Yidong Zhou

Assistant Professor in Statistics at the University of Minnesota

I work on machine learning, causal inference, and nonparametric statistics, with a particular interest in data such as distributions, networks, and curves.

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ydzhou@umn.edu ORCID GitHub LinkedIn
Portrait of Yidong Zhou outdoors

I received my Ph.D. in Statistics from UC Davis in 2024, advised by Professor Hans-Georg Müller, and my B.S. in Statistics from the University of Science and Technology of China in 2019.

Before joining Minnesota, I was a postdoctoral scholar at UC Davis. I was also a research intern at Amazon in 2022.

Research

Much of my work uses the geometry of data to guide statistical modeling and inference. I study probability distributions, networks, functions, compositions, and positive-definite matrices, with applications in neuroimaging, child development, economics and policy evaluation, and plant genomics.

Machine learning

Neural networks, tree-based methods, and transfer learning for complex outcomes.

Causal inference

Causal effects with structured outcomes, with a focus on difference-in-differences, regression discontinuity, and synthetic control.

Nonparametric statistics

Fréchet regression and inference for distributions, networks, functions, and manifold-valued data.

Functional data analysis

Longitudinal trajectories and sparsely observed functional data, motivated by biomedical and population studies.

Selected publications

All publications

* Equal contribution.

Recent updates

All news

  1. I am helping organize Statistics Beyond Euclid: Functional Data, Random Objects and AI at UC Davis, November 12–14, 2026.

  2. Regression Discontinuity Designs for Functional Data and Random Objects in Geodesic Spaces has been accepted by the Journal of the Royal Statistical Society: Series B.

  3. Our paper Geodesic Difference-in-Differences will appear in Biometrika.

Photography

Outside research, I enjoy hiking and landscape photography, usually with my camera.

All photographs