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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

Hot-spots detection in count data by Poisson assisted smooth sparse tensor decomposition

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Pivotal Estimation of Linear Discriminant Analysis in High Dimensions

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Repetitive Low-level Blast Exposure and Neurocognitive Effects in Army Ranger Mortarmen

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Robust High-Dimensional Linear Discriminant Analysis under Training Data Contamination

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Surge Capacity in the COVID-19 Era : a Natural Experiment of Neurocritical Care in General Critical Care

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Active Quickest Detection When Monitoring Multi-streams with Two Affected Streams

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Adaptive Partially Observed Sequential Change Detection and Isolation

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Asymptotic Theory of `1-Regularized PDE Identification from a Single Noisy Trajectory

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Cannabis Use Is Not Associated with Aneurysmal Subarachnoid Hemorrhage Complications or Outcomes

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Does intrathecal nicardipine for cerebral vasospasm following subarachnoid hemorrhage correlate with reduced delayed cerebral ischemia? A retrospective propensity score-based analysis

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Efficient Sequential UCB-based Hungarian Algorithm for Assignment Problems

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Implicit Regularization Properties of Variance Reduced Stochastic Mirror Descent

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Predicting the rheology of limestone calcined clay cements (LC3) : Linking composition and hydration kinetics to yield stress through Machine Learning

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Private Sequential Hypothesis Testing for Statisticians : Privacy, Error Rates, and Sample Size

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Rapid detection of hot-spots via tensor decomposition with applications to crime rate data

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Robust change detection for large-scale data streams

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ROBUSTNESS AND TRACTABILITY FOR NONCONVEX M-ESTIMATORS

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The Directional Bias Helps Stochastic Gradient Descent to Generalize in Kernel Regression Models

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Treatment Effect Modeling for FTIR Signals Subject to Multiple Sources of Uncertainties

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A boosting inspired personalized threshold method for sepsis screening

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Aneurysmal Subarachnoid Hemorrhage : Trends, Outcomes, and Predictions from a 15-Year Perspective of a Single Neurocritical Care Unit

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Correlation-based dynamic sampling for online high dimensional process monitoring

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Creation of a Pediatric Choledocholithiasis Prediction Model

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Editorial : Mathematical Fundamentals of Machine Learning

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Multi-Stream Quickest Detection with Unknown Post-Change Parameters under Sampling Control

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Contact

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