin J. Wainwright in the field of statistical machine learning. His research has been awarded the 2016 IEEE Communications Society Marconi Prize for Best Paper (one of the most important academic awards in the field of wireless communication), as well as the 2016 IEEE Signal Processing Society Best Young Author Paper Award. His main research interests are machine learning, signal processing, mathematical optimization, high-dimensional statistics, and their applications in 6G networks, artificial intelligence, the Internet of Things, and big data.

  • Name:Xia Shuhao
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  • Name:Huang Shaoming
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  • Name:Yang Zhanpeng
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  • Name:Zeng Xiangyu
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  • Name:Zhang Pengfei
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  • Name:Huang Jingfeng
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  • Name:Zeng Li
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  • Name:Xing Lukuan
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  • Name:Yang Yuhan
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  • Name:You Jiawei
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  • Name:Zhu Jingyang
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  • Name:He Jinglian
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  • Name:Zhuang Zeming
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  • Name:Yang Hanzhe
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My research focuses on optimization, statistics, machine learning, wireless communications, and their applications to 6G, IoT, and edge AI, including:

  • 6G wireless networks

  • Edge artificial intelligence

  • Federated learning and analytics

  • Optimization and statistics

  • Internet-of-Things

Resources

  1. Optimization

    1. Convex Optimization, by S. Boyd and L. Vandenberghe, Cambridge University Press, 2003. [EE364a][EE364b]

    2. Numerical Optimization, by J. Nocedal and S. Wright, Springer-Verlag, 2006.

    3. Nonlinear Programming, by D. Bertsekas, Athena Scientific. 2016.

  2. Statistics

    1. High-Dimensional Probability: An Introduction with Applications in Data Science, by Roman Vershynin, Cambridge University Press, 2018.

    2. High-Dimensional Statistics: A Non-Asymptotic Viewpoint, by Martin Wainwright, 2017.

    3. Topics in Random Matrix Theory, by Terence Tao, American Mathematical Society, 2012.

    4. Asymptotic Statistics, by A. W. van der Vaart, Cambridge University Press, 2012.

  3. Learning

    1. Pattern Recognition and Machine Learning, by C. M. Bishop, Springer, 2007. [CS229]

    2. Deep Learning, by I. Goodfellow, Y. Bengio and A. Courville, MIT Press, 2016. [CS231n]

    3. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by T. Hastie, R. Tibshirani, and J. Friedman, Springer, 2009.

  4. Information

    1. Fundamentals of Wireless Communication, by David Tse and Pramod Viswanath, Cambridge University Press, 2005.

    2. Elements of Information Theory, by Thomas M. Cover and Joy A. Thomas, Wiley, 2006.

Principal Collaborators


  1. Convex Optimization (SI251), ShanghaiTech University, Spring 2016, Spring 2017, Spring 2018, Spring 2019.

  2. Probability and Statistics (SI140), ShanghaiTech University, Fall 2019.

  3. Optimization and Machine Learning (SI151), ShanghaiTech University, Spring 2017, Spring 2018.

Tutorials

  1. Federated Machine Learning in 6G: Opportunities and Challenges [slides]

    1. IEEE/CIC International Conference on Communications in China, Xiamen, Jul. 2021.

  2. Mobile Edge Artificial Intelligence: Opportunities and Challenges [slides]

    1. IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, Dec. 2019.

  3. Sparse and low-rank optimization for dense wireless networks: models, algorithms and theory [slides]

    1. IEEE/CIC International Conference on Communications in China, Beijing, Aug. 2018.

    2. International Workshop on Big Data and Optimization, Sungkyunkwan University, Suwon, Korea, Dec. 2017.

    3. IEEE Global Communications Conference (GLOBECOM), Singapore, Dec. 2017.

Invited Talks

  1. Graph neural networks for large-scale optimization in wireless networks

    1. East China Normal University, Shanghai, Jun. 2021.

    2. Huawei Technologies Co., Ltd., Shanghai, May 2021.

  2. Reconfigurable intelligent surface empowered wireless networks

    1. Shandong University, Online, May 2020.

    2. Huawei Technologies Co., Ltd., Shanghai, Dec. 2019

  3. Federated machine learning via over-the-air computation [slides]

    1. East China Normal University, Shanghai, Jun. 2021.

    2. Shandong University, Online, May 2020.

    3. Xiamen University, Xiamen, Jul. 2019.

    4. Zhejiang University, Hangzhou, May 2019.

    5. Shanghai Jiao Tong University, Shanghai, May 2019.

  4. Nonconvex demixing from bilinear measurements [slides]

    1. Xiamen University, Xiamen, Jul. 2019.

    2. Tsinghua University, Beijing, Sept. 2018.

    3. Sungkyunkwan University, Suwon, Korea, Sept. 2018.

    4. The Chinese University of Hong Kong, Shenzhen, Aug. 2018.

    5. Shanghai University, Shanghai, Jul. 2018.

    6. Southern University of Science and Technology, Shenzhen, Jul. 2018.

    7. University of Electronic Science and Technology of China, Chengdu, Jul. 2018.

  5. Scalable sparse optimization in dense wireless cooperative networks [slides]

    1. Xiamen University, Xiamen, Jul. 2019.

    2. Shanghai Jiao Tong University, Shanghai, Aug. 2016.

    3. ShanghaiTech University, Shanghai, Jul. 2015.

1. AI

"Edge AI, Federated Learning"

Books

  • Y. Shi, K. Yang, Z. Yang, and Y. Zhou, Mobile Edge Artificial Intelligence: Opportunities and Challenges, Elsevier, Feb. 2021, in preparation.

Magazine/Survey Papers

  • K. B. Letaief, Y. Shi, J. Lu, and J. Lu, “Edge artificial intelligence for 6G: vision, enabling technologies, and applications,” IEEE J. Select. Areas Commun.

  • Y. Shi, K. Yang, T. Jiang, J. Zhang, and K. B. Letaief, “Communication-efficient edge AI: algorithms and systems,” IEEE Commun. Surveys Tuts., vol. 22, no. 4, pp. 2167-2191, 4th Quart. 2020. [paper][slides]

  • K. Yang, Y. Shi, Y. Zhou, Z. Yang, L. Fu, and W. Chen, “Federated machine learning for intelligent IoT via reconfigurable intelligent surface,” IEEE Netw., vol. 34, no. 5, pp. 16-22, Oct. 2020. [paper]

  • K. Yang, Y. Zhou, Z. Yang, and Y. Shi, “Communication-efficient edge AI inference over wireless networks,” ZTE Commun., vol. 18, no. 2, pp. 31-39, Jun. 2020. [paper]

  • K. B. Letaief, W. Chen, Y. Shi, J. Zhang, and Y. Zhang, “The roadmap to 6G - AI empowered wireless networks,” IEEE Commun. Mag., vol. 57, no. 8, pp. 84-90, Aug. 2019. [paper]

Journal Articles

  • L. Li, L. Yang, X. Guo, Y. Shi, H. Wang, W. Chen, and K. B. Letaief, “Delay analysis of wireless federated learning based on saddle point approximation and large deviation theory,” submitted. [paper]

  • Z. Wang, J. Zong, Y. Zhou, Y. Shi, and V. W. Wong, “Decentralized multi-agent power control in wireless networks with frequency reuse,” submitted.

  • X. Yang, T. Jiang, Y. Shi, and H. Wang, “An inexact iteratively reweighted approach for efficient neuron pruning,” IEEE Trans. Comput.

  • Z. Wang, J. Qiu, Y. Zhou, Y. Shi, L. Fu, W. Chen, and K. B. Letaief, “Federated learning via intelligent reflecting surface,” IEEE Trans. Wireless Commun. [paper]

  • S. Hua, Y. Zhou, K. Yang, Y. Shi, and K. Wang “Reconfigurable intelligent surface for green edge inference,” IEEE Trans. Green Commun. Netw., vol. 5, no. 2, Jun. 2021. [paper][codes]

  • Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “Graph neural networks for scalable radio resource management: architecture design and theoretical analysis,” IEEE J. Select. Areas Commun., vol. 39, no. 1, pp. 101-115, Jan. 2021. [paper][codes]

  • K. Yang, Y. Shi, W. Yu, and Z. Ding, “Energy-efficient processing and robust wireless cooperative transmission for edge inference,” IEEE Internet of Things J., vol. 7, no. 10, Oct. 2020. [paper][codes]

  • X. Yang, S. Hua, Y. Shi, H. Wang, J. Zhang, and K. B. Letaief, “Sparse optimization for green edge AI inference,” J. Commun. Information Netw., vol. 5, no. 1, Mar. 2020. [paper][codes]

  • K. Yang, T. Jiang, Y. Shi, and Z. Ding, “Federated learning via over-the-air computation,” IEEE Trans. Wireless Commun., vol. 19, no. 3, pp. 2022-2035, Mar. 2020. [paper][codes]

  • Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “LORM: Learning to optimize for resource management in wireless networks with few training samples,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 665-679, Jan. 2020. [paper]

  • K. Yang, Y. Shi, and Z. Ding, “Data shuffling in wireless distributed computing via low-rank optimization,” IEEE Trans. Signal Process., vol. 67, no. 12, pp. 3087-3099, Jun., 2019. [paper][codes]

2. IoT

"AirComp, Massive Connectivity"

Books

  • Y. Shi, J. Dong, and J. Zhang, Low-overhead Communications in IoT Networks: Structured Signal Processing Approaches, Springer, Apr. 2020. [book][website][codes]

Journal Articles

  • J. Zhai, Y. Jiang, Y. Shi, C. N. Jones, and X. Zhang, “Distributionally robust chance-constrained dispatch for integrated transmission-distribution systems via distributed optimization,” submitted.

  • A. Jalali, Y. Shi, and Z. Ding, “Robust blind demixing of coded signals based on Wirtinger flow,” submitted.

  • M. Fu, Y. Zhou, Y. Shi, W. Chen, and R. Zhang, “UAV aided over-the-air computation,” IEEE Trans. Wireless Commun. [paper]

  • W. Fang, Y. Jiang, Y. Shi, Y. Zhou, W. Chen, and K. B. Letaief, “Over-the-air computation via reconfigurable intelligent surface,” IEEE Trans. Commun. [paper]

  • S. Xia, Y. Shi, Y. Zhou, and X. Yuan, “Reconfigurable intelligent surface for massive connectivity,” IEEE Trans. Signal Process. [paper]

  • Y. Shi, H. Choi, Y. Shi, and Y. Zhou, “Algorithm unrolling for massive access via deep neural network with theoretical guarantee,” IEEE Trans. Wireless Commun. [paper]

  • J. Dong, J. Zhang, Y. Shi, and H. Wang, “Faster activity and data detection in massive random access: a multi-armed bandit approach,” IEEE Internet of Things J. [paper]

  • Z. Wang, Y. Shi, Y. Zhou, H. Zhou, and N. Zhang, “Wireless-powered over-the-air computation in intelligent reflecting surface aided IoT networks,” IEEE Internet of Things J., vol. 8, no. 3, pp. 1585-1598, Feb. 2021. [paper][codes]

  • Y. Jiang, J. Su, Y. Shi, and B. Houska, “Distributed optimization for massive connectivity,” IEEE Wireless Commun. Lett., vol. 9, no. 9, pp. 1412-1416, Sept. 2020. [paper][codes]

  • M. C. Tsakiris, L. Peng, A. Conca, L. Kneip, Y. Shi, and H. Choi, “An algebraic-geometric approach for linear regression without correspondences,” IEEE Trans. Inf. Theory. vol. 66, pp. 5130-5144, Aug. 2020. [paper]

  • J. Dong, Y. Shi, and Z. Ding, “Blind over-the-air computation and data fusion via provable Wirtinger flow,” IEEE Trans. Signal Process.,vol. 68, pp. 1136-1151, Feb. 2020. [paper][codes]

  • T. Jiang, Y. Shi, J. Zhang, and K. B. Letaief, “Joint activity detection and channel estimation for IoT networks: phase transition and computation-estimation tradeoff,” IEEE Internet of Things J., vol. 6, no. 4, pp. 6212-6225, Aug. 2019. [paper][codes]

  • J. Dong, K. Yang, and Y. Shi, “Blind demixing for low-latency communication,” IEEE Trans. Wireless Commun., vol. 18, no. 2, pp. 897-911, Feb., 2019. [paper][codes]

  • J. Dong and Y. Shi, “Nonconvex demixing from bilinear measurements,” IEEE Trans. Signal Process., vol. 66, no. 19, pp. 5152-5166, Oct., 2018. [paper][codes]

3. 5G/6G

"Cloud-RAN, Meta-Surface"

Book Chapters

  1. Y. Shi, K. Yang, and Y. Yang, “Generalized Low-Rank Optimization for Ultra-Dense Fog-RANs,” in Ultra Dense Networks: Principles and Technologies, Cambridge University Press, 2020. [chapter]

  2. Y. Shi, J. Zhang, K. B. Letaief, B. Bai, and W. Chen, “Large-Scale Convex Optimization For C-RANs,” in Cloud Radio Access Networks: Principles, Technologies, and Applications, Cambridge University Press, 2017. [chapter]

Magazine Papers

  • X. Yuan, Y. Zhang, Y. Shi, W. Yan, and H. Liu, “Reconfigurable-Intelligent-Surface empowered wireless communications: Challenges and opportunities,” IEEE Wireless Commun., vol. 28, no. 2, pp. 136-143, Apr. 2021. [paper]

  • Y. Shi, J. Zhang, W. Chen, and K. B. Letaief, “Generalized sparse and low-rank optimization for ultra-dense networks,” IEEE Commun. Mag., vol. 56, no. 6, pp. 42-48, Jun. 2018. [paper][slides I][slides II]

  • Y. Shi, J. Zhang, K. B. Letaief, B. Bai, and W. Chen, “Large-scale convex optimization for ultra-dense Cloud-RAN,” IEEE Wireless Commun., vol. 22, no. 3, pp. 84-91, Jun. 2015. [paper]

Journal Articles

  • J. He, K. Yu, Y. Shi, Y. Zhou, W. Chen, and K. B. Letaief, “Reconfigurable intelligent surface assisted massive MIMO with antenna selection,” submitted. [paper]

  • M. Fu, T. Jiang, H. Choi, Y. Zhou, and Y. Shi, “Sparse and low-rank optimization for pliable index coding via alternating projection,” IEEE Trans. Commun.

  • M. Fu, Y. Zhou, Y. Shi, and K. B. Letaief, “Reconfigurable intelligent surface empowered downlink non-orthogonal multiple access,” IEEE Trans. Commun., vol. 69, no. 6, pp. 3802-3817, Jun. 2021. [paper][codes]

  • H. Choi, T. Jiang, Y. Shi, X. Liu, Y. Zhou, and K. B. Letaief, “Large-scale beamforming for massive MIMO via randomized sketching,” IEEE Trans. Veh. Technol., vol. 70, no. 5, pp. 4669-4681, May 2021. [paper][codes]

  • Y. Zhou, J. Li, Y. Shi, and V. W. Wong, “Flexible functional split design for downlink C-RAN with capacity-constrained fronthaul,” IEEE Trans. Veh. Technol., vol. 68, no. 6, pp. 6050-6063, Jun. 2019. [paper]

  • K. Yang, Y. Shi, and Z. Ding, “Generalized low-rank optimization for topological cooperation in ultra-dense networks,” IEEE Trans. Wireless Commun., vol. 18, no. 5, pp. 2539-2552, May 2019. [paper][codes]

  • X. Liu, Y. Shi, J. Zhang, and K. B. Letaief, “Massive CSI acquisition for dense Cloud-RANs with spatial-temporal dynamics,” IEEE Trans. Wireless Commun., vol. 17, no. 4, pp. 2557-2570, Apr. 2018. [paper]

  • Y. Shi, J. Zhang, W. Chen, and K. B. Letaief, “Enhanced group sparse beamforming for dense green Cloud-RAN: A random matrix approach,” IEEE Trans. Wireless Commun., vol. 17, no. 4, pp. 2511-2524, Apr. 2018. [paper][codes]

  • Y. Shi, B. Mishra, and W. Chen, “Topological interference management with user admission control via Riemannian optimization,” IEEE Trans. Wireless Commun., vol. 16, no. 11, pp. 7362-7375, Nov. 2017. [paper][codes]

  • X. Peng, Y. Shi, J. Zhang, and K. B. Letaief, “Layered group sparse beamforming for cache-enabled wireless networks,” IEEE Trans. Commun., vol. 65, no. 12, pp. 5589-5603, Nov. 2017. [paper]

  • Y. Shi, J. Zhang, and K. B. Letaief, “Low-rank matrix completion for topological interference management by Riemannian pursuit,” IEEE Trans. Wireless Commun., vol. 15, no. 7, pp. 4703-4717, Jul. 2016. [paper][codes]

  • Y. Shi, J. Cheng, J. Zhang, B. Bai, W. Chen and K. B. Letaief, “Smoothed Lp-minimization for green Cloud-RAN with user admission control,” IEEE J. Select. Areas Commun., vol. 34, no. 4, pp. 1022-1036, Apr. 2016. [paper][codes]

  • Y. Shi, J. Zhang, B. O’Donoghue, and K. B. Letaief, “Large-scale convex optimization for dense wireless cooperative networks,” IEEE Trans. Signal Process., vol. 63, no. 18, pp. 4729-4743, Sept. 2015. [paper][codes](The 2016 IEEE Signal Processing Society Young Author Best Paper Award)

  • Y. Shi, J. Zhang, and K. B. Letaief, “Robust group sparse beamforming for multicast green Cloud-RAN with imperfect CSI,” IEEE Trans. Signal Process., vol. 63, no. 17, pp. 4647-4659, Sept. 2015. [paper][codes]

  • Y. Shi, J. Zhang, and K. B. Letaief, “Optimal stochastic coordinated beamforming for wireless cooperative networks with CSI uncertainty,” IEEE Trans. Signal Process., vol. 63,, no. 4, pp. 960-973, Feb. 2015. [paper][codes]

  • Y. Shi, J. Zhang, and K. B. Letaief, “Group sparse beamforming for green Cloud-RAN,” IEEE Trans. Wireless Commun., vol. 13, no. 5, pp. 2809-2823, May 2014. [paper][codes](The 2016 Marconi Prize Paper Award)

4. Mathematical Optimization

Journal Articles

  • H. Wang, F. Zhang, Q. Wu, Y. Hu, and Y. Shi, “Nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods,” submitted. [paper]

  • J. Dong, K. Yang, and Y. Shi, “Ranking from crowdsourced pairwise comparisons via smoothed Riemannian optimization,” ACM Trans. Knowl. Discovery Data.,vol. 14, no. 2, pp. 1-26, Feb. 2020. [paper][codes]

  • H. Choi, J. He, H. Hu, and Y. Shi, “Fast computing von Neumann entropy for large-scale graphs via quadratic approximations,” Linear Algebra Appl., vol. 585, pp. 127-146, Jan. 2020. [paper]

  • H. Choi, H. Lee, Y. Shen, and Y. Shi, “Comparing large-scale graphs based on quantum probability theory,” Appl. Math. Comput., vol. 358, pp. 1-15, Oct. 2019. [paper]

  • H. Choi, S. Kim, and Y. Shi, “Geometric mean of partial positive definite matrices with missing entries,” Linear Multilinear Algebra, pp. 1-26, Mar. 2019. [paper]