Xiaoou Li

Xiaoou Li is an Associate Professor in the Department of Statistics at the University of Minnesota Twin Cities. Her research focuses on probability theory, sequential decision problems, and latent variable models, with applications in healthcare data analysis and educational testing.

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Her work contributes to the UN Sustainable Development Goals through advancements in statistical methodologies for anomaly detection, representation learning, and sequential analysis. Recent projects include multisource representation learning for pediatric EHR data and compound sequential change-point detection in parallel data streams.

Key research areas include Latent Variable Models, Sequential Decision Problems, and Change-point Detection, with applications in healthcare and educational testing. Publications highlight asymptotic optimality, factor determination in high-dimensional data, and quality control frameworks.

Scientific awards include NSF grants for detecting structured anomalies (2022) and level crossing of likelihood functions (2017). Collaborations span healthcare informatics and educational statistics, with funded projects extending to 2027.