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.
Yajuan Si is a Researcher at the University of Michigan , affiliated with the School of Public Health and the Department of Biostatistics . Her work focuses on advancing statistical methodologies in survey inference and data analysis. Education: PhD from Duke University (2012) Email: yajuan@umich.edu Address: ISR 4014, 426 Thompson St, Ann Arbor, MI 48104 Research Interests Dr. Si specializes in Bayesian statistics , survey inference , missing data analysis , and confidentiality protection techniques . Her projects address critical challenges such as: Statistical adjustments for nonresponse bias in complex surveys Enhancing synthetic data methods for privacy-preserving analysis Population heterogeneity correction in neuroimaging studies Unifying multilevel regression and poststratification frameworks Selected Publications Her recent work spans Bayesian modeling applications in education surveys, massive data imputation , and healthcare data analysis . Key themes include survey weighting, computational scalability, and bias correction. Collaborative Efforts Dr. Si collaborates with multidisciplinary teams across public health, economics, and social sciences, applying her statistical expertise to real-world problems in COVID-19 transmission modeling and demographic research .
Rodrigo Castro Cornejo serves as Assistant Professor in the Department of Political Science within the College of Fine Arts, Humanities and Social Sciences at the University of Massachusetts-Lowell. He also holds the position of Associate Director at the university's Center for Public Opinion and acts as co-principal investigator for the Mexican Election Study (ENEM), which forms part of the Comparative Study of Electoral Systems (CSES) international project. His research focuses on the intersection of survey methodology, public opinion formation, and political behavior. Castro Cornejo's work particularly examines how partisanship, affective polarization, and motivated reasoning shape voter attitudes and electoral decisions in democratic contexts. His scholarship also investigates clientelism, vote-buying practices, and the resilience of democratic attitudes amid rising democratic backsliding. Analysis of his 15 most recent publications reveals a consistent focus on Mexican electoral politics with methodological sophistication spanning experimental designs, multilevel regression modeling, and comparative survey research. His work demonstrates interdisciplinary reach across political psychology, electoral studies, and Latin American politics, with a recurring emphasis on polarization dynamics and electoral integrity. Castro Cornejo previously held positions as Assistant Professor at CIDE in Mexico City and as a postdoctoral fellow at the University of Virginia. His academic trajectory includes significant affiliation with the University of Notre Dame, where he completed his doctoral studies as a Kellogg Institute for International Studies PhD Fellow. His advising contributions center on graduate research in political methodology and Latin American politics, while his institutional leadership includes directing survey research initiatives through the Center for Public Opinion. His laboratory work primarily involves the Mexican Election Study team, which conducts methodologically rigorous comparative electoral research across multiple election cycles.
Dr. Sumali Bajaj is a Peter J Braam Early Career Research Fellow in Global Wellbeing at Merton College, University of Oxford. Her research focuses on developing statistical and computational tools to analyze infection risks and healthcare disparities in infectious diseases. She holds a BSc from Lady Shri Ram College for Women, an MSc from Harvard University, and a DPhil in infectious disease modelling from the University of Oxford. Her work emphasizes leveraging serological data to reconstruct transmission dynamics and optimizing survey design with limited resources. Her current research collaborations span various departments at Oxford and international partners in South America and India. She is passionate about teaching the application of statistics and mathematics in disease modelling and public health. Key research areas include SARS-CoV-2 transmission analysis, healthcare inequities, and the impact of pandemic interventions on global disease patterns. Education: BSc (Statistics/Biostatistics) from Lady Shri Ram College for Women MSc (Biostatistics) from Harvard University DPhil (Infectious Disease Modelling) from University of Oxford Her recent publications highlight advancements in real-time epidemic modelling, AI applications in disease forecasting, and the sociodemographic dimensions of testing behaviors during the pandemic. Dr. Bajaj’s work bridges mathematical rigor with public health practice, addressing global challenges in infectious disease surveillance and equity.
Florian Schaffner is a postdoctoral researcher at the Department of Political Science at the University of Zurich, contributing to research and teaching in political science with expertise spanning political behavior, computational methods, and democratic processes. His academic background includes: DPhil in Politics, University of Oxford, 2024 MSc in Comparative Politics, London School of Economics and Political Science, 2018 BA in Social Sciences, University of Zurich, 2016 Dr. Schaffner's research bridges political psychology with computational methods, focusing on affective polarization, representation, and direct democracy. His work integrates quantitative text analysis and machine learning to examine contemporary political phenomena, particularly how identities form and evolve around major events like referendums. This methodological innovation distinguishes his approach within political science. His publications reveal a consistent trajectory examining democratic processes, with particular emphasis on Brexit's impact on political identities and party dynamics. The research demonstrates increasing sophistication in methodological approaches, moving from traditional political psychology to advanced computational techniques that capture nuanced voter behaviors and perceptions. As an educator, Dr. Schaffner teaches courses including Natural Language Processing for Political Science and Quantitative Text Analysis at the postgraduate level, while also supervising Master's theses and providing research seminar preparation. His teaching reflects his dual expertise in political theory and computational methodology. Dr. Schaffner has developed multiple R packages that advance political science methodology, including autoMrP for improving Multilevel Regression and Poststratification models, bodleianlibraries for library catalog access, websearchr for web operations, xaringanbeamer for academic presentations, and Xplorer for data exploration. These tools demonstrate his commitment to open science and methodological innovation in the discipline.
Michael Auslen serves as an Assistant Professor in the Department of Government at the University of Texas at Austin's College of Liberal Arts, where he teaches Media and American Democracy and Honors Tutorial courses. His research examines democratic representation mechanisms with emphasis on state/local politics, media influences, and public opinion's policy role. His educational background includes: Ph.D. in Political Science from Columbia University Master in Public Policy from Harvard Kennedy School B.A. in Journalism and Political Science from Indiana University Auslen's research spans state/local governance, media-representation dynamics, and political methodology. He investigates how local news organizations and political parties mediate public-official connections while developing advanced techniques for subnational opinion estimation. His work bridges empirical political science and journalistic practice, leveraging prior experience covering Florida state politics. Recent publications reveal three interconnected research strands: methodological innovations for subnational polling (particularly cluster-sampled data), historical analysis of partisan polarization through state party platforms, and media's watchdog role in state legislatures. His 2024-2025 work increasingly integrates multivariate modeling with granular policy analysis. His scientific recognition includes: Best Graduate Student Poster at 2022 State Politics and Policy Conference Christopher Z. Mooney Best Dissertation Prize from APSA State Politics and Policy Section Auslen advises honors theses while maintaining active research collaborations. His prior journalism career at the Tampa Bay Times and Miami Herald directly informs his academic focus on state political institutions. Although specific grant details aren't public, his methodological papers suggest support for computational social science infrastructure. No dedicated labs or research teams are documented, though his work involves large-scale text analysis of newspaper archives and state legislative data.
Justin H. Phillips is Eaton Professor of Political Science and Chair of the Department of Political Science at Columbia University. He specializes in American state and urban politics, public opinion, and sub-national policymaking, with current research examining the effects of public opinion on policy outcomes and gubernatorial power dynamics in legislative negotiations. He received his PhD from University of California, San Diego. His research focuses on American political institutions and behavior, particularly state-level politics, partisan polarization, representation gaps, and the impact of institutional reforms on policy outcomes. His methodological expertise includes multilevel regression and poststratification techniques for estimating public opinion dynamics. Phillips' recent publications examine partisan polarization in state parties, representation gaps in Congress, methodological innovations in public opinion research, and impacts of electoral reforms in California. His work demonstrates consistent focus on how institutional arrangements mediate relationships between public preferences and policy outcomes.
Andrew Gelman is the Higgins Professor of Statistics and Professor of Political Science at Columbia University, where he also serves as director of the Applied Statistics Center. His dual appointments reflect his interdisciplinary approach to research and teaching, bridging statistical methodology with political science applications. Gelman earned his Ph.D. from Harvard University in 1990. His educational background established the foundation for his influential career that combines rigorous statistical methodology with substantive social science inquiry. He has maintained a strong presence in both academic communities throughout his career, contributing to the development of statistical methods while applying them to pressing questions in political science and public policy. Gelman's research spans an exceptionally wide range of topics at the intersection of statistics and social science. His work addresses fundamental questions in voting behavior, electoral systems, and political representation, while simultaneously advancing methodological innovations in Bayesian statistics, multilevel modeling, and data visualization. He has made significant contributions to understanding why it is rational to vote, why campaign polls are variable despite predictable elections, and how redistricting affects democracy. His methodological work spans statistical inference challenges in diverse contexts from toxicology to medical imaging, from arsenic exposure in Bangladesh to radon levels in homes, and from police practices to social network analysis. His development of multilevel regression and poststratification (MRP) has become a standard technique in survey analysis and small-area estimation. Analysis of Gelman's recent publications reveals a continued focus on foundational statistical methodology with applications across multiple domains. His work demonstrates consistent attention to practical implementation challenges in Bayesian computation, causal inference, and survey methodology. A strong theme throughout his recent work is addressing the replication crisis through improved statistical practice, with particular emphasis on model checking, transparent reporting of uncertainty, and the integration of Bayesian methods with machine learning approaches. His research increasingly focuses on the practical implementation challenges of advanced statistical methods in real-world settings. Outstanding Statistical Application award from the American Statistical Association Best article published in the American Political Science Review Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of forty As director of Columbia's Applied Statistics Center, Gelman oversees a hub for interdisciplinary statistical research and collaboration. He has mentored numerous students and researchers through the Center's activities, fostering a community that applies advanced statistical methods to real-world problems across various domains. His teaching includes graduate courses in quantitative political research and applied regression methods, reflecting his commitment to training the next generation of researchers in robust statistical practice. Gelman has taught courses including Principles of Quantitative Political Research, Quantitative Political Research, and Quantitative Methods II: Applied Regression. Gelman leads research teams focused on developing and applying advanced statistical methods to social science questions. His work often involves collaboration across disciplines, bringing statistical expertise to address substantive questions in political science, public health, and social policy. The Applied Statistics Center under his direction serves as a nexus for methodological innovation and application, connecting statisticians with domain experts to tackle complex data challenges while promoting best practices in statistical analysis and communication.
Elizaveta Semenova is a Research Associate in the Department of Computer Science at the University of Oxford. Her work focuses on the intersection of artificial intelligence, epidemiology, and public health, with particular expertise in Bayesian modelling, spatial statistics, and disease surveillance systems. She develops advanced machine learning frameworks for addressing challenges in global health equity, including small area estimation, disease mapping, and pandemic response strategies. Her research spans computational methods for infectious disease modelling, AI-driven healthcare analytics, and the ethical application of foundation models to culturally diverse datasets. Notable contributions include frameworks for integrating mobile survey data with traditional epidemiological methods, and innovations in graph-based active learning for optimizing disease surveillance networks. Ms. Semenova collaborates with international groups such as the Country Data Author Group to analyze global health trends, and has pioneered techniques for quantifying health inequity impacts on disease transmission dynamics. Her methodological work includes advancements in probabilistic programming using tools like Numpyro, and scalable Bayesian inference through deep generative models.
Dr Lauren Kennedy is a Lecturer at the University of Adelaide's School of Computer and Mathematical Sciences, Department of Mathematical Sciences. Her research focuses on survey methodology, multilevel modeling, poststratification, causal inference, and Bayesian statistical techniques. She specializes in addressing challenges arising from non-representative data and improving inference in social sciences through advanced statistical methods. Her work emphasizes practical applications in public opinion analysis, epidemiological modeling, and policy evaluation. Dr Kennedy is actively involved in supervising postgraduate students in Masters and PhD programs, particularly as a co-supervisor. She maintains an office in 6.56 Ingkarni Wardli Building on North Terrace campus and can be contacted at lauren.a.kennedy@adelaide.edu.au . Key research contributions include innovations in Bayesian workflow, cross-validation methodologies, and the integration of machine learning with traditional survey techniques. Her recent publications highlight advancements in causal inference frameworks and hierarchical modeling approaches for large-scale datasets.
Dr. Anja Ernst is an Assistant Professor at the University of Groningen's Faculty of Behavioural and Social Sciences, affiliated with the Psychometrics & Statistics Department. She holds a PhD in Dynamic Clustering (2022) and focuses on statistical modeling for intensive longitudinal data, clustering methods, and software implementation. Her research includes developing latent class vector-autoregressive models and promoting open-source tools via GitHub and the Open Science Framework. Education: BSc Psychology (cum laude), Honours College Research Master in Psychometrics & Statistics (cum laude) PhD on Dynamic Clustering (supervised by Albers & Timmerman) Research Interests: Ernst's work centers on between-individual differences in longitudinal data, statistical software development, and improving statistical methodology adoption in empirical research. She emphasizes practical applications through freely available code and collaboration with researchers globally. Grants & Awards: Recipient of the NWO Research Talent grant supporting her PhD research. Active in promoting research transparency and open science practices. Teaching & Mentorship: Teaches Advanced Statistics and supervises multiple PhD students focusing on psychometrics and statistical techniques. Leads the Methodology shop supporting students and researchers in methodology design.
Joseph Ornstein is an Assistant Professor in the Department of Political Science at the University of Georgia . He holds a PhD from the University of Michigan (2018) and has previously worked at the Brookings Institution and Washington University in St. Louis. Current affiliation: School of Public and International Affairs, University of Georgia Research focus: Statistical methodology, computational social science, and urban politics Key contributions: Development of R packages ( fuzzylink , promptr ) for LLM integration in social science Research Trends in his publications include leveraging large language models for text analysis, advancing record linkage techniques, exploring urban governance dynamics, and applying agent-based simulations to public health and policy challenges. His work spans methodological innovation and empirical policy evaluation. Collaborations with scholars like Elise Blasingame and Jake Truscott highlight his work on LLM applications in political science. He actively contributes to open-access platforms and software development for social science research.
