• 2003-2007: University of Hong Kong, Statistics, Doctoral Degree (Full-time)
• 1998-2001: Peking University, Mathematics, Master Degree (Full-time)
• 1994-1998: Peking University, Mathematics, Bachelor Degree (Full-time)

Work Experience

• 2026-Present: Shenzhen Loop Area Institute, Professor
• 2021-Present: School of Computing and Data Science, University of Hong Kong, Professor
• 2015-2021: School of Computing and Data Science, University of Hong Kong, Associate Professor
• 2009-2014: School of Computing and Data Science, University of Hong Kong, Assistant Professor
• 2007-2009: Division of Mathematical Sciences, Nanyang Technological University, Singapore, Assistant Professor

Guodong Li, PhD, is a professor at the School of Computing and Data Science, University of Hong Kong. He obtained Bachelor and Master degrees from the School of Mathematical Sciences, Peking University, and PhD degree from the Department of Statistics and Actuarial Science, University of Hong Kong. He has been an assistant professor at Nanyang Technological University, Singapore. His research areas include theoretical machine learning, high-dimensional data analysis and econometrics, and he was/is the PI of 9 projects funded by Hong Kong Research Grant Council. He has published more than 70 papers at top conferences or journals from machine learning, statistics and econometrics. Cooperation in machine learning and AI, statistics and econometrics and applications from outstanding students are welcome.

Research Projects

Hosted Projects ：

2025-2028: Interpretable and Efficient Autoregressive Models for High-Dimensional Tensor-Valued Time Series, GRF 17309625, Principal Investigator

2022-2025: A computationally scalable autoregressive moving average model for high-dimensional time series modeling, GRF 17313722, Principal Investigator

2019-2020: Small Sample Learning for Sequential Data, Huawei Noah’s Ark Lab’s project, Principal Investigator

Participated Projects ：

2021-2025: Big economic data analysis: theory, methodology and application, NSFC, 72033002, Core Participant

Representative Publications ：

Liu Q, Zhao W, Huang W, Fang Y, Yu L, Li G. From Layers to States: A State Space Model Perspective to Deep Neural Network Layer Dynamics, Proceedings of the 13th International Conference on Learning Representations (ICLR), 2025.

Zhao J, Fang Y, Li G. Recurrence along depth: deep convolutional neural networks with recurrent layer aggregation, Advances in Neural Information Processing Systems (NeurIPS), 2021, Vol. 34, pp.10627-10640.

Zhao J, Huang F, Lv J, Duan Y, Qin Z, Li G, Tian G. Do RNN and LSTM have long memory? Proceedings of the 37th International Conference on Machine Learning (ICML), 2020. Vol. 119, pp.11365-11375.

Zhu Q, Tan S, Zheng Y, Li G. Quantile autoregressive conditional heteroscedasticity, Journal of the Royal Statistical Society, Series B, 2023, 85(4), 1099-1127. (Top 4 statistical journals, DOI: 10.1093/jrsssb/qkad068)

Wang D, Zheng Y, Lian H, Li G. High-dimensional vector autoregressive time series modeling via tensor decomposition, Journal of the American Statistical Association, 2022, 117(539), 1338-1356. (Top 4 statistical journals, DOI: 10.1080/01621459.2020.1855183)

Huang F, Lu K, Zheng Y, Li G. Supervised Factor Modeling for High-Dimensional Linear Time Series, Journal of Econometrics, 2025, 249(B), 105995. (Top 2 econometric journals, DOI: 10.1016/j.jeconom.2025.105995)

Wang D, Zheng Y, Li G. High-dimensional low-rank tensor autoregressive time series modeling, Journal of Econometrics, 2024, 238(1), 105544. (Top 2 econometric journals, DOI: 10.1016/j.jeconom.2023.105544)

Research Awards：

2025: Outstanding Research Student Supervisor Award, University of Hong Kong