- Ph.D. – Statistics – Purdue University 2018
- M.S. -Statistics – University of Science and Technology of China 2013
- B.S. Mathematics, Anhui University, China 2010
Awards and Honors
- New Faculty Mentoring Grant, Virginia Tech, 2020-2022
- Cagiantas Fellowship, Purdue University 2017-2018
- Frederick N. Andrews Fellowship, Purdue University 2013-2017
- Member of American Statistical Association
- Member of International Chinese Statistical Association
- Member of Women in Machine Learning
List of courses taught
- CMDA 2005: Integrated Quantitative Science I
List of research projects
- Deep learning: graph embedding, variational inference with application in neuroscience.
- Learning theory: stochastic gradient descent inference, variational inference.
- Big data analysis: domain adaptation, random projection, divide-and-conquer, active learning.
- Semi/Non-parametric inference
- Meimei Liu, Zuofeng Shang, Guang Cheng. Nonparametric distributed learning under general designs. Electronic Journal of Statistics 14.2 (2020): 3070-3102.
- Xin Xing, Meimei Liu, Wenxuan Zhong, Ping Ma. Minimax Nonparametric Parallelism Test. Journal of Machine Learning Research 21.94 (2020): 1-47.
- Meimei Liu, Zuofeng Shang, Guang Cheng. Sharp Theoretical Analysis for Nonparametric Testing under Random Projection. Conference on Computational Learning Theory (COLT) 2019, in Proceedings of Machine Learning Research 99:2175-2209.
- Meimei Liu, Guang Cheng. Early Stopping for Nonparametric Testing. Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 3985–3994.
- Meimei Liu, Jean Honorio, Guang Cheng. Statistically and computationally efficient variance estimator for kernel ridge regression. 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton), IEEE, 1005-1011.