Xin (Mike)
Bing
Ph.D. candidate
Department of Statistics and Data Science
Xin Bing is the Ph.D. candidate in the Department of Statistics and Data Science. Before joining to Cornell, he received a BS in Mathematics in 2013 and an MS in Mathematics and Financial Engineering in 2016 from Shandong University, China. He also received an MS in Statistics in 2016 from University of Washington, Seattle.
His research interest lies in developing new methodology with theoretical guarantees to tackle modern statistical learning and high-dimensional statistical problems, such as dimension reduction, clustering and inference. He is also interested in the application of developed methods to various fields, such as genetics, social science and neuroscience.
Publications
- Xin Bing, Florentina Bunea, Seth Strimas-Mackey and Marten Wegkamp. Prediction in latent factor regression: Adaptive PCR and beyond. https://arxiv.org/abs/2007.10050
- Xin Bing, Yang Ning and Yaosheng Xu. Adaptive estimation of multivariate regression with hidden variables. https://arxiv.org/abs/2003.13844.
- Xin Bing, Florentina Bunea and Marten Wegkamp. Inference in interpretable latent factor regression models. https://arxiv.org/abs/1905.12696.
- Xin Bing, Florentina Bunea and Marten Wegkamp. Optimal estimation of sparse topic models. Accepted by JMLR. https://arxiv.org/abs/2001.07861.
- Xin Bing, Florentina Bunea, Martin Royer and Jishnu Das. Latent Model-Based Clustering for Biological Discovery. iScience (2019), 14, 125 - 135.
- Xin Bing, Florentina Bunea and Marten Wegkamp. A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics. Bernoulli (2020).
- Xin Bing, Florentina Bunea, Yang Ning and Marten Wegkamp. Sparse Latent Factor Models with Pure Variables for Overlapping Clustering. Annals of Statistics (2019).
- Xin Bing and Marten Wegkamp. Adaptive estimation of the rank of the coefficient matrix in high dimensional multivariate response regression models. Annals of Statistics (2019).
- Xin Bing and Marten Wegkamp. Discussion of Random-projection Ensemble Classification by Timothy I. Cannings and Richard J. Samworth. J. R. Statist. Soc. B, 79(4), 1006-1007 (2017).