**Ph.D. Candidate**

Department of Statistics

Purdue University

Office: HAAS 174

Emai: liu1197@purdue.edu

**Education**

Ph.D. of Statistics, Purdue University, USA, 2013 - 2018(expected)

Advisor:__Prof. Guang Cheng__M.S. in Statistics, University of Science and Technology of China, China. 2010 - 2013

Advisor:__Prof. Weiping Zhang__B.S. in Mathematics, Anhui University, China. 2006 - 2010

**Research Interest**

Big data analysis: random projection, divide-and-conquer

Machine learning: active learning, randomized algorithm

Semi/Non-parametric inference: kernel ridge regression, partially linear model

Bioinformatics

**Manuscripts**

**Meimei Liu**, Zuofeng Shang, Guang Cheng. (2017) Nonparametric testing under random projection.**Meimei Liu**, Zuofeng Shang, Guang Cheng. (2017) How many machines can we use in a distributed algorithm for kernel ridge regression?**Meimei Liu**, Jean Honorio, Guang Cheng. (2017) Statistically and computationally efficient variance estimator for kernel ridge regression.Xin Xing,

**Meimei Liu**, Wenxuan Zhong, Ping Ma. (2017) Global asymptotic component inference for bivariate smoothing spline ANOVA model.**Meimei Liu**, Guang Cheng. (2017) Parametric impact on nonparametric component for partially linear regression.

**Non-Refereed Discussions**

**Meimei Liu**, Guang Cheng. (2017)Discussion on “Random-projection ensemble classiﬁcation” by Timothy I. Cannings and Richard J. Samworth.*Journal of the Royal Statistical Society: Series B (Statistical Methodology)*, to appear.

**Honors and Awards**

Cagiantas Fellowship, Purdue University 2017-2018

Frederick N. Andrews Fellowship, Purdue University 2013-2017

**Teaching and Statistical Consulting**

STAT 301: Elementary Statistical Methods, lab TA Fall, 2015

STAT 350: Introduction to Statistics, ﬂipped section Fall, 2015

STAT 532: Elements of Stochastic Processes, grader Spring, 2017

Statistical Consulting Service, Consultant (2016) Work Description: Provided statistical analysis, experimental design, and software support to students/researchers. Edited SAS tutorial for internal use.

**Programming Skills**

R, Python, Matlab, SAS