The mission of the SR2c is to pursue sound and reproducible research, support statistical study design, analysis and interpretation, without borders.
We offer a broad range of statistical expertise, conduct state-of-the-art statistical research and computing, thrive in challenging interdisciplinary research or collaboration requiring statistical practice or innovations, and support the training and education of next-generation scientists.
For more information, feel free to contact us at sr2c AT case DOT edu.
Professor Sun received her Ph.D. in Statistics from Stanford University. She is Professor of Statistics at the Department of Population and Quantitative Health Sciences, formerly the Department of Epidemiology and Biostatistics, Case Western Reserve University.
She is an elected Fellow of the American Statistical Association (ASA), an elected Fellow of Institute of Mathematical Statistics (IMS), and an elected Member of International Statistical Institute (ISI).
Professor Sunâ€™s statistical research is integrated with methodology, theory and computing, from small, moderate samples to complex, huge, or large-dimensional data; and for heterogeneous or longitudinal data with missing information, measurement errors, or selection biases.
Dr. Ma received his Ph.D in statistics from Case Western Reserve University and worked at SAMSI and NISS. His expertise includes statistics, data visualization, computing, and statistical package development. His research interests include clustering analysis, Bayesian data analysis, selection bias, survival analysis, and clinical trials. Dr. Ma is also a Data Scientist at Dow Chemical.
Dr. Yifan Xu received his Ph.D in Mathematical Sciences from Binghamton University. His expertise and fields of interest include statistical inference in clinical research, statistical modeling in high dimensional data analysis and statistical software development. In addition to his adjunct faculty position, Dr. Xu works at IBM Watson Health as a data scientist, developing predictive models and algorithms and performing ad-hoc studies.
Jang Ik is pursuing his Ph.D in modern biostatistics from the department of Epidemiology and Biostatistics at Case Western Reserve University School of Medicine. He has an MS in biostatistics from Harvard School of Public Health and has worked as a biostatistician at Channing Lab and TIMI Study in Brigham and Women's Hospital in Boston MA. His interest is in developing statistical methods for big and high-demensional data and causal mediation anaysis.
Arielle Bloostein is pursuing her MS in Computer Science at the School of Engineering at Case Western Reserve University. She has a BA in Psychology from Case Western Reserve University's College of Arts and Sciences. Her interests are in machine learning and data analytics.
Youjun Li is pursuing his Ph.D in modern biostatistics from the department of Epidemiology and Biostatistics at Case Western Reserve University School of Medicine. He has an MS in Statistics from Case Western Reserve University School of Arts and Science as well as an MS in Economics from University of Freiburg in Germany. He is also a research assistant in Professor Jeffrey Albert's team. His research interests include Bayesian Inference, machine learning and longitudinal data analysis.
Jang Ik Cho
Katie Pezzot - Battelle Memorial Institute
Yulei Wang - Harvard University
James Matthiesen - London School of Economics
Ali Mahmoud - Senior at Case Western (Pre-Med)
Sheng Yang - Working on research in Neurology Dept.
Rebecca R. Carter
Nishant Sudhir Barlinge
Yuchen Han - Washington University in St. Louis
Joseph Sedransk received his Ph.D. in Statistics from Harvard University. He was the founding chair and professor in the Department of Statistics at Case Western Reserve University and is currently the founding co-editor of the Journal of Survey Statistics and Methodology (started in 2012 and sponsored by ASA). He is a Fellow of the ASA and an Elected Member of the ISI.
Professor Sedransk's research interests include Bayesian inference, sample survey theory and methodology, and he has published prolifically in these areas. In addition to his University appointments, he has served as an ASA/NSF Fellow at Bureau of Labor Statistics, and expert Statistician for the Energy Information Administration, as well as the U.S. Department of Energy.
Heping Zhang received his Ph.D. in Statistics from Stanford University. He is the Susan Dwight Bliss Professor of Public Health in Biostatistics, Professor of the Child Study Center, and Professor of Statistics at Yale University. He is a Fellow of the ASA, and a Fellow of the IMS. In addition, Professor Zhang was named the 2008 Myrto Lefokopoulou distinguished lecturer by Harvard School of Public Health and a Medallion Lecturer by the IMS. In 2011, he received the Royan International Award on Reproductive Health.
Professor Zhang's research interests include: Biostatistics, Genomics, Epidemiology, Psychiatry, Pregnancy, Infertility, Substance Use and Bioinformatics. Members of Zhang's center conduct research in both the general area of regression and classification analyses and the methodologies for post-genome data analyses.
Myron Katzoff received his Ph.D. in Mathematical Statistics from The George Washington University. He has been a Senior Mathematical Statistician for the National Center for Health Statistics (NCHS) at the CDC, and was Branch Chief, Health Surveys and Supplements Branch (HSSB), Statistical Methods Division, U.S. Bureau of the Census, and statistician at National Health Insurance Modeling Group (NHIMG), Health Care Financing Administration, among other positions. He has also been a member of IMS and held positions as Vice-Chair for ASA Council of Sections Governing Board (2011-2013) and Program Chair for the Section on Statistics in Defense of the ASA (2010); and, at his local Washington Statistical Society (WSS), as co-chair for Defense and National Security and several others.
Dr. Katzoff's expertise includes survey design and analysis, hierarchical Bayesian nonresponse models, time series analysis and spatial-temporal modeling for Biosurveillance, small area estimation and adaptive sampling.