A Statistical and Computational Foundation for Tensor Learning

Seminar Series

Monday, August 30, 2021 - 12:00
Zoom Conference
Anru Zhang, PhD

Abstract: The analysis of tensor data has become an active research topic in data science recently. Many high-order datasets arising from a wide range of modern applications, such as genomics, material science, and neuroimaging analysis, requires modeling with high-dimensional tensors. Tensor methods also provide unique perspectives and solutions to many high-dimensional problems where the observations are not necessarily tensors. High-dimensional tensor problems possess distinct characteristics that pose unprecedented challenges. There is a need to develop novel methods, algorithms, and theories to analyze the high-dimensional tensor data. In this talk, we discuss some recent advances in high-dimensional tensor data analysis through several fundamental topics and their applications in microscopy imaging and neuroimaging. We will also illustrate how we develop new statistically optimal methods, computationally efficient algorithms, and fundamental theories that exploit information from high-dimensional tensor data based on the modern theory of computation, non-convex optimization, applied linear algebra, and high-dimensional statistics. 

Speaker: Anru Zhang, PhD
                Department of Biostatistics and Bioinformatics
                Duke University School of Medicine

The Duke Computer Science Colloquium 

Please contact Jennifer Schmidt at jschmidt@cs.duke.edu to request the Zoom link for this event.