Robust sequential change-point detection

Yao Xie
Assistant Professor, Georgia Tech
Date: Mar. 16th, 2018

Abstract

Sequential change-point detection is a fundamental problem in statistics and signal processing, with broad applications in security, network monitoring, imaging, and genetics. Given a sequence of data, the goal is to detect any change in the underlying distribution as quickly as possible from the streaming data. Various algorithms have been developed including the commonly used CUSUM procedure. However, there is a still a gap when applying change-point detection methods to real problems, notably, due to the lack of robustness. Classic approaches usually require exact specification of the pre and post change distributions forms, which may be quite restrictive and do not perform well with real data. On the other hand, Huber’s classic robust statistics built based on least favorable distributions are not directly applicable since they are computationally intractable in the multi-dimensional setting. In this seminar, I will present several of our recent works in developing computationally efficient and robust change-point detection algorithms with certain near optimality properties, by building a connection of statistical sequential analysis with (online) convex optimization.

Bio

Yao Xie is an Assistant Professor and Harold R. and Mary Anne Nash Early Career Professor in the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. She received her Ph.D. in Electrical Engineering (minor in Mathematics) from Stanford University in 2011. Prior joining Georgia Tech in 2013, she worked as a Research Scientist at Duke University. Her research interests are statistics, signal processing, and machine learning. She received a Best Student Paper Award at Annual Asilomar Conference on Signals, Systems and Computers in 2005, Finalist of Best Student Paper Award in ICASSP Conference in 2007, and the National Science Foundation (NSF) CAREER Award in 2017.