SEMINAR 2024-10-02

Streaming Data Analysis for Efficient Quickest Decision-Making

Dr. Yajun Mei  

Department of Biostatistics  

School of Global Public Health  

New York University 

Wednesday, October 2^nd, 2024 

1:30 P.M. – 2:30 P.M. Eastern Time 

Nguyen Engineering Building, Room 5115 (Dean’s Conference Room)  

4511 Patriot Circle, Fairfax, VA 

The seminar talk is also live-streamed. Please register here to receive the link.

(Large-scale) streaming data are generated or encountered in many real-world applications ranging from bio surveillance and public/personal health to environmental monitoring and network security to finance and economics and so on. Often one would like to utilize observed streaming data to make efficient sequential or quickest decision. In the first part of this talk, we provide an overview of the classical sequential analysis and change-point detection problem for one-dimensional data. Next, we present our latest research on sequential change-point detection algorithms that are statistically efficient and computationally scalable when monitoring high-dimensional streaming data. Two scenarios will be considered: one is passive sampling when all data are passively observed at each time, and the other is active sampling when the decision maker is responsible to actively choose partial data to be observed. Asymptotic analysis, numerical studies, and their adaptations to case studies such as hot-spots detection of images or infectious diseases will be presented.

Yajun Mei is a Professor of the Department of Biostatistics in the School of Global Public Health at New York University. Prior to joining NYU, he had been an Assistant/Associate/Full Professor in H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at the Georgia Institute of Technology, Atlanta, GA during 2006 and June 2024, and was a co-director of Biostatistics, Epidemiology, Research Design (BERD) at Georgia Clinical & Translational Science Alliance. He received the B.S. degree in Mathematics from Peking University, Beijing, China, in 1996, and the Ph.D. degree in Mathematics with a minor in Electrical Engineering from California Institute of Technology, Pasadena, CA, USA, in 2003. He did a Post Doc in biostatistics in the renowned Fred Hutchinson Cancer Research Center in Seattle, WA, USA during 2003-2005.  

Yajun's main research interests are (bio)statistics, machine learning, and data science, and their applications in biomedical sciences and engineering, particularly streaming data analysis, change-point problems, sequential analysis and active/reinforcement learning in Statistics and Machine Learning, as well as precision/personalized medicine, hot-spots detection for infectious diseases, longitudinal data analysis and clinical trials in Biostatistics. His research has been supported by the NSF and NIH, and his work has received several recognitions including 2009 and 2024 Abraham Wald Prize in Sequential Analysis, 2010 NSF CAREER Award, 2023 ASA Fellow, a plenary speaker in 2024 International Workshop on Sequential Methodologies, and multiple best paper awards. 

Ben Seiyon Lee

Jonathan L. Auerbach