Meimei Liu
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)
Jointly supervised by Prof. Guang Cheng and Prof. Zuofeng ShangM.S. in Statistics, University of Science and Technology of China, China. 2010 - 2013
Supervised by Prof. Weiping ZhangB.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. (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