Education

• Ph.D. of Statistics, Purdue University, USA, 2013 - 2018(expected)
  Jointly supervised by Prof. Guang Cheng and Prof. Zuofeng Shang
  

• M.S. in Statistics, University of Science and Technology of China, China. 2010 - 2013
  Supervised by 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

Manuscripts

• Meimei Liu, Zuofeng Shang, Guang Cheng. (2018) Nonparametric testing under random projection.

• Meimei Liu, Guang Cheng. (2018) Early Stopping for Nonparametric Testing.

• Meimei Liu, Zuofeng Shang, Guang Cheng. (2018) 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