Description: Yajun Mei                          Yajun Mei

          Coca-Cola Associate Professor
            H. Milton Stewart School of Industrial and Systems Engineering
            Georgia Institute of Technology
            Phone: (404) 894-2334
            Fax: 404-894-2301

            Email: ymei AT isye dot gatech dot edu

           


Mailing Address:

H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology
Atlanta, GA 30332-0205


Bio:

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 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. Currently I am interested in high-dimensional streaming data analysis over networks, and their applications in engineering and biomedical sciences. 

Preprints/Manuscripts

1.     S. Holte and Y. Mei. Precision in the specification of ordinary differential equations and parameter estimation in modelling biological processes, 2016.

2.     Y. Li and Y. Mei. D-ary sequential tests of circular error probability, 2015.



Selected Publications

1.     R. Zhang, J. Wang, and Y. Mei. Search for evergreens in science: a functional data analysis, Journal of Informetrics, 2017 (Accepted).

2.     K. Liu, R. Zhang and Y. Mei. Scalable SUM-Shrinkage schemes for distributed monitoring large-scale data streams, Statistica Sinica, 2017 (Accepted).

3.     S.E. Holte, E. Lee and Y. Mei. Symmetric directional false discovery rate control, Statistical Methodology, vol. 33, page 71-82, 2016.

4.     K. Liu and Y. Mei. Discussion on “Sequential detection/isolation of abrupt changes’ by Igor Nikiforov, Sequential Analysis, vol. 35, page 316-319, 2016.

5.     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.

6.     Yuan Wang and Y. Mei. Large-Scale multi-stream quickest change detection via shrinkage post-change estimation. IEEE Transactions on Information Theory, vol. 61, page 6926-6938, 2015.

7.     C.D. 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.

8.     K. 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. 

9.     J. Wang, Y. Mei and D. Hicks. Comments on “Quantifying long-term scientific impact”. Science, vol. 345 no. 6193, page 149, 2014. 

10.  Y. 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, 2013.

11.  Y. Wang and Y. Mei. A multistage procedure for decentralized sequential multi-hypothesis testing problems. Sequential Analysis, vol. 31, pp. 505-527, 2012.

12.  Y. Wang and Y. Mei. Asymptotic optimality theories for decentralized multi-hypothesis sequential detection. IEEE Transactions on Information Theory, vol. 57, issue 10, page 7068-7083, 2011.

13.  Y. 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.

14.  Y. Mei. Efficient scalable schemes for monitoring a large number of data streams. Biometrika, vol. 97, page 419-433, 2010.

15.  Y. Mei, “Is average run length to false alarm always an informative criterion?” (with discussions), Sequential Analysis, vol. 27, page 354-419, 2008.

16.  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.

17.  Y. Mei, L. Wang, and S. E. Holte. “A comparison of methods for determining HIV viral set point.” Statistics in Medicine, vol. 27, issue 1, page 121-139, 2008.

18.  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.

19.  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.

20.  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.

21.  Y. Mei, Information bounds and quickest change detection in decentralized decision systems. IEEE Transactions on Information Theory, vol. 51, issue 7, page 2669-2681, 2005.

22.  Y. Mei, Asymptotically optimal methods for sequential change-point detection. Ph.D. thesis, 2003.

 

Course(s) I am teaching this semester

  • Spring 2017: ISyE 7406 (Data Mining and Statistical Learning)