H. Milton Stewart School of Industrial and
Georgia Institute of Technology
Phone: (404) 894-2334
Email: ymei AT isye.gatech.edu
H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology
Atlanta, GA 30332-0205
I received a B.S. in Mathematics
in 1996 from Peking University, P.R. China, and a Ph.D. in Mathematics
with a minor in Electrical Engineering in 2003 from the California Institute of
Technology, Pasadena, CA. I have also worked as a Postdoc in
Biostatistics for two years in the Fred Hutchinson Cancer Research Center in
Seattle, WA. My current research interests include change-point problems and sequential
analysis in Mathematical Statistics; sensor networks and information theory in
Engineering; as well as longitudinal data analysis, random effects models, and
clinical trials in Biostatistics.
1. K. Liu, R. Zhang and Y. Mei. Scalable
SUM-Shrinkage schemes for distributed monitoring large-scale data streams,
2. S.E. Holte, E. Lee
and Y. Mei. Symmetric
directional false discovery rate control, 2015.
3. Y. Li and Y. Mei. D-ary
sequential tests of circular error probability, 2015.
Zhang, J. Wang, and Y. Mei. Classification of paper citation trajectories
through functional Poisson regression model, 2016.
- Y. Li and Y. Mei. Effect
of bivariate data’s correlation on sequential tests of circular error
probability. Journal of
Statistical Planning and Inference, vol. 171, page 99-114, 2016.
- Yuan Wang and Y. Mei. Large-Scale
multi-stream quickest change detection via shrinkage post-change
Transactions on Information Theory, vol. 61, page 6926-6938, 2015.
Fuh and Y.
Mei. Quickest change detection and Kullback-Leibler divergence for two-state hidden
Markov models. IEEE Transactions
on Signal Processing, vol. 63, No. 18, page 4866-4878, 2015.
Liu, Y. Mei and J. Shi. An adaptive sampling strategy for online
high-dimensional process monitoring. Technometrics, vol. 57, No.
3, page 305-319, 2015.
Wang, Y. Mei and D. Hicks. Comments on
“Quantifying long-term scientific impact”. Science, vol. 345 no. 6193, page 149, 2014.
Wang and Y. Mei. Quantization
effect on the log-likelihood ratio and its application to decentralized
sequential detection. IEEE
Transactions on Signal Processing, vol. 61, issue 6, page 1536-1543,
Wang and Y. Mei. A
multistage procedure for decentralized sequential multi-hypothesis testing
problems. Sequential Analysis,
vol. 31, pp. 505-527, 2012.
- Y. Wang and Y. Mei. Asymptotic
optimality theories for decentralized multi-hypothesis sequential
Transactions on Information Theory, vol. 57, issue 10, page 7068-7083,
Mei, S. W. Han and K. Tsui. Early
detection of a change in Poisson rate after accounting for population size
effects. Statistica Sinica, vol. 21, page 597-624, 2011.
- Y. Mei. Efficient
scalable schemes for monitoring a large number of data streams. Biometrika,
vol. 97, page 419-433, 2010.
- Y. Mei, “Is average
run length to false alarm always an informative criterion?” (with discussions), Sequential
Analysis, vol. 27, page 354-419, 2008.
- Y. Mei, “Asymptotic
optimality theory for decentralized sequential hypothesis testing in
sensor networks,” IEEE
Transactions on Information Theory, vol. 54, issue 5, page 2072-2089, 2008.
- Y. Mei, L. Wang, and S. E. Holte.
comparison of methods for determining HIV viral set point.” Statistics in Medicine, vol. 27,
issue 1, page 121-139, 2008.
- Y. Mei, Suboptimal
properties of Page’s CUSUM and Shiryayev-Roberts
procedures in change-point problems with dependent observations. Statistica Sinica, vol. 16, page 883-897, 2006.
- Y. Mei, Comments on
“A note on optimal detection of a change in distribution” by Benjamin Yakir The
Annals of Statistics, vol.
34, no. 3, page 1570-1576, 2006.
- Y. Mei, Sequential
change-point detection when unknown parameters are present in the
pre-change distribution. The Annals of Statistics, vol. 34, no. 1, page 92-122, 2006.
- Y. Mei, Information
bounds and quickest change detection in decentralized decision systems.
Transactions on Information Theory, vol. 51, issue 7, page 2669-2681, 2005.
- Y. Mei, Asymptotically
optimal methods for sequential change-point detection. Ph.D. thesis, 2003.
Course(s) I am teaching this semester