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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.



Dr. Parast is an Associate Professor in the Department of Statistics and Data Sciences at the University of Texas at Austin. Her statistical research has focused on developing robust methods to evaluate surrogate markers, robust estimation of treatment effects, and developing and evaluating risk prediction procedures for long term survival. Her applied research has focused on measuring and comparing health care quality, and survey design and analysis for health care related surveys in a variety of settings including the emergency department, inpatient hospital, hospice, and pediatric setting. Prior to joining UT Austin, she was a senior statistician at the RAND Corporation and co-director of RAND's Center for Causal Inference. 



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.

Executive Committee: Skills


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.



Dr. O’Malley is the Peggy Y. Thomson Professor of the Evaluative Clinical Sciences in The Dartmouth Institute of Health Policy and Clinical Practice and Professor in The Department of Biomedical Data Science at the Geisel School of Medicine at Dartmouth. His methodological interests in statistics encompass social network analysis, multivariate hierarchical models, causal inference using instrumental variables, and Bayesian inference. Much of his work is motivated by problems in health services research. He has published over 240 peer-reviewed research papers. He chaired the HPSS in 2008 and co-chaired its International Conference in 2011. In 2011, he received the HPSS Mid-career Excellence award, in 2012 became an elected fellow of the ASA, and in 2019 received the ISPOR Methodological award for scientific excellence.

Executive Committee: Skills


Dr. Brinkley is a biostatistician, principal data scientist, and health researcher who leads the Research Design and Analytics team in Digital and Data Services Division at Abt Associates. He has expertise in a wide range of analytic methods, and he specializes in machine learning, customized data visualizations, maps, and dash-boarding. He balances research time between examining theoretical ways to assess the impact of medical interventions on public health and developing best practices for using statistical software. Before joining Abt, Dr. Brinkley was a senior researcher at American Institute of Research. Prior to that, he was an assistant professor of biostatistics at East Carolina University, where he remains as a research affiliate with the school’s North Carolina Agromedicine Institute (NCAI). H has a passion for rural health, methods for exploring disparities, and in evaluating bias in machine learning and other algorithms.

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Dr. Shafie Khorassani is an Assistant Professor of Biostatistics at the Boston University School of Public Health. Her statistical methodology research focuses on data integration methods for time-to-event outcomes, causal inference for observational data, and statistical methods for the evaluation of surrogate outcomes. Her work is motivated by studying health disparities using complex observational data sources. She also does work on the use of cigarettes, electronic nicotine delivery systems, and other tobacco products. She earned her PhD in Biostatistics from the University of Michigan. 



Dr. Burgette is a Senior Statistician at the RAND Corporation. He is also a faculty member at Pardee RAND Graduate School. His applied research primarily focuses on health policy including Medicare’s physician payment policies, evaluations of Medicare Innovation Models, and research on high-deductible health plans. Other recent applied research includes a large-scale evaluation of the 2020 census in California, estimation of recidivism risk for employment background checks, and research on the impacts of state gun laws. His statistical research interests include methods for causal inference and Bayesian modeling in the health and social sciences. Dr. Burgette has worked in methodological areas related to categorical outcomes, propensity score methods, methods for missing data, and nonparametric modeling. He received his Ph.D. in statistics from the University of Wisconsin.

Executive Committee: Skills


Dr. Griffin is a senior statistician at the RAND Corporation and codirector the NIDA-funded RAND/USC Opioid Policy Tools and Information Center (OPTIC). Her statistical research has focused on methods for estimating causal effects using observational data. Her public health research has primarily fallen into three areas: (1) the effects of gun and opioid state policies on outcomes, (2) substance use treatment evaluations for adolescents, and (3) the impact of nongenetic factors on Huntington's disease. She codirected the RAND Center for Causal Inference between 2013 and 2018 and became an ASA Fellow in 2023. Dr. Griffin served has Area Editor for the Annals of Applied Statistics from 2013 to 2023. She received her Ph.D. in biostatistics from Harvard University.

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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

Executive Committee: Skills
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