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Dr. Zigler is an associate professor in the Department of Statistics and Data Science (College of Natural Sciences) and in the Department of Women's Health (Dell Medical School) at the University of Texas, Austin.  His research focuses on development of Bayesian methods for causal inference in complex observational studies, mostly motivated by problems public health and epidemiology.  His health policy interests lie mostly in air pollution regulatory policy and comparative effectiveness research using large administrative databases.



Dr. Hatfield is an Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School's Department of Health Care Policy. Her methods research centers on trade-offs among multiple outcomes, with an emphasis on hierarchical Bayesian modeling. She co-leads the Health Policy Data Science Lab and the Methods Core of the Healthcare Markets and Regulation Lab, funded by the Laura and John Arnold Foundation. Dr. Hatfield earned her BS in genetics from Iowa State University and her PhD in biostatistics from the University of Minnesota.



Dr. Wang is a Professor in the Department of Biostatistics and Department of Psychiatry at Columbia University, and a core member of the Division of Biostatistics at New York State Psychiatry Institute. Dr. Wang works on developing data-driven approaches to explore relationship between biomarkers, clinical markers, and health outcomes to assist discoveries in disease etiology and increase diagnostic capabilities of psychiatric and neurological diseases. Her research interests include statistical learning, analytics for precision medicine, evaluation of treatment effects, and novel design and analysis of clinical trials and electronic health records.



Coming soon



Jason S. Brinkley, PhD, MS, MA is a Senior Associate and Biostatistician at Abt Associates Inc. where he works on a wide variety of data for health services, policy, and disparities research. He maintains a research affiliation with the North Carolina Agromedicine Institute and an Adjunct Professor of Public Health at East Carolina University.  His work, out of Durham, NC, focused on traditional analytics and novel machine learning methods to surveys, geospatial data, electronic health records and claims data.  Follow him on Twitter @DrJasonBrinkley.

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Frank is a senior statistician at IBM Watson Health, where he supports clients in federal and state government agencies to reform the healthcare delivery system using evidence and statistical science. In his professional role, he focuses on innovative applications of statistical methods for quality and performance measurement, program evaluation, administrative data development, and machine learning for health outcomes of interest. Frank previously served on HPSS as communications officer and program chair and currently serves as representative to the ASA Council of Sections. He remains active in the Washington Statistical Society on the quantitative literacy committee, which supports regional science fairs to recognize high school students for excellence in the practice of statistical science.



Dr. Xie is the Milan and Maureen Ilich/Merck Frosst Chair Professor of Biostatistics in the Faculty of Health Sciences at Simon Fraser University. His research interests include developing tractable and distribution-free statistical methods for incomplete data and their applications in population, clinical and experimental studies of chronic diseases, such as arthritis diseases and cancer. These statistical methods can be used for more powerful evaluation of treatment, program and policy impact, more effective identification of risk factors for cancer, arthritis and other diseases, to quantify and improve the reliability of empirical results using imperfect data, to improve the usability and accessibility of large medical and administrative databases as well as policy simulations.

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Mousumi Banerjee is Anant M. Kshirsagar Collegiate Research Professor of Biostatistics at University of Michigan’s School of Public Health. She is also the Director of Biostatistics at the Center for Healthcare Outcomes and Policy, and Member of the Rogel Cancer Center at Michigan. Dr. Banerjee's research focuses on machine learning, correlated data, survival analyses, and competing risks, with applications to health services and outcomes research. She studies fundamental issues related to optimal quality, equitable care delivery, and treatment and outcome disparities in cancer and congenital heart disease. She received her BStat and MStat degrees from the Indian Statistical Institute and her PhD in Statistics from the University of Wisconsin. Dr. Banerjee is a Fellow of the American Statistical Association, and an elected member of the International Statistical Institute.



Dr. Han is an Assistant professor in the Quantitative Sciences Unit of the Stanford Biomedical Informatics Research in the Department of Medicine at Stanford University. Her research focuses on developing and applying statistical methods to evaluate efficient screening strategies for cancer, utilizing large cancer registry data, epidemiologic data, and administrative claims data.  Dr. Han is a member of the Cancer Intervention and Surveillance Modeling Network (CISNET) of the National Cancer Institute and has joined the efforts for developing microsimulation models for evaluating efficient lung cancer screening strategies in the U.S. since 2012. The areas of her research interests include cancer screening, health policy modeling, risk prediction modeling, machine learning, statistical genetics, and molecular epidemiology.



Ruth Etzioni is Full Member in Biostatistics at the Fred Hutchinson Cancer Research Center and Affiliate Professor in Biostatistics and Health Services at the University of Washington. Her research focuses on modeling and analytics to fill in the evidence gaps that inevitably arise when developing policies for cancer early detection and control. Much of her cancer modeling work is done in association with the Cancer Intervention and Surveillance Modeling Network (CISNET) of the NCI. She is also the curriculum designer and principal instructor for the graduate course “Advanced Research Methods: Large Public Databases, Big Data,” in the School of Public Health at the University of Washington. She spent many years not knowing where she belonged in the American Statistical Association before finding a home in HPSS. As HPSS chair for 2019 she is dedicated to creating community and making sure that others doing relevant work feel a sense of belonging within the section.

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Michael Baiocchi, PhD, is an Assistant Professor in the Department of Epidemiology and Population Health at Stanford University. He is an interventional-statistician, creating interventions and the means for analyzing them. He specializes in creating simple, easy to understand statistical methodologies for getting reliable results out of messy data and messy situations. His research is in nonparametric estimation and design-based inference. He was the inaugural Stein Fellow in the department of Statistics at Stanford University. He works on policy and health-outcome research in cardiothoracic surgery.

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Miguel Marino, PhD is an Associate Professor of Biostatistics in the Department of Family Medicine at Oregon Health & Science University. Miguel’s current research interest lies in population-based studies using large administrative observational data sources and electronic health records (EHRs). Specifically, his focus is on development and implementation of novel statistical methodology to address complexities associated with the use of EHRs to study changes in policy, health disparities, validation of EHRs as a reliable source for observation studies, and design/analysis of cluster-based randomized trials. Miguel currently serves as the statistical editor for the Annals of Family Medicine. Follow him on Twitter @MmMiguelmM


Dr. Hong is an Assistant Professor in the Department of Biostatistics and Bioinformatics at Duke University and a member of the Duke Clinical Research Institute. She is interested in integrating multiple data sources (e.g., multiple RCTs and RWD) to answer clinical and scientific questions in public health and medicine. She works on comparative effectiveness research, network meta-analysis, generalizability, measurement error, synthetic control in clinical trials, and Bayesian methods. Dr. Hong earned her MS in biostatistics from Harvard University and her PhD in biostatistics from the University of Minnesota

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