Xiongzhi Chen

Biography

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I have extensive interests and expertise in Statistics and Mathematics. I have conducted research on simultaneous inference for high-dimensional dependent data and for high-dimensional discrete data, and on exponential family of distributions, Ito diffusion processes and time series models. I am shifting my research efforts to “Foundation of analysis of non-Euclidean data”, and “Foundation of deep learning”. Here is a summary of my research interests:

• Mathematical and statistical foundations of analysis of non-Euclidean data, focusing on uniqueness of Frechet mean on non-negatively curved metric spaces and on estimating geometric quantities (such as curvature) from data.
• Mathematical and statistical foundations of deep learning, focusing on geometric, topological and statistical properties of convolutional neural networks.
• Simultaneous inference (including variable/model selection and multiple comparison) with applications in biology and pharmaceutical science.
• Latent variable models for high-dimensional data with applications in genomics and signal processing.

Education/Training

• Postdoctoral Research Associate, Princeton University, 2013-2017
• PhD in Statistics, Purdue University, 2012
• MA in Mathematics, University of Hawaii, 2009
• MS in Mathematics, Sichuan University, 2006