Xiao Hu

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Assistant Professor
Cheriton School of Computer Science
University of Waterloo
Email: xiaohu@uwaterloo.ca
Room 3353, Davis Center

I am seeking highly motivated Ph.D. and MMath students for fully funded positions starting in 2026. If you are interested in working with me, please send me an email and mention my name as a potential advisor in your application.

Efficient query engines lie at the heart of modern data-intensive applications, such as real-time analytics, ML pipelines, privacy-preserving services, knowledge-graph exploration, and beyond. My group work on new algorithms and complexity results for database query processing, so that next-generation data systems can run faster, use fewer resources, and adapt optimally to new constraints. Some of our recent works include join-aggregate query processing [PODS25], sampling/clustering over relational data [SIGMOD18, SIGMOD24, PODS25], leveraging fast matrix multiplication to improve decades-old complexity barriers [PODS24, PODS25]. These algorithmic insights naturally extend beyond classic relational settings into high-dimensional, unstructured, and multimodal domains: algorithms for hybrid sparse/dense vector search, machine learning over representative samples from query results, and automatically inducing structures from unstructured multimodal data for efficient querying. I am looking for students who want to push both the theoretical foundations and these emerging areas forward.

Publications