Luca De Benedictis is a Professor of International Economics and Network Analysis at the University of Macerata's Department of Economics and Law. His research focuses on international trade empirics, including trade specialization measurement, network analysis, and causal models. He has authored numerous articles on topics like gravity models, migration impacts, and historical trade networks. His work spans journals such as the Journal of the Royal Statistical Society and Network Science . He teaches courses in International Economics and Network Analysis. His research interests include economic geography, policy evaluation, and applied econometrics. Notable projects include analyzing the Erasmus Program's inclusivity, Roman road networks' legacy, and immigration's effect on trade. De Benedictis has secured funding from EU initiatives like COSTNET and GeComplexity, focusing on network data science and economic systems. He serves on editorial boards of journals like Italian Economic Journal and Journal of Historical Network Research . His work bridges theoretical models with empirical applications in trade, migration, and policy.
Siva Balakrishnan is an Associate Professor at Carnegie Mellon University with a joint appointment in the Department of Statistics and Data Science and the Machine Learning Department . He holds an affiliation with the Dietrich College of Humanities and Social Sciences. Previously, he was a postdoctoral researcher at UC Berkeley's Department of Statistics, advised by Martin Wainwright and Bin Yu, and earned his Ph.D. in Computer Science from CMU's Language Technologies Institute under Jaime Carbonell. His research focuses on statistical machine learning, causal inference, and high-dimensional statistics, with notable contributions to domain adaptation, optimal transport, and robust statistics. Education: Ph.D. in Computer Science, Carnegie Mellon University (Language Technologies Institute) Postdoctoral Researcher, University of California, Berkeley (Department of Statistics) Research Interests: His work bridges theoretical foundations and algorithmic development, emphasizing robust statistical methods and their applications in causal inference, public policy, and machine learning. Key areas include nonparametric methods, optimization, and high-dimensional data analysis. He has pioneered techniques in domain adaptation, such as the RLSbench framework for relaxed label shift scenarios. Awards & Grants: Amazon Research Award (2021) Google Research Scholar Award (2021) NVIDIA Pioneer Award (2018) IMS Lawrence D. Brown Student Award (2020, 2022) National Science Foundation Grants (CCF-1763734, DMS-1713003, etc.) Professional Activities: He serves as an Associate Editor for JASA and on the editorial boards of Foundations and Trends in Statistics . His work has been featured in top venues like NeurIPS, ICML, and the Annals of Statistics. He currently holds a sabbatical at UC Berkeley's Department of Statistics (Spring 2025). Labs & Collaborations: He actively participates in the Statistics and Machine Learning Reading Group and the Causal Inference Working Group , fostering interdisciplinary research in CMU's vibrant academic community.
Charles E. Leonard is an Associate Professor of Epidemiology at the Perelman School of Medicine , University of Pennsylvania. He holds affiliations with multiple Penn-based institutions, including the Center for Real-World Effectiveness and Safety of Therapeutics (CREST), Institute for Translational Medicine and Therapeutics, Leonard Davis Institute, Institute on Aging, and Center of Excellence in Environmental Toxicology. Additionally, he serves as a Special Government Employee at the FDA and Honorary Lecturer at Muhimbili University of Health and Allied Sciences in Tanzania. Pharmacoepidemiology Post-market prescription drug safety Causal inference methods Real-world evidence generation Environmental health impacts on chronic disease Dr. Leonard’s research focuses on generating real-world evidence to address critical gaps in drug safety, particularly for: Population health effects of drug interactions Comparative safety of antidiabetes drugs Drug-induced sudden cardiac arrest Ambient temperature extremes and chronic disease Pharmacoepidemiology methods development His work is primarily funded by the National Institutes of Health (NIH) and has been recognized through multiple awards, including the 2024 Harold I. Feldman Distinguished Scholar Award and the 2020 Elected Fellow status at the International Society for Pharmacoepidemiology. He also contributes to curriculum development and student mentoring in Penn’s Graduate Group in Epidemiology and Biostatistics. 2024 – Harold I. Feldman Distinguished Scholar Award (Penn) 2024 – Ronald D. Mann Best Paper Award (ISPE) 2020 – Leadership Medallion (BPS) 2019 – Abraham G. Hartzema Distinguished Lecturer (University of Florida)
Richard J. Cook is a University Professor and Mathematics Faculty Research Chair in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds cross-appointments at the School of Public Health and Health Systems at the University of Waterloo and the Faculty of Health Sciences at McMaster University. Previously, he held a Tier I Canada Research Chair in Statistical Methods for Health Research from 2005 to 2019. His educational background includes: BSc in Statistics from McMaster University MMath in Mathematics from University of Waterloo PhD in Statistics from University of Waterloo Professor Cook's research focuses on developing and applying statistical methods for public health research. His primary areas of interest include the analysis of life history data, longitudinal data analysis, methods for incomplete data, clinical trial design, and multivariate analysis. His work provides critical methodological frameworks for understanding disease progression and evaluating interventions in complex health settings. He has made significant contributions to the development of multistate models for disease processes and methods for handling interval-censored data. His extensive publication record demonstrates consistent focus on methodological innovations addressing real-world health research challenges. Recent work emphasizes estimand specification in clinical trials, transportability of research findings, and causal inference methods. His research bridges theoretical statistics with practical applications in autoimmune diseases, transfusion medicine, and public health. Professor Cook has received significant professional recognition: Tier I Canada Research Chair in Statistical Methods for Health Research (2005-2019) Mathematics Faculty Research Chair at University of Waterloo His students have earned prestigious awards including multiple Pierre-Robillard Awards, ISCB Student Conference Awards, and ENAR Distinguished Student Paper Awards, with notable achievements like Dr. Shu (Joy) Jiang being named in the Forbes Top 30 Under 30 North America (2023) for Healthcare. Professor Cook has advised numerous graduate students throughout his career, with many going on to successful academic and industry positions. His research has been supported by various grants, and he collaborates extensively with researchers in rheumatology, transfusion medicine, and public health through affiliations with the Centre for Prognosis Studies in Rheumatic Diseases, the International Psoriasis and Arthritis Research Team, and the McMaster Centre for Transfusion Research. He leads a vibrant research team that includes research associates, post-doctoral fellows, and graduate students working on cutting-edge statistical methodology. His research group maintains strong connections with multiple institutions and research centers focused on health outcomes and disease progression.
Shu Yang is an Associate Professor of Statistics at North Carolina State University (NC State), specializing in causal inference, missing data analysis, and biostatistics. She holds a Ph.D. in Applied Mathematics and Statistics from Iowa State University and has held roles including Postdoctoral Fellow at Harvard University and Assistant Professor at NC State. Her research focuses on developing statistical methods for observational and clinical studies, particularly in healthcare and environmental applications. Education: Ph.D. in Applied Mathematics and Statistics from Iowa State University (2014) B.Sc. in Mathematics and Applied Mathematics from Beijing Normal University (2009) Research Interests: Dr. Yang’s work addresses challenges in causal inference, including longitudinal data analysis, missing data imputation, and high-dimensional statistics. She applies these methods to environmental health, cardiovascular diseases, HIV infection, and cancer research. Her team also explores spatial statistics and data integration techniques. Awards: 2025: Think, Collaborate & Do Ideation Award 2024: COPSS Emerging Leader Award, Cavell Brownie Mentoring Award 2022: University Faculty Scholar 2018: Ralph E. Powe Junior Faculty Enhancement Award Grants & Advising: She leads funded projects on causal inference methods in environmental health, sepsis detection, and marine protected areas. She advises over 20 Ph.D. students and postdocs, focusing on causal methods, data integration, and healthcare analytics.
Eli Ben-Michael is an Assistant Professor jointly appointed in the Heinz College of Information Systems and Public Policy and the Department of Statistics & Data Science at Carnegie Mellon University. He is affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC), contributing to cutting-edge research at the intersection of statistics, policy analysis, and artificial intelligence. His educational background includes a PhD in Statistics from U.C. Berkeley and undergraduate studies at Columbia University where he earned a dual degree in Computer Science and Statistics. Prior to his current position, he completed a postdoctoral fellowship at Harvard University's Institute for Quantitative Social Science and Department of Statistics. Ben-Michael's research focuses on developing innovative statistical and computational methods for causal inference and policy evaluation, with particular emphasis on integrating machine learning techniques to address complex problems in public policy and social science. His work bridges theoretical statistics with practical applications in healthcare, criminal justice, education, and social policy. Current research directions include safe policy learning, sensitivity analysis for clustered data, and methodological innovations for the synthetic control method. His publication record shows a strong trajectory in top-tier journals including Journal of the American Statistical Association, Journal of the Royal Statistical Society, and Proceedings of ICML. Recent work demonstrates increasing focus on policy-relevant applications including abortion legislation impacts, pre-trial risk assessment, and healthcare disparities, while maintaining methodological rigor in causal inference frameworks. Ben-Michael has developed open-source software tools including augsynth and multical R packages, which implement his methodological contributions for synthetic controls and multilevel calibration weighting. These packages have been adopted by researchers in multiple disciplines for causal inference applications.
Masoud Asgharian is a Professor in the Department of Mathematics and Statistics at McGill University. His research focuses on survival analysis, changepoint problems, nonparametric Bayesian methods, and data envelopment analysis. He has contributed to influential studies on dementia survival rates, censored data methodologies, and statistical efficiency measures. His work bridges biostatistics and operations research, with applications in public health and medical sciences. Key contributions include methodologies for prevalent cohort survival analysis, input relaxation efficiency measures in stochastic DEA, and causal inference techniques. Asgharian has collaborated extensively with researchers in epidemiology and biomedical engineering, as evidenced by his co-authored publications on topics ranging from tooth enamel properties to low-precision neural network quantization. His research has been published in high-impact journals such as New England Journal of Medicine , Journal of the American Statistical Association , and Biometrics . Current affiliations include leadership roles in statistical research at McGill, with ongoing projects in computational statistics and healthcare analytics.
Guanglei Hong is a Professor at the University of Chicago, holding tenure in the Comparative Human Development Department and the Committee on Education. She chairs the University-wide Committee on Quantitative Methods in Social, Behavioral, and Health Sciences and the Committee on Education. Her research focuses on causal inference methodologies for evaluating educational and social policies, particularly mediation and moderation effects in multi-level longitudinal studies. She developed the RMPW and MMWS methods, widely used in causal mediation analysis. Hong holds a Master’s in Applied Statistics and a Ph.D. in Education from the University of Michigan. Education: Ph.D. in Education, University of Michigan, 2004 Master's in Applied Statistics, University of Michigan, 2002 Research Interests: Hong’s work centers on causal moderation and mediation, spillover effects, and sensitivity analysis in policy evaluation. She applies these methods to assess impacts of educational programs, contextual changes, and socioeconomic factors on child/youth development. Her monograph *Causality in a Social World* (2015) is a foundational text in the field. Awards: John Simon Guggenheim Fellowship (2021–2022) William T. Grant Scholar Award (2009–2014) NAE/Spencer Postdoctoral Fellowship (2006–2007) AERA Mary Catherine Ellwein Dissertation Award (2005) Teaching & Training: Hong teaches advanced quantitative methods courses, including causal inference and mediation analysis. She leads the NSF-funded SIARM for STEM institute, training researchers in computational methods for education research. Notable courses include *Advanced Topics in Causal Inference* and *Mediation, Moderation, and Spillover Effects*. Grants & Leadership: Hong leads major grants from NSF, IES, and private foundations. Her current projects include methodological advancements for multisite trials and sensitivity analysis in mediation. She has co-authored over 60 peer-reviewed articles and edited volumes, and serves on editorial boards of leading journals. Labs/Teams: Hong directs the Quantitative Methods Group at the University of Chicago, fostering interdisciplinary collaborations in causal inference and policy evaluation. Her work integrates statistical innovation with real-world applications in education and health sciences.
Sara Chatfield is an Associate Professor of Political Science at the University of Denver's College of Arts, Humanities and Social Sciences, with additional affiliations in Gender and Women's Studies. Her work bridges American political development, gender politics, and methodological innovation in political science research. Dr. Chatfield earned her Ph.D. and MA in Political Science from the University of California, Berkeley (2014 and 2007 respectively), and her BA in Politics from Oberlin College (2006). Her research focuses on American political development with particular emphasis on gender and politics, public law, and political behavior. American Political Development Gender and Politics Women's Rights History Public Law Political Behavior Methodology and Causal Inference Her recent publications demonstrate a consistent focus on how gender shapes political institutions and processes across historical and contemporary contexts. Early work examined 19th century married women's economic rights reform, while more recent research investigates contemporary issues like bathroom access politics and gender disparities in political campaign staffing. Her scholarship often employs innovative methodological approaches to historical questions, bridging qualitative and quantitative traditions in political science. Dr. Chatfield teaches courses on American politics and law, including Constitutional Law I, Judicial Politics, and a freshman seminar on the politics of bathrooms. Her book In Her Own Name: The Politics of Women's Rights Before Suffrage (Columbia University Press, 2023) and her Cambridge Element The Politics of Bathroom Access and Exclusion in the United States (2024) represent significant contributions to understanding how gender shapes political institutions across historical periods.
Dr. Peter Guenther is a Visiting Professorial Fellow at the Institute of Management and Decision Sciences (MDS) at Hamburg University of Technology (TUHH), specializing in marketing strategy and quantitative methods. His research focuses on quantifying marketing's impact on financial performance, marketing asset accountability, business-to-business advertising, and digital marketing. He holds a PhD in Marketing from the University of Melbourne and a Diplom (German Master) in Commerce from the University of Mannheim. Key research interests include understanding marketing's financial value, improving PLS-SEM methodologies in business research, and analyzing B2B advertising dynamics. His work has been published in top-tier journals such as the Journal of Marketing , International Journal of Research in Marketing , and Industrial Marketing Management . He serves on the Editorial Review Board of Industrial Marketing Management . Dr. Guenther has prior affiliations with the University of Melbourne and RMIT (Australia). He contributes to the Liverpool Advanced Methods for Big Data Analytics (LAMBDA) Research Centre and teaches modules like 'Consumer Insight Research' and 'Marketing & Digital Analytics.'
Martin Huber is Professor of Applied Econometrics and Policy Evaluation at the University of Fribourg, Switzerland, within the Faculty of Management, Economics and Social Sciences, Department of Economics. He leads the Chair of Applied Econometrics and maintains an active research profile with numerous publications in top economics and statistics journals. His work bridges theoretical econometrics with practical policy applications across multiple domains including labor, health, and education economics. Professor Huber earned his Ph.D. in Economics and Finance in 2010 and served as Assistant Professor at the University of St. Gallen until 2014. He has conducted research stays at Harvard University (2011/2012) and the University of Sydney (2014 and 2019), establishing an international research network. His academic affiliations include the Committee for Econometrics of the Verein für Socialpolitik, Global Labor Organization, Soda Labs (Monash Business School), and Centre for European Economic Research (ZEW) Mannheim. Huber's research focuses on data-based causal analysis , machine learning applications in economics , and policy evaluation methods . He specializes in developing and applying statistical and econometric methods for measuring causal effects, with particular emphasis on semi- and nonparametric microeconometrics. His work spans labor economics (gender occupational segregation, maternal labor supply), health economics, education policy, and competition policy (bid-rigging cartels detection). His recent publications (2023-2025) demonstrate a clear trajectory toward integrating machine learning techniques with traditional econometric methods for causal inference. This includes developing frameworks for causal discovery, improving difference-in-differences methods with machine learning, and creating novel approaches for detecting collusion in markets. His 2023 book "Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R" (MIT Press) has become a key reference in the field. As an active researcher, Professor Huber directs several research projects including experimental evaluations of gender occupational segregation in the Swiss apprenticeship market. His work combines theoretical rigor with practical policy relevance, often employing experimental and quasi-experimental methods to address questions of causal mechanisms in social and economic phenomena. Through his Chair of Applied Econometrics, Huber supervises Ph.D. students and maintains an active research group focused on advancing causal inference methodologies. His work has significant implications for evidence-based policymaking across multiple sectors, particularly in evaluating the effectiveness of social programs and economic policies.
Geoffrey Wodtke is a Professor in the Department of Sociology at the University of Chicago, where he also serves as Associate Director of the Stone Center for Research on Wealth Inequality and Mobility. He holds multiple committee appointments including the Committee on Quantitative Methods in the Social, Behavioral, and Health Sciences, the Committee on Environment, Geography, and Urbanization, and the Committee on Education. Additionally, he is a Research Associate at the Population Research Center and a Faculty Affiliate with the Program in Computational Social Science. Wodtke earned his Ph.D. in Sociology from the University of Michigan in 2014, where he also completed an M.A. in Statistics in 2011. His undergraduate education began at the University of Wisconsin-Milwaukee before transferring to the University of Wisconsin-Madison, where he received his B.A. in Sociology with a concentration in analysis and research. His research program spans four interconnected areas: neighborhood effects and urban poverty, group conflict and racial attitudes, class structure and income inequality, and methods of causal inference in observational research. Wodtke's work on neighborhood effects has particularly focused on the temporal and developmental dimensions of how neighborhood poverty impacts child development, with findings suggesting more severe effects than previously documented, especially during adolescence for children from poor families. His recent publications reveal a clear trajectory from substantive neighborhood effects research toward increasingly sophisticated causal methodology development. This evolution includes integrating machine learning approaches with traditional causal inference frameworks, as seen in his forthcoming work on "Deep Learning with DAGs." His methodological contributions focus on handling treatment-induced confounding and developing regression-with-residuals approaches for causal mediation analysis. Leo Goodman Award for contributions to sociological methodology (2020) Reviewer Award, Sociology of Education (2016) Mark Chesler Award for best graduate student paper (2014) Student Paper Award honorable mention (2011) Jane Addams Award for best article (2011) Wodtke has secured significant research funding including a $300,264 NSF grant for "Why Neighborhoods Matter" (2020-2023) and an $87,819 SSHRC Canada grant for "Neighbourhoods, Schools, and Environmental Health Hazards" (2018-2022). He has advised numerous graduate students and developed specialized courses on causal mediation analysis. Wodtke co-hosts The Inequality Podcast produced by the Stone Center and has developed several software packages for causal inference including MedFlow, RcGNF, cGNF, and RWRMED.
Luke Miratrix serves as Assistant Professor at Harvard Graduate School of Education and affiliate faculty in Harvard Department of Statistics. His methodological expertise centers on causal inference applications in educational research, particularly treatment effect heterogeneity and cluster-randomized trial evaluation. His academic background includes a Doctorate in Statistics from University of California, Berkeley (2012), Master of Science in Computer Science from M.I.T., Bachelor of Science in Computer Science from California Institute of Technology, and Bachelor of Arts in Mathematics from Reed College. Prior to academia, he spent seven years as a high school teacher and tutor. Miratrix's research prioritizes minimal-assumption statistical approaches to validate data-driven arguments. Key interests include developing methods for characterizing variation in treatment impacts, analyzing post-treatment subgroups, and applying high-dimensional techniques to text summarization in legal, journalistic, and educational contexts. His work consistently bridges theoretical statistics with practical implementation challenges in real-world settings. Analysis of his recent publications (2023-2025) reveals three dominant trends: advancement of matching methodologies (e.g., synthetic controls, caliper matching), refinement of heterogeneous treatment effect estimation across multisite trials, and integration of machine learning with human coding for efficient text-based inference in educational assessments. These efforts demonstrate increasing focus on scalable, accessible tools for applied researchers. He contributes to methodological infrastructure through the CARES Lab and software packages like 'matchMulti' and 'textreg', providing practical implementation guides for complex statistical techniques. His work emphasizes translating advanced causal inference methods into usable frameworks for education researchers and policymakers.
Dylan Small is the Universal Furniture Professor and Chair of the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania. His expertise spans causal inference, observational study design, and statistical applications in public health and policy. PhD in Statistics (Stanford University, 2002) BA in Mathematics (Harvard University, 1997) His research focuses on causal inference methodology, measurement error in longitudinal studies, and health policy applications. He has advanced techniques for sensitivity analysis in observational studies and instrumental variable modeling. Recent publications analyze covariate imbalance in hormone therapy studies, zero-inflated treatment effects, and causal frameworks for global health interventions. His work bridges statistical theory with practical healthcare and policy solutions. Awards include: American Statistical Association Fellow (2013) IMS Medallion Lecturer (2022) He has served as Associate Editor for journals such as the Journal of Causal Inference and founded the journal Observational Studies. Current courses include advanced seminars on causal inference and observational study design.
Liang Zhang is a Professor of Higher Education at New York University's Steinhardt School, specializing in higher education economics, finance, and public policy. He previously taught at the University of Minnesota, Vanderbilt University, and Penn State University. His research examines the role of governments and institutions in shaping institutional performance and student outcomes, with a focus on college access, labor markets, and policy efficacy. Dr. Zhang holds dual PhDs from Cornell University (Economics) and the University of Arizona (Higher Education). His work has been published in leading journals such as Review of Higher Education , Economics of Education Review , and Harvard Education Review . Key research areas include the impact of state policies on college enrollment, peer effects in academic decisions, and the global dynamics of scientific productivity. Recent studies highlight his analysis of the Post-9/11 GI Bill’s effects on veteran education access and outcomes, as well as the stratification of faculty employment in U.S. higher education institutions. His work consistently bridges economic theory and policy practice, offering actionable insights for institutional leaders and policymakers.
