I am an Associate Professor of Statistics and Data Science at Washington University in St. Louis. I received my Ph.D. degree in statistics at the University of Chicago. My Ph.D. adviser is Prof. Wei Biao Wu. Prior to graduate school, I obtained my B.S. in mathematics at Tsinghua University.

I am intersted in high dimensional data analysis, time series, statistical learning theory.

Teaching

Paper

1. Stability and asymptotics for autoregressive processes, 2016
   Likai Chen and Wei Biao Wu, Electronic Journal of Statistics, Vol. 10, 3723-3751, 2016. [pdf]
2. Concentration inequalities for empirical processes of linear time series, 2018
   Likai Chen and Wei Biao Wu, Journal of Machine Learning Research, 18(231):1-46, 2018. [pdf]
3. Testing for trends in high-dimensional time series, 2019
   Likai Chen and Wei Biao Wu, Journal of the American Statistical Association, 2019, Vol. 114, No. 526, 869-881: Theory and Methods. [pdf]
4. Dynamic semiparametric factor model with structural breaks, 2020
   Likai Chen, Weining Wang and Wei Biao Wu, Journal of Business and Economic Statistics, 2020, Vol. 00, No. 0, 1-15. [pdf]
5. Inference of break points in high-dimensional time series
   Likai Chen, Weining Wang and Wei Biao Wu, Journal of the American Statistical Association, 2021, Vol. 00, 1-13 [pdf]
6. Estimation of nonstationary nonparametric regression model with multiplicative structure
   Likai Chen, Ekaterina Smetanina and Wei Biao Wu, Econometrics journal, 2021, Vol. 00, pp. 1–39. [pdf]
7. Change point detection for high-dimensional time series based on maxima of Gaussian processes
   Likai Chen, Jia He, Maggie Cheng and Wei Biao Wu, Submitted in ICASSP [pdf]
8. MicrobiomeCensus: Estimating Human Population Sizes from Wastewater Samples Based on Inter-Individual Variability in Gut Microbiomes
   Fangqiong Ling, Likai Chen, Lin Zhang, Xiaoqian Yu, Claire Duvallet, Siavash Isazadeh, Chengzhen Dai, Shinkyu Park, Katya Frois-Moniz, Fabio Duarte, Carlo Ratti, and Eric J. Alm Submitted. [pdf]

Invited Talk

1. Statistical Learning for Time Dependent data
   Michigan State University, Oct 2018,
   University of Illinois Urbana-Champaign, March 2018,
   Northwestern University, Feb 2018.
2. Concentration inequalities for empirical processes of linear time series,
   INFORMS Applied Probability Society Conference, 2017.
3. Testing for trends in high-dimensional time Series,
   ICSA Applied Statistics Symposium, 2017.

Service