**xcading@ucdavis.edu**

Department of Statistics

University of California Davis

Davis, CA 95616

I am currently a tenure-track Assistant Professor in the Department of Statistics at the University of California Davis (starting from 2020). I am also a faculty in Graduate Group in Applied Mathematics(GGAM). From 2018-2020, I was a research associate at Duke University working with Hau-Tieng Wu. Before that, I got my PhD in statistics in 2018 from the University of Toronto (2014-2018), under the supervision of Jeremy Quastel.

My research lies in the intersection of applied probability, mathematical statistics and statistical learning theory with a focus on manifold learning and machine learning. Currenly, I mainly focus on the following topics: Random Matrix Theory and its statistical and algorithmic applications, non-stationary time series analysis, statistical analysis for algorithms (machine learning and manifold learning) for complex, high dimensional and noisy data. My research is currently supported by the National Science Foundation. Thanks, NSF!

** Selected works **

- A Riemann--Hilbert approach to the perturbation theory for orthogonal polynomials: Applications to numerical linear algebra and random matrix theory , with Thomas Trogdon
- Auto-regressive approximations to non-stationary time series, with inference and applications, with Zhou Zhou
- On the spectral property of kernel-based sensor fusion algorithms of high dimensional data,
**IEEE Transactions on Information Theory**, with Hau-Tieng Wu - Estimation and inference for precision matrices of nonstationary time series,
**The Annals of Statistics**, with Zhou Zhou - Singular vector and singular subspace distribution for the matrix denoising model,
**The Annals of Statistics**, with Zhigang Bao and Ke Wang - Spiked separable covariance matrices and principal components,
**The Annals of Statistics**, with Fan Yang - A necessary and sufficient condition for edge universality at the largest singular values of covariance matrices,
**The Annals of Applied Probability**, with Fan Yang