Anru Zhang, Ph.D.

Duke University

Title: Statistical Tensor Learning in 2020s: Methodology, Theory, and Applications

Abstract: The analysis of tensor data, i.e., arrays with multiple directions, has become an active research topic in the era of big data. Datasets in the form of tensors arise from a wide range of scientific applications. Tensor methods also provide unique perspectives to many high-dimensional problems, where the observations are not necessarily tensors. Problems in high-dimensional tensors generally possess distinct characteristics that pose great challenges to the data science community.

In this talk, we discuss several recent advances in statistical tensor learning and their applications in computational imaging, social network, and generative model. We also illustrate how we develop statistically optimal methods and computationally efficient algorithms that interact with the modern theories of computation, high-dimensional statistics, and non-convex optimization.

Bio: Anru Zhang is the Eugene Anson Stead, Jr. M.D. Associate Professor at the Department of Biostatistics & Bioinformatics and Associate Professor at the Departments of Computer Science, Mathematics, and Statistical Science at Duke University. He obtained his Bachelor's degree from Peking University in 2010 and Ph.D. from the University of Pennsylvania in 2015. His work focuses on high-dimensional statistical inference, non-convex optimization, computational complexity, tensor learning, and applications in electronic health records, genomics, and computational imaging. He won the NSF CAREER Award (2020), ASA Gottfried E. Noether Junior Award (2021), Bernoulli Society New Researcher Award (2021), ICSA Outstanding Young Researcher Award (2021), and the IMS Tweedie Award (2022, awarded by Institute of Mathematical Statistics to a single young statistician/probabilist per year).