Xiongzhi ChenAssistant ProfessorOffice: Neill Hall 230Phone: (509) 3353150Fax: (509) 3351188Email: xiongzhi.chen@wsu.edu 
Education and Training
 Postdoctoral Research Associate, Princeton University, 20132017
 PhD in Statistics, Purdue University, 2012
 MA in Mathematics, University of Hawaii, 2009
 MS in Mathematics, Sichuan University, 2006
Courses
Research Interests
 Analysis of nonEuclidean data (such as networks, shapes, images and tensors) (Active).
 Theoretical foundations of machine learning models (Active).
 Statistical inference on stochastic differential equations (including those driven by fractional Brownian motions) with applications in finance, biology and geology. (Dormant)
 Spectra of random matrices (including those of sample autocorrelation/autocovariance matrices/adjacency matrices) with applications to inference on highdimensional data. (Dormant)
 Simultaneous inference (including variable/model selection and multiple comparison) with applications in biology and pharmaceutical science. (Inactive)
 Latent variable models for highdimensional data with applications in genomics and signal processing. (Inactive)
More on My Research and Me

Please read through my Research webpage (whose link is given below) and then contact me if you want to work with me.
 Research and CV ; R pacakge fdrDiscreteNull for my papers A and B and C
 Notes in Math and Notes in Statistics
 Miscellaneous and Some Open Problems
Working with Me
 Core: Techniques in the books "Mathematical Statistics: Basic Ideas and Selected Topics, Volume I" by Kjell A. Doksum and Peter J. Bickel, "Real Analysis and Probability" by Robert B. Ash (or "Real Analysis and Probability" by R. M. Dudley), "Asymptotic Statistics" by Aad van der Vaart, "Real and Complex Analysis" by Walter Rudin, and "Introduction to Riemannian Manifolds" by John M. Lee, the last of which is needed for analysis of nonEuclidean data but not necessarily for the theoretical foundation of machine learning models.
 Supplementary: Techniques in "Measure Theory" by Paul Halmos, "Probability" by Albert N. Shiryaev, "A Course in Functional Analysis" by John B. Conway, "Riemannian Geometry" by Takashi Sakai, "A Comprehensive Introduction to Differential Geometry" by Michael Spivak, "Mathematical Foundations of InfiniteDimensional Statistical Models" by Evarist Gine Masdeu and Richard Nickl, and “General Topology” by John L. Kelley. Familiarity with the basics of ordinary differential equations and partial differential equations, such as how to solve typical such equations and properties of their solutions.
 Default: Skills required by Statistics (or Math) GQE, and univariate and multivariate calculus (e.g., "Differential and Integral Calculus, Volumes I and II" by Richard Courant) and linear algebra (e.g., "Lectures in Abstract Algebra, Volume II" by Nathan Jacobson). You also need to be good at R programming.
I strive to maintain high standards and high creativity in both my theoretical and applied research. As you might have already found out from my Research webpage, I am no longer doing research in highdimensional simultaneous inference or highdimensional sparse linear models. Instead, I am doing research on analysis of nonEuclidean data and on the theoretical foundation of machine learning models. Here are the tools that will be used in a theoretical project in any of the two active fields mentioned earlier:
If you are interested in working with me on a theoretical project of my expertise, please schedule a meeting with me.
I may also have some applied projects that I may get through collaborations. However, eventually new methodologies (and their theories) will be proposed for their associated applied problems. Please note the difference between "a purely applied project" and "a methodological project": the former proposes no new methodology whereas the latter does. So, I will not work on purely applied projects since they do not have sufficient intellectual contributions. If you choose to work with me on an applied project as your PhD thesis research, we will need to develop new methodologies (but not neccesarily their accompanying theories), and together with me you will sharpen much your skills in probability and mathematical statistics beyond those offered by our courses.
Acknowledgements

I am very grateful in particular to Gerard Letac (Universite de Toulouse), Sanat K. Sarkar (Temple U), Armin Schwartzman (UCSD), Donald St. P. Richards (Penn State U), Hongming Yin (WSU), Yimin Xiao (Michigan State U), Persi Diaconis (Stanford U) and Wayne Smith (U of Hawaii) for advice and help on my research.