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Streaming data analysis for efficient quickest decision-making

Event Type
Industrial and Enterprise Systems Engineering, Dept. Head office
Room 303, Transportation Building (104 S. Mathews Ave, Urbana)
Feb 24, 2023   10:00 - 11:00 am  
Professor Yajun Mei
BuuLinh Quach
Originating Calendar
ISE Seminar Calendar

*Presentation will be recorded.


(Large-scale) streaming data are generated or encountered in many real-world applications ranging from manufacturing engineering, biosurveillance and public/personal heath 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 quickest or sequential 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 schemes 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 adaptions to case studies such as hot-spots detection of images or infectious diseases will be presented.


Yajun Mei (Pronounced as "YA-JUNE MAY") is a Professor in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at the Georgia Institute of Technology, Atlanta, GA. He is a co-director of Biostatistics, Epidemiology, Research Design (BERD) at Georgia Clinical & Translational Science Alliance and the coordinator of the MS STAT program in ISyE at Georgia Tech. 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 for joining Georgia Tech in 2006. 

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 Abraham Wald Prize in Sequential Analysis, 2010 NSF CAREER Award, a plenary speaker in 2023 International Workshop on Sequential Methodologies (expected), and multiple best paper awards.  

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