Xiao Shou

Dr. Xiao Shou is an Assistant Professor in the Department of Computer Science at Baylor University, within the College of Arts and Sciences. He holds a PhD in Applied Mathematics and an MS in Computer Science, both from Rensselaer Polytechnic Institute (RPI), where he also served as a visiting researcher prior to joining Baylor. His academic journey includes an MS from Ohio State University and a BA from Wittenberg University.

His research centers on machine learning for sequential data, with a focus on temporal point processes and time series. He develops deep learning models that incorporate causality, uncertainty, and attention mechanisms to better understand complex event sequences. His work bridges theoretical advances with real-world applications in data-intensive domains.

The selected publications highlight a strong trend in modeling event sequences using advanced neural architectures. Key themes include causal learning, probabilistic attention, and multi-label prediction in streaming environments. These works have been published in top-tier conferences such as NeurIPS, ICML, and AAAI, reflecting high impact and technical rigor.

Scientific Awards:

• 2023 IBM Research Accomplishment Award
• 2021 ACM BCB Best Student Paper Award

Dr. Shou has served as a Program Committee (PC) member for premier conferences including NeurIPS, ICML, ICLR, AISTATS, and AMIA, indicating active engagement in the research community. While specific grant details are not mentioned, his collaborative work with IBM suggests industry-academic partnerships. He is likely involved in mentoring students, though no advisees are currently listed.

His research group likely focuses on developing next-generation machine learning models for dynamic systems, with potential applications in healthcare, finance, and network analysis. Future work may expand into interpretable AI, real-time decision systems, and causal discovery in high-dimensional event streams.