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.

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Dr. Lix is a Professor of Biostatistics in the Department of Community Health Sciences at the University of Manitoba, Canada, a Tier 1 Canada Research Chair in Methods for Electronic Health Data Quality, and Director of the Data Science Platform in the George & Fay Yee Centre for Healthcare Innovation at the University of Manitoba. Her research expertise lies in statistical methods for complex healthcare data and patient-reported outcome measures. She collaborates widely with research groups and organizations across Canada, including Health Data Research Network Canada, Canadian Network for Observational Drug Effect Studies, and the Public Health Agency of Canada. She is co-lead of the Visual and Automated Disease Analytics (VADA) training program that is offered at the University of Manitoba and University of Victoria, and also of the national Artificial Intelligence for Public Health (AI4PH) Summer Institute.

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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. Zubizarreta is an associate professor in the Department of Health Care Policy at Harvard Medical School, an associate professor in the Department of Biostatistics at Harvard School of Public Health, and a faculty affiliate in the Department of Statistics at the Faculty of Arts and Sciences at Harvard University.  His work centers on the development of statistical methods for causal inference and impact evaluation to advance research in health care and public policy. In his methodological work, Dr. Zubizarreta is broadly interested in the design and analysis of randomized experiments and observational studies. In his health care work, he is interested in assessing the quality of care provided by hospitals and physicians using health outcomes and operations measures.  His research interests also encompass comparative effectiveness research and health program impact evaluation.

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Dr. Theodore (Ted) Lystig is SVP, Chief Analytics Officer at BridgeBio, where he provides leadership and guidance in the use of robust statistical and research design methods throughout the company. He also holds the position of Adjunct Assistant Professor within the Division of Biostatistics at the University of Minnesota. Dr. Lystig is an elected Fellow of the American Statistical Association (ASA) and an elected Member of the International Statistical Institute (ISI). He is a founding officer and past Chair for the Section on Medical Devices and Diagnostics within the ASA and an Executive Committee member of the Clinical Trials Transformation Initiative (CTTI). Dr. Lystig is an internationally recognized industry leader in statistical methodology, especially in the area of active surveillance for medical devices.

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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.

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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.



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

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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