博士（佐治亚理工学院）

硕士（佐治亚理工学院）

学士（中国科学技术大学）

李爽于2011年获得中国科学技术大学学士学位，并分别于2014年和2019年获得佐治亚理工学院硕士和博士学位。

在加入香港中文大学（深圳）之前，李爽在哈佛大学任博士后研究员，研究移动健康中的多智能体强化学习。她曾为哈佛大学序贯决策2021年春季课程提供课程材料。2014 年至 2019 年期间，她在佐治亚理工学院担任教学助理，曾负责机器学习、计算数据分析，和计算数据分析概要。2018年，她在谷歌进行了三个月的研究实习，研究推荐系统的用户行为建模。同年，李爽荣获INFORMS QSR最佳学生论文竞赛决赛入围奖和INFORMS社交媒体分析最佳学生论文竞赛决赛入围奖。2016年，获得 H. Milton Stewart 工业学院研究生贾维斯奖第二名。2011年至2012年，获得美国马萨诸塞大学阿默斯特分校工程学院的Hluchyj奖学金。2011年，获得中国科学技术大学自动化系优秀本科论文奖。她的研究领域包括用于序列数据分析和决策的机器学习、新序列模型、可靠高效的学习方法、有效推理程序、医疗保健、智慧城市和社交媒体。

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