Tensors in High Dimensional Data Analysis: Opportunities and Challenges

Large amount of multidimensional data represented by multiway arrays or tensors are prevalent in modern applications across various fields such as chemometrics, genomics, physics, psychology, and signal processing. The structural complexity of such data provides vast new opportunities for modeling and analysis, but efficiently extracting information content from them, both statistically and computationally, presents unique and fundamental challenges. Addressing these challenges requires an interdisciplinary approach that brings together tools and insights from statistics, optimization and numerical linear algebra among other fields. Despite these hurdles, significant progress has been made in the last decade. In this talk, I will review some of the key advancements and identify common threads among them, under several common statistical settings.

About Ming Yuan

Ming Yuan is a Professor of Statistics and an Associate Director of the Data Science Institute at Columbia University. He was previously a Senior Investigator in Virology at Morgridge Institute for Research and a Professor of Statistics at University of Wisconsin at Madison, and prior to that Coca-Cola Junior Professor of Industrial and Systems Engineering at Georgia Institute of Technology. His research and teaching interests lie broadly in statistics and its interface with other quantitative and computational fields such as optimization, machine learning, computational biology, and financial engineering. He has served as the program secretary of the Institute for Mathematical Statistics (IMS), and a member of the advisory board for the Quality, Statistics and Reliability section of the Institute for Operations Research and the Management Sciences (INFORMS). He was also a co-Editor of The Annals of Statistics and has served on numerous editorial boards. He was named a Senior Fellow of the Institute for Theoretical Research at ETH Zurich (2020), a Medallion Lecturer of IMS (2018), and a recipient of the Leo Breiman Junior Researcher Award (2017; American Statistical Association), the Guy Medal in Bronze (2014; Royal Statistical Society), and CAREER Award (2009; US National Science Foundation).