Yajun Mei

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

Sequential change-point detection when unknown parameters are present in the pre-change distribution

Mei, Y. (n.d.).

Publication year

2006

Journal title

Annals of Statistics

Volume

34

Issue

1

Page(s)

92-122

10.1214/009053605000000859

Abstract

Abstract

In the sequential change-point detection literature, most research specifies a required frequency of false alarms at a given pre-change distribution f θ and tries to minimize the detection delay for every possible post-change distribution g λ. In this paper, motivated by a number of practical examples, we first consider the reverse question by specifying a required detection delay at a given post-change distribution and trying to minimize the frequency of false alarms for every possible pre-change distribution f θ. We present asymptotically optimal procedures for one-parameter exponential families. Next, we develop a general theory for change-point problems when both the prechange distribution f θ and the post-change distribution g λ involve unknown parameters. We also apply our approach to the special case of detecting shifts in the mean of independent normal observations.

Suboptimal properties of page's CUSUM and shiryayev-roberts procedures in change-point problems with dependent observations

Mei, Y. (n.d.).

Publication year

2006

Journal title

Statistica Sinica

Volume

16

Issue

3

Page(s)

883-897

Abstract

Abstract

We construct a simple counterexample to the conjectures of Pollak (1985) and Yakir, Krieger and Pollak (1999), which state that Page's CUSUM procedure and the Shiryayev-Roberts procedure are asymptotically minimax optimal for dependent observations. Moreover, our example shows that the close relationship between open-ended tests and change-point detection procedures no longer holds for dependent observations. As a consequence, the standard approach which constructs change-point detection procedures based on asymptotically optimal openended tests does not in general provide asymptotically optimal procedures for dependent observations.

Information bounds and quickest change detection in decentralized decision systems

Mei, Y. (n.d.).

Publication year

2005

Journal title

IEEE Transactions on Information Theory

Volume

51

Issue

7

Page(s)

2669-2681

10.1109/TIT.2005.850159

Abstract

Abstract

The quickest change detection problem is studied in decentralized decision systems, where a set of sensors receive independent observations and send summary messages to the fusion center, which makes a final decision. In the system where the sensors do not have access to their past observations, the previously conjectured asymptotic optimality of a procedure with a monotone likelihood ratio quantizer (MLRQ) is proved. In the case of additive Gaussian sensor noise, if the signal-to-noise ratios (SNR) at some sensors are sufficiently high, this procedure can perform as well as the optimal centralized procedure that has access to all the sensor observations. Even if all SNRs are low, its detection delay will be at most π/2 - 1 ≈ 57% larger than that of the optimal centralized procedure. Next, in the system where the sensors have full access to their past observations, the first asymptotically optimal procedure in the literature is developed. Surprisingly, the procedure has the same asymptotic performance as the optimal centralized procedure, although it may perform poorly in some practical situations because of slow asymptotic convergence. Finally, it is shown that neither past message information nor the feedback from the fusion center improves the asymptotic performance in the simplest model.

Information bounds and asymptotically optimal procedures for detecting changes in decentralized decision systems

Mei, Y. (n.d.).

Publication year

2004

Journal title

IEEE International Symposium on Information Theory - Proceedings

Page(s)

249

Abstract

Abstract

Information bounds and asymptotically optimal procedures for decentralized quickest change under different scenarios were discussed. The decentralized system with limited local memory, where the sensors do not have access to their past observations was considered. It was shown that in the decentralized decision system with Gaussian sensor observations, the detection delay of the monotone likelihood ratio quantizer (MLRQ) is at most π/2-1 ≈ 57% larger than that of the optimal centralized procedure. It was found that the method can be easily extended to non-Gaussian distributions.