Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer

Slideshow

Yao Xie

Xie
Georgia Tech

How do we quickly detect small solar flares in a large video stream generated by NASA satellites? How do we improve detection by efficient representation of high-dimensional data that is time-varying? Besides astronomical imaging, high-dimensional change-point detection also arises in many other applications including computer network intrusion detection, sensor networks, medical imaging, and epidemiology. In these problems, each dimension of the data is obtained by a sensor, and there are multiple sensors monitoring the emergence of a signal---an abrupt change in the distribution of the observations. The goal is to detect such a signal as soon as possible after it occurs, and make as few false alarms as possible. 

Two key challenges in high-dimensional change-point detection are 1) how to extract useful statistics, 2) how to find an efficient representation of the data. Many high-dimensional data exhibit low-dimensional structures such as sparsity, or the data may lie on a low-dimensional manifold. The approach I take is to exploit these low-dimensional structures in change-point detection. I will describe a mixture procedure that exploits sparsity, and MOUSSE, an online algorithm for tracking the evolving data manifold and extracts efficient statistics for change-point detection. 

More information about Yao Xie may be found at http://www2.isye.gatech.edu/~yxie77/

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.