Dr. Schaubel’s methodologic research interests mostly involve survival analysis and the analysis of recurrent event data. This is an advanced elective course for graduate students in Biostatistics, Statistics, Epidemiology, Bioinformatics, Computational Biology, and other BGS disciplines. Fei is also interested in statistical genetics and data integration. The Graduate Group in Epidemiology and Biostatistics (GGEB) is responsible for developing and administering the PhD degree programs in epidemiology and biostatistics as well as the MS program in biostatistics. He is also a Statistical Reviewer for JAMA Network Open. His more recent methodology has been at the intersection of causal inference and survival analysis and has focused on the evaluation of time-dependent treatments. The courses are offered in a highly distinctive context. https://sites.google.com/site/kyungheehanstat/. The work associated with these sources of support is an essential part of the graduate training program. https://sites.google.com/view/shulei-wang/. Projects that he worked on included the Scientific Registry of Transplant Recipients contract; the United States Renal Data System (USRDS) contract; and the Dialysis Outcomes and Practice Patterns Study (DOPPS). The Graduate Group in Epidemiology and Biostatistics (GGEB) is responsible for developing and administering the PhD degree programs in epidemiology and biostatistics as well as the MS program in biostatistics. Also interested in statistical genetics, especially data integration. Copyright © 2011 The University of Pennsylvania, PhD in Statistics, University of Florida, 2018, PhD in Applied Mathematics and Computational Science, University of Pennsylvania, 2016, PhD in Statistics, University of Chicago, 2012, PhD in Genetics, University of Chinese Academy of Sciences, 2016, Ph.D. in Statistics, UNC Chapel Hill in May, 2018, PhD in Statistics, Seoul National University, 2017, PhD in Statistics. Since 2005, Dr. Schaubel’s methodology has mostly been funded by an R01 grant, "Methods for the Analysis of Survival Processes Arising in End-Stage Renal Disease'' for which he is Principal Investigator. He received the Excellence in Research Award in 2015 from the University of Michigan School of Public Health. Changgee's research area of interest includes the development of Bayesian methodologies for the integrative analysis of high-dimensional multi-omics data, incorporating their structural information when present. We improve population health by linking clinical epidemiology and biostatistics within the Perelman School of Medicine, University of Pennsylvania Health System, and Penn community. Dr. Schaubel spent the first 17 years of his career at the University of Michigan Department of Biostatistics. Dr. Schaubel spent the first 17 years of his career at the University of Michigan Department of Biostatistics. Yulun is working on multivariate and network meta-analysis for individual patient-level data. The goal of the MS program is to train students in the theory and application of statistical methods in the biomedical sciences. The program typically consists of two years of full-time study, including the preparation of a Master's thesis. She is also developing methods for analyzing next generation sequencing data, including shotgun metagenomics data sets. MS (Biostatistics), Harvard, 2019 BS (Mathematics/Global Business Administration), Sung Kyun Kwan University, 2017 PhD in Biostatistics. Working on semiparametric/nonparametric methods and scalable inference algorithms, with applications to electronic health records (EHRs). The mission of the PhD program in Epidemiology is to train independent researchers in the development and application of epidemiologic methods and to prepare them for positions as scientific leaders in academia and industry. E-Mail: Shulei.Wang@pennmedicine.upenn.edu Education: PhD in Statistics, University of Wisconsin-Madison, 2018 Faculty/Mentor: Hongzhe Li and Tony Cai Shulei is broadly interested in developing novel statistical methods and computational tools with solid theoretical foundation for analysis of biomedical data, including metagenomic and microscopy imaging data. Our response to COVID-19: roundup of learning resources, news, published research.

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