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Meimei Liu

Assistant Professor


  • 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.