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 am now recruiting research assistants! I have multiple PhD openings this year. 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 student
         — Chao Yang (2022- ) BS: Shandong University, MS: University of Edinburgh
         — Wendi Ren (2023- ) BS: Sun Yat-sen University, MS: Georgia Institute of Technology
         — Xinye Chen (2023- ) BS: Zhejiang University of Finance & Economics, MS: Johns Hopkins University

• Research Assistant
         — Chengzhi Cao (2022 Dec-2024 Jan) Visiting student from the University of Science and Technology of China. News: PhD offers from CMU etc
         — Zitao Song (2022 Jun-2023 May) Master student at The Chinese University of Hong Kong (Shenzhen). News: PhD offers from Purdue, Washington University in St. Louis, Univ. Virginia etc
         — Yang Yang (2022 Oct- ) Master student at The Chinese University of Hong Kong (Shenzhen)
         — Yinghao Fu (2023 May- ) Master student at The Chinese University of Hong Kong (Shenzhen)
         — Shuting Cui (2023 July- 2023 Dec)

• Undergraduate student (who has been working closely with me)
         — 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
         — Zhaner Mou (2021 Dec-2022 May) Initial placement: Master student in Biostatisitcs at UC San Diego
         — Zilin Jing (2022 May- ) News: PhD offers from Columbia Univ., Umich, CMU, GaTech
         — Junyu Leng (2024 Jan- )
         — Shuhan Zhang (2024 March- )

Publications

Conference

• 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

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

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

• 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