SCI Board
PRESIDENT
Luke Keele
University of Pennsylvania
Surgery and Biostatistics
Luke Keele (Ph.D., University of North Carolina, Chapel Hill, 2003) is currently an Associate Professor at the University of Pennsylvania with joint appointments in Surgery and Biostatistics. Professor Keele specializes in research on applied statistics with a focus on causal inference, design-based methods, matching, natural experiments and instrumental variables. He also conducts research on topics in educational program evaluation, election administration, and health services research. He has published articles in the Journal of the American Statistical Association, Annals of Applied Statistics, Journal of the Royal Statistical Society, Series A, The American Statistician, American Political Science Review, Political Analysis, and Psychological Methods.
PRESIDENT-ELECT
Dylan Small
Dylan Small, PhD is the Universal Furniture Professor in the Department of Statistics and Data Science at the Wharton School at the University of Pennsylvania. Dr. Small received his PhD in Statistics from Stanford University and his AB in Mathematics from Harvard University. Dr. Small’s research interests include causal inference and its applications to health and public policy. He was the founding Editor of the journal Observational Studies and continues to serve on its editorial board.
TREASURER
Marie - Abele Bind
I am an Instructor of Investigation at the Biostatistics Center at Massachusetts General Hospital (MGH) and partly funded by an NIH Early Independence Award (DP5) I received in 2016. I obtained a joint doctoral degree in Biostatistics and Environmental Health under the guidance of Profs. Schwartz and Coull at the Harvard School of Public Health in 2014. During my thesis, I, for instance, worked on causal mediation analysis with Prof. VanderWeele. After my thesis, I became a two-year Ziff postdoctoral fellow at the Harvard University Center for the Environment and learned how to apply the Rubin Causal Model to address causal questions related to extreme temperatures. In 2017, I became a John Harvard Distinguished Science Fellow (JHDSF) at the Department of Statistics, Harvard University, and developed and applied causal inference methods to examine whether environmental exposures (e.g., air pollution) are related to poorly-understood diseases (e.g., multiple sclerosis). Recently, I have promoted the use of randomization-based inference, especially in the context of air pollution chamber studies. In 2021, I accepted an academic position of Instructor of Investigation at the MGH Biostatistics Center. I am being recommended for a faculty appointment as Assistant Professor at the Harvard Medical School, Department of Medicine. At MGH, I am part of the teams of blinded biostatisticians of the current HEALEY platform trial on amyotrophic lateral sclerosis (ALS) and of the RECOVER team, a NIH initiative that seeks to understand, prevent, and treat long COVID. Since the beginning of my thesis, I have attended and presented my work at national and international research meetings. I have also been invited to give talks and short courses on causal inference in prestigious European, Asian, and US research institutes and universities.
SECRETARY
Nandita Mitra
University of Pennsylvania
Nandita Mitra, PhD is Professor of Biostatistics and Vice Chair of Education in the Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania. She is also a Co-Director of the Center for Causal Inference at Penn. She received her BA in Mathematics from Brown University, MA in Biostatistics from the University of California, Berkeley, her PhD in Biostatistics from Columbia University, and completed a postdoctoral fellowship at Harvard. Her primary research area is causal inference with applications in cancer outcomes, health policy, and health economics. Dr. Mitra is the Editor-in-Chief of Observational Studies, Chair-Elect of the ASA Statistics in Epidemiology section, and is a Fellow of the American Statistical Association.
MEMBER AT LARGE
Carrie Cihak
Carrie Cihak leads evidence-informed practice and partnerships for the regional government of the 12th largest county in the United States. Cihak guides County agencies on community outcomes and impact, with a strong focus on advancing racial equity. Former roles at the County include three years leading learning and evidence practice and partnerships at King County Metro Transit; serving as an inaugural member of the community mitigation team in the County’s pandemic response; and eight years as Chief of Policy for the King County Executive with responsibility for identifying the highest priority policy areas and community outcomes for leadership focus and developing and launching innovative solutions to complex, controversial, and cross-sector issues. Cihak has served as sponsor for the County’s nationally-recognized work on equity and social justice and is architect of several County initiatives such as Best Starts for Kids. Cihak also served for eight years as a senior-level policy and budget analyst for the King County Council and as lead staff for the King County Board of Health.
Cihak is trained as a Ph.D.-level (ABD) economist specializing in Japan and served as staff economist on international trade and finance for President Clinton’s Council of Economic Advisers. Cihak is a Local Government Fellow and serves on the Advisory Committee for the Federal Standards of Excellence at Results for America, a non-profit organization that supports all levels of government in making the use of data and evidence in decision making the “new normal”. Cihak was the first government Policy Fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University (CASBS) in 2017-18 and has been a Research Affiliate at CASBS since that time. With Jake Bowers (University of Illinois – Statistics & Political Science), Cihak co-founded and co-directs the Causal Inference for Social Impact Lab at CASBS that brings policymakers and academic researchers together to innovate on causal inference methodology and practice.
MEMBER AT LARGE
Rocío Titiunik
Princeton University
Rocío Titiunik is Professor of Politics at Princeton University, where she is also an associated faculty with the Department of Operations Research and Financial Engineering, the Center for Statistics and Machine Learning, the Program in Latin American Studies, the Center for the Study of Democratic Politics, and the Research Program in
Political Economy. She specializes in quantitative methodology for the social and behavioral sciences, with emphasis on quasi-experimental methods for causal inference and program evaluation. Her research
interests lie at the intersection of political economy, political science, statistics, and data science, particularly on the development and application of quantitative methods to the study of political institutions. Her recent methodological research includes the development of statistical methods for regression discontinuity (RD)
designs. Her recent substantive research centers on democratic accountability and the role of party systems in developing democracies. She received the Emerging Scholar Award from the Society for Political Methodology in 2016, which honors a young researcher who is making notable contributions to the field of political methodology, and was elected as fellow of the Society for Political Methodology in 2020. Rocio is one of the Principal Investigators of the Empirical Implications of Theoretical Models (EITM) Summer Institute, and has
served in various leadership roles for the American Political Science Association and for the Society for Political Methodology. She is currently associate editor for Science Advances and a member of the Board of Reviewing Editors for Science. She is also currently a member of the Advisory Committee for the Social, Behavioral, and Economic Sciences Directorate of the National Science Foundation, and an elected member of the council of the Midwest Political Science Association. Previously, she was an associate editor for the American Journal of Political Science, and a member of the advisory panel for Methodology, Measurement, and Statistics program of the National Science Foundation. Rocío was born and raised in Buenos Aires, Argentina, where she completed her undergraduate education at the Universidad de Buenos Aires. She received her Ph.D. in Agricultural and Resource Economics from UC-Berkeley in May 2009. Between 2010 and 2019, she was a faculty member in the Department of Political Science at the University of Michigan, where she was also affiliated with the Center for Political Studies and the Michigan Institute for Data Science.
SCI Steering Committee
Nominations are now being accepted for the inaugural elections for leadership positions. Click here to learn more.
Stephen Cole
University of North Carolina, Chapel Hill
Department of Epidemiology
Stephen R. Cole works to build accurate and impactful knowledge, particularly population-health (epidemiologic) knowledge.
Professor Cole is interested in study designs and analyses that accurately estimate parameters of central interest to population-health scientists, such as risk. These study designs include randomized experiments, pseudoexperiments (i.e., observational studies) and thought-experiments (e.g., simulation studies). Substantively, Dr. Cole is interested in infectious diseases, primarily HIV, and cancer.
Jennifer Hill
Jennifer Hill develops and evaluates methods that help answer causal questions vital to policy research and scientific development. Her ast work focused on situations in which it is difficult or impossible to perform traditional randomized experiments, or when even seemingly pristine study designs are complicated by grouped structures or missing data. Most recent work focuses on Bayesian nonparametric methods that allow for flexible estimation of causal models and are less time-consuming and more precise than competing methods (e.g. propensity score approaches). These approaches intersect with other causal inference topics such as common support violations, sensitivity analysis, and estimation of heterogeneous effects. Hill has published in a variety of leading journals including Journal of the American Statistical Association, Statistical Science, American Political Science Review, American Journal of Public Health, and Developmental Psychology. She earned her PhD in Statistics at Harvard University in 2000 and completed a post-doctoral fellowship in Child and Family Policy at Columbia University’s School of Social Work in 2002. Hill is current the Director of the Center for Practice and Research at the Intersection of Information, Society, and Methodology (PRIISM) and Co-Director of and the Master’s of Science Program in Applied Statistics for Social Science Research (A3SR).
Luke Keele
University of Pennsylvania
Surgery and Biostatistics
Luke Keele (Ph.D., University of North Carolina, Chapel Hill, 2003) is currently an Associate Professor at the University of Pennsylvania with joint appointments in Surgery and Biostatistics. Professor Keele specializes in research on applied statistics with a focus on causal inference, design-based methods, matching, natural experiments and instrumental variables. He also conducts research on topics in educational program evaluation, election administration, and health services research. He has published articles in the Journal of the American Statistical Association, Annals of Applied Statistics, Journal of the Royal Statistical Society, Series A, The American Statistician, American Political Science Review, Political Analysis, and Psychological Methods.
Ilya Shpitser
Johns Hopkins University
Ilya Shpitser is a John C. Malone Assistant Professor of Computer Science, Johns Hopkins University. His primary area of interest is causal and probabilistic inference, graphical models, missing data, dependent data, and algorithmic fairness. The primary application area for Dr. Shpitser’s work is healthcare, medicine, and public health.
Dylan Small
Dylan Small, PhD is the Class of 1965 Wharton Professor in the Department of Statistics at the Wharton School at the University of Pennsylvania. Dr. Small received his PhD in Statistics from Stanford University and his AB in Mathematics from Harvard University. Dr. Small’s research interests include causal inference and applications of statistics to health and public policy. He was the founding Editor of the journal Observational Studies and continues to serve on the editorial board of Observational Studies as well as the Journal of Casual Inference and several other statistical journals.