LI, Shuang

Assistant Professor

Education Background

Ph.D. in Industrial Engineering, Georgia Institute of Technology, 2019
M.S. in Statistics, Georgia Institute of Technology, 2014
B.S. in Automation, University of Science and Technology of China, 2011

Research Field
Machine Learning for Sequential Data Analysis and Decision-making, Applications to Healthcare, Smart Cities, and Social Media
Personal Website
Biography

Shuang Li received her bachelor’s degree from University of Science and Technology of China in 2011, master’s and PhD degree from Georgia Institute of Technology in 2014 and 2019, respectively.

Prior to joining CUHK (Shenzhen), Shuang Li was a postdoctoral fellow of Harvard University, researching on multi-agent reinforcement learning in mobile health. Besides, she was responsible for preparing course materials for sequential decision making course at Harvard University in Spring 2021. During 2014 to 2019, she took the role of teaching assistant at Georgia Institute of Technology, used to be in charge of machine learning, computational data analysis, and introduction to computational data analysis. In 2018, she worked as a research intern at Google for three months, where she did research on user behavior modeling for recommender systems. Shuang Li awarded finalist of INFORMS QSR Best Student Paper Competition and finalist of INFORMS Social Media Analytics Best Student Paper Competition in the same year. She also won second place in the Jarvis Award for Graduate Student Research in H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, in 2016. She received the Hluchyj Fellowship from School of Engineering of University of Massachusetts at Amherst, from 2011 to 2012. In 2011, she won outstanding undergraduate thesis award of Department of Automation at University of Science and Technology of China.

Her research interests include Machine learning for sequential data analysis and decision-making, new sequential models, reliable and efficient learning methods, and effective inference procedures, applications to healthcare, smart cities, and social media.

Academic Publications

A. Conferences

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

2. S. Li, S. Xiao, S. Zhu, N. Du, Y. Xie, and L. Song.  Learning Temporal Point Processes via Reinforcement Learning. Neural Information Processing Systems, 2018. Spotlight

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

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

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

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

7. M. Farajtabar, Y. Wang, M. Gomez-Rodriguez, S. Li, H. Zha, and L. Song. COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution. Neural Information Processing Systems, 2015. Oral

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

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

B. Journals

1. S. Li, Y. Xie, H. Dai, and L. Song. Scan B-statistic for Kernel Change-point Detection. Sequential Analysis, 38(4):503-544, 2019.

- Finalist, INFORMS Quality, Statistics, and Reliability (QSR) Best Student Paper Award, 2018

2. S. Li, Y. Xie, M. Farajtabar, A. Verma, and L. Song. Detecting Changes in Dynamic Events over Networks. IEEE Transactions on Signal and Information Processing over Networks, Vol. 3, No. 2, June 2017.

- Finalist, INFORMS Social Media Analytics Best Student Paper Award, 2018

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

4. M. Farajtabar, Y. Wang, M. Gomez-Rodriguez, S. Li, H. Zha, and L. Song. COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution. Journal of Machine Learning Research, 18(41):1-49, 2017.

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

6. S. Zhu, S. Li, Z. Peng, and Y. Xie. Reinforcement Learning of Spatio-Temporal Point Processes. IEEE Transactions on Knowledge and Data Engineering, 2021.

C. Book Chapters

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

A complete list of publications can be found at https://scholar.google.com/citations?user=HxCZsCUAAAAJ&hl=en