Meimei Liu
Department of Statistics
Virginia Tech
Emai: meimeiliu@vt.edu
About Me
I am an Associate 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
Publications
[1] Scalable inference for Nonparametric Stochastic Approximation in Reproducing Kernel Hilbert Spaces
Meimei Liu, Zuofeng Shang, Yun Yang.
Annals of Statistics, 2026
[2] Ensemble Computational Pipelines for Robust Machine Learning with Application in Manufacturing
Yixin Chen*, Xiaoyu Chen, Ran Jin,
Meimei Liu
Informs Journal on Data Science, 2026. (* PhD student)
[3] Domain Adaptive Bootstrap Aggregating
Meimei Liu, David Dunson.
IEEE Transactions on Signal Processing, 2025
[4] Differential roles of deterministic and stochastic processes in structuring soil bacterial ecotypes across terrestrial ecosystems
Mia Riddley, Shannon Hepp, FNU Hardeep, Aruj Nayak, Meimei Liu, Xin Xing, Hailong Zhang, Jingqiu Liao (2025).
Nature communications, 2025
[5] DuST: Dual Swin Transformer for video and time-series multi-modal modeling
Liang Shi, Yixin Chen, Meimei Liu, Feng Guo
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
[6] Motion-Invariant Variational Auto-Encoding of Brain Structural Connectomes
Yizi Zhang, Meimei Liu, Zhengwu Zhang, David Dunson
Imaging Neuroscience, 2024
[7] Auto-encoding Graph-valued Data with Applications to Brain Connectomes.
Meimei Liu, Zhengwu Zhang, David Dunson
NeuroImage, 2022
[8] Nonparametric Testing under Random Projection
Meimei Liu, Zuofeng Shang, Yun Yang, Guang Cheng
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
[9] Nonparametric Distributed Learning under General Designs
Meimei Liu, Zuofeng Shang, Guang Cheng
Electronic Journal of Statistics, 2020
[10] Minimax Nonparametric Parallelism Test
[Software]
Xin Xing, Meimei Liu, Ping Ma, Wenxuan Zhong
Journal of Machine Learning Research (JMLR), 2020
[11] Sharp Theoretical Analysis for Nonparametric Testing under Random Projection
Meimei Liu, Zuofeng Shang, Guang Cheng
Conference on Computational Learning Theory (COLT), 2019
[12] Bayesian Joint Semiparametric Mean-Covariance Modeling for Longitudinal Data
Meimei Liu, Weiping Zhang, Yu Chen
Communications in Mathematics and Statistics, 2019
[13] Early Stopping for Nonparametric Testing
Meimei Liu, Guang Cheng
Neural Information Processing Systems, 2018
[14] Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression
Meimei Liu, Jean Honorio, Guang Cheng
Allerton Conference, 2018
[15] Discussion on “Double sparsity kernel learning with automatic variable selection and data extraction”
Meimei Liu, Guang Cheng
Statistics and its Inference, 2018
[16] Discussion on “Random-projection ensemble classification”
Meimei Liu, Guang Cheng
Journal of the Royal Statistical Society: Series B, 2017
[17] 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
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 |