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Speakers: Dr. Yajun Mei, Georgia Institute of Technology
Title:
Monitoring Large-Scale Data Streams via Shrinkage
Abstract:
In the modern information age one often monitors a large number of data stream with the aim of offering the potential for early detection of a ``trigger" event, e.g., quality control, (bio)surveillance, health care, security and environmental science. In this talk, we develop efficient global monitoring schemes by using the tools from the subfield of sequential change-point detection in statistics. In the first part of this talk, we provide a brief overview of the classical sequential change-point detection problem where one is monitoring a single data stream whose distribution changes at an unknown time. In the second part of the talk, we extend the classical sequential change-point detection algorithms to high-dimensional setting via shrinkage estimation. Both asymptotic analysis and numerical simulations demonstrate the usefulness of shrinkage in the context of online monitoring large-scale data streams.
Speaker Bio:
Dr. Mei received the Ph.D. degree in mathematics with a minor in electrical engineering from the California Institute of Technology in 2003, and the B.S. degree in mathematics from Peking University, P. R. China in 1996. He is currently an associate professor in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at the Georgia Institute of Technology. He was a postdoctoral researcher in biostatistics and biomathematics in the Fred Hutchinson Cancer Research Center, Seattle, WA, from 2003 to 2005. He received the 2009 Abraham Wald Prize in Sequential Analysis and an NSF CAREER award during 2010-2015. His research interests include change-point detection, sensor networks, sequential analysis, and their applications in engineering and biomedical sciences.
