Shuang Li Assistant Professor School of Data Science The Chinese University of Hong Kong (Shenzhen) Daoyuan Building, 508b Shenzhen, Guangdong, China Email: lishuang@cuhk.edu.cn

Biography

I am an Assistant Professor in the School of Data Science at The Chinese University of Hong Kong (Shenzhen). Previously, I was a postdoctoral fellow at Harvard University, working on mobile health with Prof. Susan Murphy. I earned my Ph.D. in Industrial Engineering from H. Milton Stewart School of Industrial & Systems Engineering at Georgia Tech in 2019, and earlier, I received B.E. in Automation from University of Science and Technology of China in 2011.

[Google Scholar] [Curriculum Vitae]

I’m recruiting Research Assistants year-round and have 1~2 PhD openings starting Fall 2026. If you are interested in working with me, and with good programming skills and math background, you can contact me via email with your CV.

Research Interests

My research delves into the development of knowledge-enhanced sequential models and sequential decision tools, which prioritize interpretability and trustworthiness in machine learning.

More specifically, my research focuses on:

• Knowledge-Enhanced Sequential Models: By integrating domain-specific knowledge into machine learning algorithms, we aim to facilitate transparent decision-making processes and to create robust and reliable frameworks applicable in high-stakes systems.

• Human Cognitive Process Modeling: By incorporating Theory of Mind and spatial-temporal logical reasoning into AI systems, we aim to enable effective collaboration between humans and AI.

• Applications in Healthcare: We aim to apply machine learning tools to improve healthcare policies, clinical workflows, and patient outcomes through informed decision-making.

People

• PhD students:
         — Chao Yang (2022 Fall- ) BS: Shandong University, MS: University of Edinburgh. First-author paper: ICML 2025, UAI 2025.
         — Wendi Ren (2023 Fall- ) BS: Sun Yat-sen University, MS: Georgia Institute of Technology. Second-author paper: ICLR 2024, ICLR 2025, NeurIPS 2024.
         — Xinye Chen (2023 Fall- ) BS: Zhejiang University of Finance & Economics, MS: Johns Hopkins University.
         — Yanwen Liu (2025 Fall- ) BS: Beihang University.
         — Tianjian Zhang (2025 Fall- ) BS: Sun Yat-sen University.

• Research Assistants:
         — Chengzhi Cao (2022 Dec-2024 Jan) Visiting student from the University of Science and Technology of China. Initial placement: PhD student in ECE at CMU. First-author paper: NeurIPS 2023, ICLR 2024.
         — Zitao Song (2022 Jun-2023 May) Master student at The Chinese University of Hong Kong (Shenzhen). Initial placement: PhD student in CS at Purdue University. First-author paper: ICLR 2024, ICML 2024.
         — Yang Yang (2022 Oct- 2024 Aug) Master student at The Chinese University of Hong Kong (Shenzhen). Initial placement: PhD student at the Hong Kong University of Science and Technology (Guangzhou). First-author paper: ICML 2024, NeurIPS 2024.
         — Yinghao Fu (2023 May- 2024 Aug) Master student at The Chinese University of Hong Kong (Shenzhen). Initial placement: PhD student in DS at the City University of Hong Kong. Co-first-author paper: ICLR 2024.
         — Shuting Cui (2023 July- 2023 Dec) Initial placement: PhD student in Hong Kong University of Science and Technology (Guangzhou). Second-author paper: ICML 2025.
         — Zhiren Gong (2025 March-2025 Aug) Initial placement: PhD student in the Nanyang Technological University.
         — Yuting Yan (2025 Jan- ) BS: Beihang University. Master student at The Chinese University of Hong Kong (Shenzhen). First-author paper: NeurIPS 2025 GenAI4Health Workshop (Award Candidate).
         — Wenjie Shen (2025 Aug- ) BS: University of Science and Technology of China.

• Undergraduate students in The Chinese University of Hong Kong (Shenzhen):
         — Minghao Mou (2022 Jun-2023 May) Initial placement: PhD student in ECE at Purdue University
         — Yiling Kuang (2022 Sep-2023 May) Initial placement: PhD student in Statistics at The Chinese University of Hong Kong. First-author paper: AISTATS 2024.
         — Zhaner Mou (2021 Dec-2022 May) Initial placement: Master student in Biostatisitcs at UC San Diego. Current placement: PhD student in DS at UC San Diego
         — Zilin Jing (2022 May-2023 May) Initial placement: PhD student in CS at the Columbia University. First-author paper: ICML 2023 Interpretable ML in Healthcare Workshop.
         — Junyu Leng (2024 Jan-2025 May) Initial placement: PhD student in ISE at the Texas A&M University.
         — Shuhan Zhang (2024 March- ) First-author paper: ICLR 2025, NeurIPS 2025 (spotlight).
         — Jinlong Li (2025 Jun- )
         — Yongxi Feng (2025 Aug- )
         — Yitong Ding (2025 Aug- )

Publications

Conference

• Deep Context-Dependent Choice Model.
  S. Zhang, Z. Wang, R. Gao and S. Li
  Neural Information Processing Systems (NeurIPS), 2025. (Spotlight, acceptance rate: 3%)

• RKHS Choice Model
  Y. Yang, Z. Wang, R. Gao and S. Li.
  ACM Conference on Economics and Computation (EC), 2025.

• Flow-Based Delayed Hawkes Process
  C. Yang, W. Ren and S. Li.
  Conference on Uncertainty in Artificial Intelligence (UAI), 2025.

• Evolving Minds: Logic-Informed Inference from Temporal Action Patterns
  C. Yang, S. Cui, Y. Yang and S. Li.
  International Conference on Machine Learning (ICML), 2025.

• Convergence of Mean-Field Langevin Stochastic Descent-Ascent for Distributional Minimax Optimization
  Z. Liu, F. Liu, R. Gao and S. Li.
  International Conference on Machine Learning (ICML), 2025. (Spotlight, acceptance rate: 2.6%)

• Logic-Logit: A Logic-Based Approach to Choice Modeling
  S. Zhang, W. Ren and S. Li.
  International Conference on Learning Representations (ICLR), 2025.

• HyperLogic: Enhancing Diversity and Accuracy in Rule Learning with HyperNets
  Y. Yang, W. Ren and S. Li.
  Neural Information Processing Systems (NeurIPS), 2024.

• Neuro-Symbolic Temporal Point Processes
  Y. Yang, C. Yang, B. Li, Y. Fu and S. Li.
  International Conference on Machine Learning (ICML), 2024.

• Latent Logic Tree Extraction for Event Sequence Explanation from LLMs
  Z. Song, C. Yang, C. Wang, B. An and S. Li.
  International Conference on Machine Learning (ICML), 2024.

• Unveiling Latent Causal Rules: A Temporal Point Process Approach for Abnormal Event Explanation
  Y. Kuang *, C. Yang *, Y. Yang and S. Li.
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.

• Enhancing Human-AI Collaboration Through Logic-Guided Reasoning
  C. Cao, Y. Fu, S. Xu, R. Zhang and S. Li.
  International Conference on Learning Representations (ICLR), 2024.

• Amortized Network Intervention to Steer Excitatory Point Processes
  Z. Song, W. Ren and S. Li.
  International Conference on Learning Representations (ICLR), 2024.

• Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions
  C. Cao, C. Yang, R. Zhang and S. Li.
  Neural Information Processing Systems (NeurIPS), 2023.

• Explaining Point Processes by Learning Interpretable Temporal Logic Rules
  S. Li*, M. Feng *, L. Wang, A. Essofi, Y. Cao, J. Yan and L. Song.
  International Conference on Learning Representations (ICLR), 2022.

• Temporal Logic Point Processes
  S. Li, L. Wang, R. Zhang, X. Chang, X. Liu, Y. Xie, Y. Qi, and L. Song
  International Conference on Machine Learning (ICML), 2020.

• Continuous-Time Dynamic Graph Learning via Neural Interaction Processes
  X. Chang, X. Liu, J. Wen, S. Li, Y, Fang, L. Song, and Y. Qi
  The Conference on Information and Knowledge Management (CIKM), 2020.

• Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
  X. Chen, S. Li, H. Li, S. Jiang, Y. Qi, and L. Song
  International Conference on Machine Learning (ICML), 2019.

• Learning Temporal Point Processes via Reinforcement Learning
  S. Li, S. Xiao, S. Zhu, N. Du, Y. Xie, and L. Song
  Neural Information Processing Systems (NIPS), 2018. (Spotlight, acceptance rate: 168/4856 = 3.5%)

• Recurrent Hidden Semi-Markov Model
  H. Dai, B. Dai, Y. Zhang, S. Li, and L. Song
  International Conference on Learning Representations (ICLR), 2017.

• Fake News Mitigation via Point Processes Based Intervention
  M. Farajtabar, J. Yang, X. Ye, R. Trivedi, E. Khalil, S. Li, H. Xu, L. Song, and H. Zha
  International Conference on Machine Learning (ICML), 2017.

• Online Seismic Event Picking Via Sequential Change-Point Detection
  S. Li, Y. Cao, C. Leamon, Y. Xie, L. Shi, and W. Song
  Allerton Conference on Control, Communications and Computing (Allerton), 2016.

• M-Statistic for Kernel Change-Point Detection
  S. Li, Y. Xie, H. Dai, and L. Song
  Neural Information Processing Systems (NIPS), 2015.

• Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression
  Y. Liu, S. Li, F. Li, L. Song, and J. Rehg
  Neural Information Processing Systems (NIPS), 2015.

• COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
  M. Farajtabar, Y. Wang, M. Gomez-Rodriguez, S. Li, H. Zha, and L. Song
  Neural Information Processing Systems (NIPS), 2015. (Oral, acceptance rate: 15/1838 = 0.8%)

Journal

• Micro-Randomized Trials for Promoting Engagement in Mobile Health Data Collection: Adolescent/Young Adult Oral Chemotherapy Adherence as an Example
  S. Li, A. Psihogios, E. McKelvey, A. Ahmed, M. Rabbi, and S. Murphy
  Current Opinion in Systems Biology, 2020.

• Scan B-statistic for Kernel Change-point Detection
  S. Li, Y. Xie, H. Dai, and L. Song
  Sequential Analysis, 38(4):503-544, 2019.
         — Finalist of 2018 INFORMS QSR Best Student Paper Competition

• Detecting Weak Changes in Dynamic Events over Networks
  S. Li, Y. Xie, M. Farajtabar, A. Verma, and L. Song
  IEEE Transactions on Signal and Information Processing over Networks, Vol. 3, No. 2, June 2017.
         — Finalist of 2018 INFORMS Social Media analytics Best Student Paper Competition

• COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
  M. Farajtabar, Y. Wang, M. Gomez-Rodriguez, S. Li, H. Zha, and L. Song
  Journal of Machine Learning Research, 2017.

• Control for Time-Varying Delay Systems by Integrating Semi-Discretization and Hysteresis-Based Switching
  C. Shao, S. Li, H. Li, and J. Sheng
  Asian Journal of Control, 2018.

• COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
  M. Farajtabar, Y. Wang, M. Gomez-Rodriguez, S. Li, H. Zha, and L. Song
  The Web Conference, Journal Track, 2018.

• Reinforcement Learning of Spatio-Temporal Point Processes
  S. Zhu, S. Li, Z. Peng, and Y. Xie.
  IEEE Transactions on Knowledge and Data Engineering, 2022.

Book Chapter

• Learning Continuous-Time Hidden Markov Models for Event Data
  Y. Liu, A. Moreno, S. Li, F. Li, L. Song, and J. Rehg.
  Mobile Health, Springer, 2017.

Workshop

• Unanchoring the Mind: DAE-Guided Counterfactual Reasoning for Rare Disease Diagnosis.
  Y. Yan, Y. Fu, W. Ren and S. Li
  NeurIPS Workshop on GenAI4Health, 2025. (Award Candidate)

• Neural Decision Rule for Constrained Contextual Stochastic Optimization.
  Z. Liu, Z. Xu, F. Liu, R. Gao and S. Li
  NeurIPS Workshop on MLxOR, 2025.

• From Counts to Choice: Choice-Driven Spatial-Temporal Counting Process Models.
  C. Yang, Y. Kuang and S. Li
  NeurIPS Workshop on UrbanAI: Harnessing Artificial Intelligence for Smart Cities, 2025.

• Who Should Be Consulted? Targeted Expert Selection for Rare Disease Diagnosis.
  Y. Fu, C. Yang, X. Chen, Y. Yan and S. Li
  ICML Workshop on Collaborative and Federated Agentic Workflows, 2025. (Oral)

• Inferring the Invisible: Neuro-Symbolic Rule Discovery for Missing Value Imputation.
  W. Ren, K. Wan, J. Leng and S. Li
  ICML Workshop on DataWorld: Unifying Data Curation Frameworks Across Domains, 2025.

• Deep Context-Dependent Choice Model.
  S. Zhang, Z. Wang, R. Gao and S. Li
  ICML Workshop on Models of Human Feedback for AI Alignment, 2025. (Oral)

• Discovering Logic-Informed Intrinsic Rewards to Explain Human Policies.
  C. Cao, Y. Fu, C. Yang and S. Li
  ICML Workshop on Programmatic Representations for Agent Learning, 2025.

• Counterfactual Optimization of Treatment Policies Based on Temporal Point Processes.
  Z. Jing, C. Yang and S. Li
  ICML Workshop on Interpretable Machine Learning in Healthcare, 2023.

• Reinforcement Temporal Logic Rule Learning to Explain the Generating Processes of Events.
  C. Yang, L. Wang, Z. Mou and S. Li
  ICML Workshop on Interpretable Machine Learning in Healthcare, 2022.

• Interpretable Deep Generative Spatio-Temporal Point Processes
  S. Zhu, S. Li, Z. Peng, and Y. Xie
  NeurIPS Workshop on AI for Earth Sciences, 2020.

• Temporal Logic Point Processes Processes
  S. Li, L. Wang, R. Zhang, Y. Xie, N. Du, and L. Song
  NeurIPS Workshop on Learning with Temporal Point Processes, 2019. (Oral)

• Co-evolutionary Dynamics of Information Diffusion and Network Structure
  M. Farajtabar, M. Gomez-Rodriguez, Y. Wang, S. Li, H. Zha, and L. Song
  Workshop on Activity and Events in Networks: Models, Methods Applications, in conjunction with International World Wide Web Conference (WWW) 2015

Teaching

• CSC 6022 Machine Learning
         — Graduate level course, The Chinese University of Hong Kong, Shenzhen, Fall 2025.

• CSC 6022 Machine Learning
         — Graduate level course, The Chinese University of Hong Kong, Shenzhen, Spring 2025.

• DDA 2001 Introduction to Data Science
         — Undergraduate level course, The Chinese University of Hong Kong, Shenzhen, Spring 2025.

• DDA 2001 Introduction to Data Science
         — Undergraduate level course, The Chinese University of Hong Kong, Shenzhen, Spring 2024.

• DDA 6060/CSC 6022 Machine Learning
         — Graduate level course, The Chinese University of Hong Kong, Shenzhen, Spring 2024.

• CSC 6137 Generative Models
         — Graduate level course on generative models, The Chinese University of Hong Kong, Shenzhen, Fall 2023.

• DDA 6060/CSC 6022 Machine Learning
         — Graduate level course, The Chinese University of Hong Kong, Shenzhen, Spring 2023.

• DDA 6107 Advanced Machine Learning
         — Graduate level course on probabilistic graphical models and generative models, The Chinese University of Hong Kong, Shenzhen, Fall 2022.

• DDA 6060 Machine Learning
         — Graduate level course, The Chinese University of Hong Kong, Shenzhen, Spring 2022.

• DDA 2001 Introduction to Data Science
         — Undergraduate level course, The Chinese University of Hong Kong, Shenzhen, Fall 2021.

Services

• ICML, NeurIPS, ICLR

• ICML, NeurIPS, ICLR, AAAI, AISTATS, UAI

• Journal of American Statistical Association, Annals of Applied Statistics, IEEE Transactions on Signal Processing, IEEE Transactions on Information Theory, Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks and Learning Systems