Dr. Xiao Shou

Education

• PhD, Applied Mathematics, Rensselaer Polytechnic Institute (2023)
• MS, Computer Science, Rensselaer Polytechnic Institute (2023)
• MS, Ohio State University (2014)
• BA, Wittenberg University (2012)

Research Interests

• Machine Learning
• Data Science
• Uncertainty Quantification
• Optimization
• Causal Learning

 Google Scholar

Bio

Dr. Shou is an Assistant Professor in the Department of Computer Science at Baylor University. Prior to joining Baylor, he was a visiting researcher in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). He obtained his PhD from RPI in 2023. His research focuses on machine learning and deep learning for sequential data, particularly temporal point processes, and time series data. His work has led to over publications at top machine learning conferences, including NeurIPS, ICML, AAAI, CleaR, etc. Dr. Shou served as a PC member of top international conferences, such as NeurIPS, ICML, ICLR, AISTATS, AMIA, etc. Notably, he received the 2023 IBM Research Accomplishment Award for his collaborative work on Graphical Event Models with IBM researchers and the 2021 ACM BCB best student paper award.

Selected Publications

Shou, X., Bhattacharjya, D., Gao, T., Subramanian, D., Hassanzadeh, O., & Bennett, K. P. (2023). Pairwise causality guided transformers for event sequences. In Advances in Neural Information Processing Systems, 36.

Shou, X., Bhattacharjya, D., Gao, T., Subramanian, D., Hassanzadeh, O., & Bennett, K. (2023). Probabilistic attention-to-influence neural models for event sequences. In International Conference on Machine Learning, 40.

Shou, X., Gao, T., Subramanian, D., Bhattacharjya, D., & Bennett, K. P. (2023). Concurrent multi-label prediction in event streams. In Proceedings of the AAAI Conference on Artificial Intelligence.