Xiaowei Jia

I am an Assistant Professor in the Department of Computer Science at the University of Pittsburgh. I obtained my Ph.D. degree at the University of Minnesota, under the supervision of Prof. Vipin Kumar. Prior to that, I got my B.S. and M.S. from the University of Science and Technology of China (USTC) and State University of New York at Buffalo.

More About Me and My Research


My research is focused on developing data mining and machine learning methods to solve real-world problems with great societal and scientific impacts.

I am looking for self-motivated Ph.D. students who are interested in data mining and machine learning. Please email me your CV if you are interested. More details can be found here.

Knowledge-Guided Machine Learning

We aim to build a new generation of ML models by integrating scientific theory into ML models for knowledge discovery in scientific problems. Under extensive collaboration with domain scientists, we hope to use these ML techniques to solve some major challenges faced by human beings.

Spatial and Temporal Data Mining

How to mine important patterns from the spatio-temporal data to provide accurate and timely information for decision making? We need customized deep learning solutions (e.g., spatio-temporal models and graph models) that are context-aware, interpretable, and generalizable.

Fairness for Social Good

Develop new models and fairness-preserving learning strategies to reduce location-related model bias, which can facilitate fair distribution of social resources.

Downscaling for Scientific Data

Build data-driven and knowledge-aware solutions to efficiently generate high-resolution and high-fidelity data over space and time.

Reinforcement Learning for Complex Systems

Use data-driven reinforcement learning methods to inform human decisions in complex systems, e.g., supply chain and resource management.

Active Learning in Scientific Systems

Machine learning solutions are needed to intelligently select the right time and locations to deploy sensors.