Taming Complexity in High-Dimensional Inference
Abstract
High-dimensional data is increasingly pervasive in modern applications, posing significant challenges for statistical inference. In this talk, we will explore various approaches for tackling complexity in high-dimensional problems. We will discuss methods for efficient computation and model selection, as well as strategies for dealing with sparsity and noise. The goal is to develop a deeper understanding of the fundamental trade-offs between estimation accuracy, computational resources, and model complexity in the context of high-dimensional statistical inference.
Bio
Michael Jordan is a Professor of Electrical Engineering and Computer Science at the University of California, Berkeley, where he also holds joint appointments in the Department of Statistics and the Department of Molecular and Cell Biology. He obtained his Ph.D. in Cognitive Science from the University of California, San Diego in 1985. Jordan's research interests span the areas of machine learning, statistical inference, computational biology, and artificial intelligence. He is a fellow of the American Academy of Arts and Sciences, the National Academy of Engineering, and the National Academy of Sciences.
