Meimei Liu
Department of Statistics
Virginia Tech
Emai: meimeiliu@vt.edu
About Me
I am an Assistant Professor in the Department of Statistics at Virginia Tech. Prior to VT, I worked with Prof. David B. Dunson as a post-doc in Department of Statistical Science at Duke University, where I develop methodology for nonparametric Bayesian, machine learning, and neural imaging. I was a Ph.D. student in Purdue University, where I was advised by Prof. Guang Cheng and Prof. Zuofeng Shang. I received a M.S. in statistics at University of Science and Technology of China, where I was supervised by Prof. Weiping Zhang.
Research Interest
Deep learning: graph embedding, variational autoencoding with application in neuroscience.
Bayesian data analysis: bayesian latent space model, bayesian nonparametric model.
Learning theory: stochastic gradient descent inference, variational inference.
Big data analysis: random projection, divide-and-conquer, active learning.
Semi/Non-parametric inference: kernel ridge regression, partially linear regression.
Research
Preprints
Domain Adaptive Bootstrap Aggregating
Meimei Liu, David Dunson
arXiv, 2020
Publications
Auto-encoding Graph-valued Data with Applications to Brain Connectomes.
Meimei Liu, Zhengwu Zhang, David Dunson
NeuroImage, 2021
Nonparametric Testing under Random Projection
Meimei Liu, Zuofeng Shang, Yun Yang, Guang Cheng
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Nonparametric Distributed Learning under General Designs
Meimei Liu, Zuofeng Shang, Guang Cheng
Electronic Journal of Statistics, 2020
Minimax Nonparametric Parallelism Test
[Software]
Xin Xing, Meimei Liu, Ping Ma, Wenxuan Zhong
Journal of Machine Learning Research (JMLR), 2020
Sharp Theoretical Analysis for Nonparametric Testing under Random Projection
Meimei Liu, Zuofeng Shang, Guang Cheng
Conference on Computational Learning Theory (COLT), 2019
Bayesian Joint Semiparametric Mean-Covariance Modeling for Longitudinal Data
Meimei Liu, Weiping Zhang, Yu Chen
Communications in Mathematics and Statistics, 2019
Early Stopping for Nonparametric Testing
Meimei Liu, Guang Cheng
Neural Information Processing Systems, 2018
Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression
Meimei Liu, Jean Honorio, Guang Cheng
Allerton Conference, 2018
Discussion on “Double sparsity kernel learning with automatic variable selection and data extraction”
Meimei Liu, Guang Cheng
Statistics and its Inference, 2018
Discussion on “Random-projection ensemble classification”
Meimei Liu, Guang Cheng
Journal of the Royal Statistical Society: Series B, 2017
Joint Semiparametric Mean-Covariance modeling by Moving Average Cholesky Decomposition for Longitudinal Data
Xin Xing, Meimei Liu, Weiping Zhang
Journal of University of Science and Technology of China, 2013
Grants
NVIDIA GPU Grant for Accelerated Data Science 2019
Awards
COLT Travel Award 2019
NeurIPs Travel Award 2018
Cagiantas Fellowship, Purdue University 2017-2018
Frederick N. Andrews Fellowship, Purdue University 2013-2017
Teaching
| Semester | Course | Course Title |
| Fall 2022 | CMDA 2005 | Integrated Quantitative Science |
| Fall 2021 | STAT 3615 | Biological Statistics |
| Spring 2021 | CMDA 2005 | Integrated Quantitative Science |
| Fall 2020 | CMDA 2005 | Integrated Quantitative Science |
| Fall 2019 | STAT 611 | Introduction to Mathematical Statistics |
| Fall 2018 | STAT 611 | Introduction to Mathematical Statistics |