Xin (Shayne) Xing, Ph.D. is an Assistant Professor in the Department of Statistics at Virginia Tech. Prior to joining Virginia Tech, he worked with Jun Liu as a post-doc in Department of Statistics at Harvard University. His research interests include nonparametric testing, smoothing spline, dimension reduction, and controlled variable selection. Xin has extensive experience in machine learning techniques (neural networks, domain adaptation, transfer learning) and computational biology, including metagenomics, single cell, epigenomics, and neuroimaging.
- Xin Xing, Meimei Liu, Wenxuan Zhong, Ping Ma. (2020). Minimax Nonparametric Parallelism Test, Journal of Machine Learning Research, 21(94):1-47.
- Xin Xing, Long Sha, Pengyu Hong, Zuofeng Shang, Jun S. Liu (2020). Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks. International Conference on Learning Representations (ICLR)
- Xin Xing, Yu Gui, Chengguang Dai, Jun S. Liu (2020). Deep Gaussian Mirror for Controlled Variable Selection, IEEE ICMLA, 2020.
- Terry Ma, Di Xiao, Xin Xing (Corresponding author) (2019). MetaBMF: A Scalable Binning Algorithm for Large-scale Reference-free Metagenomic Studies. Bioinformatics.
- Xin Xing, Jun S. Liu,Wenxuan Zhong (2018). MetaGen: reference-free learning with multiple metagenomic samples. Genome biology, 18 (1), 187, 2018.
- Terry Ma, Xin Xing (2018). A Scalable Reference-Free Metagenomic Binning Pipeline. International Symposium on Bioinformatics Research and Applications, 79-83.
- Yiwen Liu, Xin Xing, Wenxuan Zhong (2018). Sufficient Dimension Reduction for Tensor Data. Handbook of Big Data Analytics, Springer.
- Wenxuan Zhong, Xin Xing, Kenneth Suslick (2015). Tensor Sufficient Dimension Reduction WIREs Computational Statistics, 7(3), 178-184.
- Xin Xing, Jinjin Hu, and Yaning Yang (2014). Robust minimum variance portfolio with L-infinity constraints, Journal of Banking and Finance, 46, 107-117.
- Xin Xing, Meimei Liu, Weiping Zhang (2014). Joint Semiparametric Mean-Covariance Modeling by Moving Average Cholesky Decomposition for Longitudinal Data, Journal of University of Science and Technology of China, 43(8), 607-621.