Yajun Mei
Yajun Mei
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Professor of Biostatistics
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Professional overview
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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.
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Education
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BS, Mathematics, Peking UniversityPhD, Mathematics, California Institute of Technology
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Honors and awards
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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 AwardThank 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)
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Publications
Publications
Asymptotic optimality theory for decentralized sequential multihypothesis testing problems
AbstractWang, Y., & Mei, Y. (n.d.).Publication year
2011Journal title
IEEE Transactions on Information TheoryVolume
57Issue
10Page(s)
7068-7083AbstractThe Bayesian formulation of sequentially testing M ≥ 3 hypotheses is studied in the context of a decentralized sensor network system. In such a system, local sensors observe raw observations and send quantized sensor messages to a fusion center which makes a final decision when stopping taking observations. Asymptotically optimal decentralized sequential tests are developed from a class of "two-stage" tests that allows the sensor network system to make a preliminary decision in the first stage and then optimize each local sensor quantizer accordingly in the second stage. It is shown that the optimal local quantizer at each local sensor in the second stage can be defined as a maximin quantizer which turns out to be a randomization of at most M-1 unambiguous likelihood quantizers (ULQ). We first present in detail our results for the system with a single sensor and binary sensor messages, and then extend to more general cases involving any finite alphabet sensor messages, multiple sensors, or composite hypotheses.Early detection of a change in poisson rate after accounting for population size effects
AbstractMei, Y., Won Han, S., & Tsui, K. L. (n.d.).Publication year
2011Journal title
Statistica SinicaVolume
21Issue
2Page(s)
597-624AbstractMotivated by applications in bio and syndromic surveillance, this article is concerned with the problem of detecting a change in the mean of Poisson distributions after taking into account the effects of population size. The family of generalized likelihood ratio (GLR) schemes is proposed and its asymptotic optimality properties are established under the classical asymptotic setting. However, numerical simulation studies illustrate that the GLR schemes are at times not as efficient as two families of ad-hoc schemes based on either the weighted likelihood ratios or the adaptive threshold method that adjust the effects of population sizes. To explain this, a further asymptotic optimality analysis is developed under a new asymptotic setting that is more suitable to our finite-sample numerical simulations. In addition, we extend our approaches to a general setting with arbitrary probability distributions, as well as to the continuous-time setting involving the multiplicative intensity models for Poisson processes, but further research is needed.Discussion on "Quickest detection problems: Fifty years later" by Albert N. Shiryaev
AbstractMei, Y. (n.d.).Publication year
2010Journal title
Sequential AnalysisVolume
29Issue
4Page(s)
410-414AbstractIn his interesting article Professor Shiryaev reviewed how he got motivated from real-applications of target detection in radar systems to develop sequential changepoint detection theory and also described different approaches to formulate mathematical problems. In our discussion of his article we focus on a couple of new real-world applications of sequential change-point detection and some new challenges as well.Efficient scalable schemes for monitoring a large number of data streams
AbstractMei, Y. (n.d.).Publication year
2010Journal title
BiometrikaVolume
97Issue
2Page(s)
419-433AbstractThe sequential changepoint detection problem is studied in the context of global online monitoring of a large number of independent data streams. We are interested in detecting an occurring event as soon as possible, but we do not know when the event will occur, nor do we know which subset of data streams will be affected by the event. A family of scalable schemes is proposed based on the sum of the local cumulative sum, cusum, statistics from each individual data stream, and is shown to asymptotically minimize the detection delays for each and every possible combination of affected data streams, subject to the global false alarm constraint. The usefulness and limitations of our asymptotic optimality results are illustrated by numerical simulations and heuristic arguments. The Appendices contain a probabilistic result on the first epoch to simultaneous record values for multiple independent random walks.Linear-mixed effects models for feature selection in high-dimensional NMR spectra
AbstractMei, Y., Kim, S. B., & Tsui, K. L. (n.d.).Publication year
2009Journal title
Expert Systems with ApplicationsVolume
36Issue
3Page(s)
4703-4708AbstractFeature selection in metabolomics can identify important metabolite features that play a significant role in discriminating between various conditions among samples. In this paper, we propose an efficient feature selection method for high-resolution nuclear magnetic resonance (NMR) spectra obtained from time-course experiments. Our proposed approach combines linear-mixed effects (LME) models with a multiple testing procedure based on a false discovery rate. The proposed LME approach is illustrated using NMR spectra with 574 metabolite features obtained for an experiment to examine metabolic changes in response to sulfur amino acid intake. The experimental results showed that classification models constructed with the features selected by the proposed approach resulted in lower rates of misclassification than those models with full features. Furthermore, we compared the LME approach with the two-sample t-test approach that oversimplifies the time-course factor.A comparison of methods for determining HIV viral set point
AbstractMei, Y., Wang, L., & Holte, S. E. (n.d.).Publication year
2008Journal title
Statistics in MedicineVolume
27Issue
1Page(s)
121-139AbstractDuring a course of human immunodeficiency virus (HIV-1) infection, the viral load usually increases sharply to a peak following infection and then drops rapidly to a steady state, where it remains until progression to AIDS. This steady state is often referred to as the viral set point. It is believed that the HIV viral set point results from an equilibrium between the HIV virus and immune response and is an important indicator of AIDS disease progression. In this paper, we analyze a real data set of viral loads measured before antiretroviral therapy is initiated, and propose two-phase regression models to utilize all available data to estimate the viral set point. The advantages of the proposed methods are illustrated by comparing them with two empirical methods, and the reason behind the improvement is also studied. Our results illustrate that for our data set, the viral load data are highly correlated and it is cost effective to estimate the viral set point based on one or two measurements obtained between 5 and 12 months after HIV infection. The utility and limitations of this recommendation will be discussed.Asymptotic optimality theory for decentralized sequential hypothesis testing in sensor networks
AbstractMei, Y. (n.d.).Publication year
2008Journal title
IEEE Transactions on Information TheoryVolume
54Issue
5Page(s)
2072-2089AbstractThe decentralized sequential hypothesis testing problem is studied in sensor networks, where a set of sensors receive independent observations and send summary messages to the fusion center, which makes a final decision. In the scenario where the sensors have full access to their past observations, the first asymptotically Bayes sequential test is developed having the same asymptotic performance as the optimal centralized test that has access to all sensor observations. Next, in the scenario where the sensors do not have full access to their past observations, a simple but asymptotically Bayes sequential tests is developed, in which sensor message functions are what we call tandem quantizer, where each sensor only uses two different sensor quantizers with at most one switch between these two possibilities. Moreover, a new minimax formulation of optimal stationary sensor quantizers is proposed and is studied in detail in the case of additive Gaussian sensor noise. Finally, our results show that in the simplest models, feedback from the fusion center does not improve asymptotic performance in the scenario with full local memory, however, even a one-shot, one-bit feedback can significantly improve performance in the case of limited local memory.Is average run length to false alarm always an informative criterion?
AbstractMei, Y. (n.d.).Publication year
2008Journal title
Sequential AnalysisVolume
27Issue
4Page(s)
354-376AbstractApart from Bayesian approaches, the average run length (ARL) to false alarm has always been seen as the natural performance criterion for quantifying the propensity of a detection scheme to make false alarms, and no researchers seem to have questioned this on grounds that it does not always apply. In this article, we show that in the change-point problem with mixture prechange models, detection schemes with finite detection delays can have infinite ARLs to false alarm. We also discuss the implication of our results on the change-point problem with either exchangeable prechange models or hidden Markov models. Alternative minimax formulations with different false alarm criteria are proposed.Sample size calculation for the van Elteren test adjusting for ties
AbstractZhao, Y. D., Rahardja, D., & Mei, Y. (n.d.).Publication year
2008Journal title
Journal of Biopharmaceutical StatisticsVolume
18Issue
6Page(s)
1112-1119AbstractIn this article we study sample size calculation methods for the asymptotic van Elteren test. Because the existing methods are only applicable to continuous data without ties, in this article we develop a new method that can be used on ordinal data. The new method has a closed form formula and is very easy to calculate. The new sample size formula performs very well because our simulations show that the corresponding actual powers are close to the nominal powers.A discussion on "Detection of intrusions in information systems by sequential change-point methods" by Tartakovsky, Rozovskii, Blažek, and Kim
Mei, Y. (n.d.).Publication year
2006Journal title
Statistical MethodologyVolume
3Issue
3Page(s)
304-306Comments on "a note on optimal detection of a change in distribution," by benjamin Yakir
AbstractMei, Y. (n.d.).Publication year
2006Journal title
Annals of StatisticsVolume
34Issue
3Page(s)
1570-1576AbstractThe purpose of this note is to show that in a widely cited paper by Yakir [Ann. Statist. 25 (1997) 2117-2126], the proof that the so-called modified Shiryayev-Roberts procedure is exactly optimal is incorrect. We also clarify the issues involved by both mathematical arguments and a simulation study. The correctness of the theorem remains in doubt.Sequential change-point detection when unknown parameters are present in the pre-change distribution
AbstractMei, Y. (n.d.).Publication year
2006Journal title
Annals of StatisticsVolume
34Issue
1Page(s)
92-122AbstractIn 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
AbstractMei, Y. (n.d.).Publication year
2006Journal title
Statistica SinicaVolume
16Issue
3Page(s)
883-897AbstractWe 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
AbstractMei, Y. (n.d.).Publication year
2005Journal title
IEEE Transactions on Information TheoryVolume
51Issue
7Page(s)
2669-2681AbstractThe 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.