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Yajun Mei

Yajun Mei

Yajun Mei

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Professor of Biostatistics

Professional overview

Yajun Mei is a Professor of Biostatistics at NYU/GPH, starting from July 1, 2024. 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 the California Institute of Technology, Pasadena, CA, USA, in 2003. He was a Postdoc in Biostatistics in the renowned Fred Hutch Cancer Center in Seattle, WA during 2003 and 2005.  Prior to joining NYU, Dr. Mei was an Assistant/Associate/Full Professor in H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology, Atlanta, GA for 18 years from 2006 to 2024, and had been a co-director of Biostatistics, Epidemiology, and Study Design (BERD) of Georgia CTSA since 2018.  

Dr. Mei’s research interests are statistics, machine learning, and data science, and their applications in biomedical science and public health, particularly, streaming data analysis, sequential decision/design, change-point problems, precision/personalized medicine, hot-spots detection for infectious diseases, longitudinal data analysis, bioinformatics, and clinical trials. His work has received several recognitions including Abraham Wald Prizes in Sequential Analysis in both 2009 and 2024, NSF CAREER Award in 2010, an elected Fellow of American Statistical Association (ASA) in 2023, and multiple best paper awards.

Education

BS, Mathematics, Peking University
PhD, Mathematics, California Institute of Technology

Honors and awards

Fellow of American Statistical Association (2023)
Star Research Achievement Award, 2021 Virtual Critical Care Congress (2021)
Best Paper Competition Award, Quality, Statistics & Reliability of INFORMS (2020)
Bronze Snapshot Award, Society of Critical Care Medicine (2019)
NSF Career Award
Thank a Teacher Certificate, Center for Teaching and Learning (2011201220162020202120222023)
Abraham Wald Prize (2009)
Best Paper Award, 11th International Conference on Information Fusion (2008)
New Researcher Fellow, Statistical and Applied Mathematical Sciences Institute (2005)
Fred Hutchinson SPAC Travel Award to attend 2005 Joint Statistical Meetings, Minneapolis, MN (2005)
Travel Award to 8th New Researchers Conference, Minneapolis, MN (2005)
Travel Award to IEEE International Symposium on Information Theory, Chicago, IL (2004)
Travel Award to IPAM workshop on inverse problem, UCLA, Los Angeles, CA (2003)
Fred Hutchinson SPAC Course Scholarship (2003)
Travel Award to the SAMSI workshop on inverse problem, Research Triangular Park, NC (2002)

Publications

Publications

Quickest change detection and Kullback-Leibler divergence for two-state hidden Markov models

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Quickest Change Detection and Kullback-Leibler Divergence for Two-State Hidden Markov Models

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Comment on "Quantifying long-term scientific impact"

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Online parallel monitoring via hard-thresholding post-change estimation

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Discussion on "Change-Points : From Sequential Detection to Biology and Back" by David O. Siegmund

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Quantization effect on the log-likelihood ratio and its application to decentralized sequential detection

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A multistage procedure for decentralized sequential multi-hypothesis testing problems

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Quantization effect on second moment of log-likelihood ratio and its application to decentralized sequential detection

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Asymptotic optimality theory for decentralized sequential multihypothesis testing problems

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Early detection of a change in poisson rate after accounting for population size effects

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Quickest detection in censoring sensor networks

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Decentralized multihypothesis sequential detection

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Discussion on "Quickest detection problems : Fifty years later" by Albert N. Shiryaev

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Efficient scalable schemes for monitoring a large number of data streams

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Decentralized two-sided sequential tests for a normal mean

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Linear-mixed effects models for feature selection in high-dimensional NMR spectra

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A comparison of methods for determining HIV viral set point

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Asymptotic optimality theory for decentralized sequential hypothesis testing in sensor networks

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Author's responses

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Is average run length to false alarm always an informative criterion?

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Optimal stationary binary quantizer for decentralized quickest change detection in hidden Markov models

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Sample size calculation for the van Elteren test adjusting for ties

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A discussion on "Detection of intrusions in information systems by sequential change-point methods" by Tartakovsky, Rozovskii, Blažek, and Kim

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Comments on "a note on optimal detection of a change in distribution," by benjamin Yakir

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Information bounds for decentralized sequential detection

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Contact

yajun.mei@nyu.edu 708 Broadway New York, NY, 10003