New Researcher Awards


Fei Xue

Fei is a Postdoc Researcher under the supervision of Professor Hongzhe Li in Department of Biostatistics, Epidemiology and Informatics at University of Pennsylvania. She received her PhD degree in Statistics at University of Illinois Urbana-Champaign in 2019, advised by Professor Annie Qu. Prior to UIUC, she obtained her bachelor’s degree from School of Mathematical Sciences at Fudan University in 2014. Fei’s general research goal is to develop statistical methods for improving data integration, variable selection, and mediation analysis. She is also interested in missing data, high dimensional data, precision medicine, statistical genetics, and survival analysis.


Zijian Guo

Zijian Guo is an Assistant Professor in Department of Statistics, at Rutgers University. He has obtained Bachelor degree in Mathematics from The Chinese University of Hong Kong in 2012 and Ph.D. in Statistics at University of Pennsylvania in 2017, under the supervision of Professor Tony Cai. His research interests include High-dimensional statistics, Nonparametric Statistics, Causal inference and Econometrics. Specifically, he has been working on uncertainty quantification in high-dimensional regression, instrumental variable analysis, mediation analysis, additiv


Xin Zhang

Xin (Henry) Zhang is Assistant Professor, Department of Statistics, Florida State University. He received his B.S. in Applied Physics from University of Science and Technology of China (USTC) in 2008, and Ph.D. in Statistics from University of Minnesota in 2014. His research interests include applied and multivariate statistics, statistical machine learning, high-dimensional discriminant analysis and clustering, tensor data analysis, neuroimaging data analysis, sufficient dimension reduction and envelope methodology. He is currently serving as Associate Editor of Biometrics. He is a member of the American Statistical Association, the Institute of Mathematical Statistics, and the International Chinese Statistical Association.


Anru Zhang

Anru Zhang is currently an assistant professor at the Department of Statistics, University of Wisconsin-Madison. He is also affiliated to  Machine Learning Group and Institute for Foundations of Data Science at UW-Madison. He obtained the PhD degree from University of Pennsylvania in 2015 and a bachelor’s degree from Peking University in 2010. His current research interests include Statistical Learning Theory, High-dimensional Statistical Inference, and Tensor Data Analysis. He received grants from the National Science Foundation and the National Institute of Health.


Timothy Cannings

Tim is a lecturer in Statistics and Data Science in the School of Mathematics, University of Edinburgh, and a Turing Fellow at the Alan Turing Institute. He completed his PhD with Prof Richard Samworth in the Statistical Laboratory at the University of Cambridge in 2015. He also worked with Prof Yingying Fan as a Postdoc at the University of Southern California until August 2018. 


Yang Ni

Yang Ni is currently Assistant Professor in Department of Statistics, Research Affiliate at Texas A&M Institute of Data Science (TAMIDS), and Co-Director of Center for Statistical Bioinformatics at Texas A&M University. He received his PhD in Statistics from Rice University in 2015, supervised by Francesco Stingo and Veerabhadran Baladandayuthapani. He completed his postdoctoral training supervised by Peter Müller from the University of Texas at Austin in  2018. His current interests lie in the development of novel Bayesian graphical models, Bayesian nonparametric models, and scalable computation algorithms to address various scientific questions with single-cell data, microbiome data, and electronic health records data. 

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Jue Hou

Jue “Marquis” Hou is currently a postdoctoral fellow in Dr. Tianxi Cai ’s lab at the Department of Biostatistics, Harvard T.H. Chan School of Public Health. He received his Ph.D. in Mathematics with specialization in Statistics in 2019 at University of California San Diego, advised by Dr. Ronghui Xu and Dr. Jelena Bradic. Before that, he received his Bachelor in Mathematics at Fudan University in 2011 and his M.S. in Statistics at the University of Illinois at Urbana-Champaign in 2013. His research covers survival analysis, inference and causal inference with high-dimensional data, semi-supervised learning, as well as various application in cancer, pregnancy, HIV, addiction and EMR.

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Fan Zhou

Fan Zhou is currently an assistant professor in the school of statistics and management at shanghai university of finance and economics. Prior to joining SUFE, he got a PhD degree in biostatistics from the university of North Carolina at chapel hill at 2019, co-advised by Prof Hongtu Zhu and Prof Haibo Zhou. Fan received his master’s degree in applied mathematics from Johns Hopkins University and bachelor’s degree in economics from Huazhong University of Science and Technology. 

During his PhD career, Fan’s research was mainly about statistical genetics, biased sampling, and biomedical imaging studies. He did a lot of work in these fields and published several papers at Nature Genetics, Biometrics, Neuroimage, Bioinformatics and so on. 

Now, he has shifted his interests to the joint study of traditional statistics and deep learning, especially the research in graph convolution networks and spatial-temporal systems. His most recent works have been accepted by some top AI and machine learning conferences such as NIPS and MICCAI. 

Fan builds some long-term collaboration with technology companies, such as didi chuxing. He helped them design advanced algorithms to efficiently solve many real-world problems. Fan have developed and currently maintain several software and their online manuals on GitHub. 

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Imaizumi Masaaki

Masaaki Imaizumi is an assistant professor at the Department of Statistical Inference and Mathematics, the Institute of Statistical Mathematics, which is a national research institute in Japan. He mainly works on mathematical statistics, statistical machine learning, and theoretical aspects of deep learning. His recent research focuses on developing a statistical theory to describe a mechanism of the great performance of deep learning. He obtained his Ph.D. degree in 2017 at the Department of Statistics, the Graduate School of Economics, the University of Tokyo. His dissertation develops several statistical methods and theories for the nonparametric regression problem with complex data. During his Ph.D. course, he interned NEC Cooperation and NEC Laboratories America. Currently, he is also working as a visiting research fellow at the Center for Advanced Intelligence Project, RIKEN, and a research fellow at Presto, the Japan Science and Technology Agency.


Tianxi Li

Tianxi Li is currently an assistant professor in the Department of Statistics at the University of Virginia. Tianxi Li finished his undergraduate study at a statistics major at Zhejiang University in 2010. He earned his Ph.D. in statistics from the University of Michigan in 2018, under the supervision of Prof. Liza Levina and Ji Zhu. His current research interests main include statistical network analysis and statistical learning.


Anderson Zhang

Anderson Ye Zhang is an assistant professor in Department of Statistics at the Wharton School, the University of Pennsylvania. Before joining Wharton, he was a William H. Kruskal Instructor in Department of Statistics at the University of Chicago. He obtained his PhD degree from Department of Statistics and Data Science at Yale University, advised by Professor Harrison Zhou. He graduated from Zhejiang University with a Bachelor degree in Statistics. Anderson has a broad interest in the theory and application of statistics and machine learning. His current research interests include: network analysis, mean field varatioinal inference, theoretical guarantees of iterative algorithms, clustering and mixture models, and spectral methods.