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Joshua D. Angrist is the Ford Professor of Economics at the Massachusetts Institute of Technology, where he has been a faculty member since 1996. He is also a co-founder and director of MIT's Blueprint Labs and a Research Associate at the National Bureau of Economic Research. Angrist shares the 2021 Nobel Prize in Economic Sciences with David Card and Guido Imbens for their methodological contributions to the analysis of causal relationships. Angrist received his B.A. from Oberlin College in 1982 and completed his Ph.D. in Economics at Princeton University in 1989. Prior to joining MIT, he taught at Harvard University and the Hebrew University of Jerusalem. His academic journey began somewhat unconventionally, as he left high school early after 11th grade, worked for over a year, and only later discovered his passion for economics through an inspiring teacher at Oberlin. Angrist's research focuses on developing and applying innovative econometric methods to answer important economic questions using natural experiments. His work spans labor economics, education economics, and causal inference methodology. He is particularly known for his contributions to instrumental variables methods and the Local Average Treatment Effect (LATE) framework developed with Guido Imbens. His research explores the economics of education and school reform, the impact of social programs on labor markets, and the effects of immigration and regulation. His recent publications reveal a continued focus on causal inference methods applied to education policy questions, labor market issues, and health economics. The trend shows increasing sophistication in research design, with particular attention to addressing selection bias and developing methods for external validity. His work spans theoretical econometric contributions alongside empirical applications in education, labor markets, and health. Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (2021) Fama Prize for Graduate Education (2018) Fellow of the American Academy of Arts and Sciences Fellow of the Econometric Society Angrist is deeply committed to teaching and mentoring. He has developed influential econometrics textbooks including 'Mostly Harmless Econometrics' and 'Mastering Metrics' with Jörn-Steffen Pischke. At MIT, he teaches courses including Labor Economics I (14.661), Econometric Data Science (14.32), and Labor Economics and Public Policy (14.64). He emphasizes selecting UROP students who have mastered foundational economics through courses like 14.64 and 14.32. Beyond MIT, Angrist co-founded Avela, a software startup using cutting-edge research to help schools improve enrollment and operations. Angrist co-founded and directs MIT's Blueprint Labs, which brings together researchers from economics, computer science, and education to develop innovative solutions for educational challenges. Through Blueprint Labs and Avela, his work bridges academic research with practical applications in education policy and technology.

• Econometrics
• Labor Economics
• Causal Inference
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Rodrigo Carril is an Assistant Professor at the Department of Economics and Business at Universitat Pompeu Fabra (UPF) and an Affiliated Professor at the Barcelona School of Economics (BSE). His research focuses on Public Economics and Industrial Organization, particularly examining public procurement policies and their economic impacts. He holds a PhD in Economics from Stanford University (2020) and is a Juan de la Cierva Researcher. Education: PhD in Economics, Stanford University (2020). His work explores topics such as pharmaceutical market dynamics, defense contracting efficiency, and regulatory frameworks for public procurement. He has received prestigious awards including the Claire and Ralph Landau Prize (2020) and the Young Economists' Essay Award (2022). Key research trends include analyzing procurement policies' effects on competition, evaluating preference programs for disadvantaged groups, and methodological contributions to econometric techniques like regression discontinuity designs. Awards: Claire and Ralph Landau Prize 2020 Young Economists' Essay Award 2022 Advising and Grants: While specific grants aren’t listed, his collaborative work involves co-authors like Claudia Allende, Mark Duggan, and Andres Gonzalez-Lira, indicating active academic partnerships. He is affiliated with the BSE and contributes to policy-oriented research initiatives. Labs/Teams: Engaged in interdisciplinary projects at UPF and BSE, focusing on public sector efficiency and regulatory economics.

• Public Economics
• Industrial Organization
• Government Procurement
• +3 more

Swantje Falcke is an Assistant Professor in Economic and Social History at Utrecht University's Department of History and Art History. As part of the College of Humanities, her research focuses on migrant integration, naturalization, and the interplay between environmental change and migration patterns. Areas of Expertise: Migration and Societal Change, Pathways to Sustainability, In-Equality Studies Academic Background: PhD in Economics (2017) at Utrecht University, Post-Doc in Political Science at Maastricht University Her interdisciplinary work examines: Education-labor market mismatches among Dutch migrants Impact of German citizenship law reforms on naturalization Long-term effectiveness of refugee integration policies Environmental change effects on migration dynamics Scientific recognition includes: Duncan Black Award (2014) for political economy research NWO XS grant (2024) for environmental migration studies Her publications demonstrate methodological rigor, combining longitudinal analysis, policy evaluation, and empirical economic testing.

• Migration Studies
• Economic and Social History
• Citizenship Policy
• +2 more

Michal Kolesár is a Professor in the Department of Economics at Princeton University , holding this position since July 2020. Previously, he served as Assistant Professor (2014-2020) with dual appointments in Economics and the Woodrow Wilson School (2018-2020), and as Visiting Assistant Professor at MIT (2016-2017). His research focuses on econometrics , particularly causal inference , instrumental variables , nonparametric regression , and robust statistical methods . His work addresses fundamental challenges in high-dimensional data analysis, treatment effect heterogeneity, and finite-sample inference validity. Current projects include developing bias-aware methods for regularized regression and analyzing dynamic causal effects in nonlinear systems. Kolesár's recent publications reveal a strong emphasis on methodological rigor with practical applications. His work spans instrumental variable techniques (addressing contamination bias, weak identification), regression discontinuity designs (discrete running variables, measurement error), and high-dimensional inference (sparsity fragility, honest confidence intervals). Key recurring themes include finite-sample optimality, coverage probability guarantees, and robustness to model misspecification. Fellow of the International Association for Applied Econometrics (2023) Journal of Econometrics best associate editor award (2023) Sloan Research Fellowship (2019) NSF grants for high-dimensional data inference (2021-2025) and nonparametric regression (2016-2019) Graduate Economics Club teaching awards (2017, 2018) As an educator, Kolesár teaches advanced econometrics courses at both undergraduate (ECO 312, ECO 313) and graduate levels (ECO 517, ECO 519, ECO 539b). He serves as Co-editor of the Journal of Business & Economic Statistics (2024-2027) and sits on editorial boards of Econometrica , American Economic Journal: Applied Economics , and others. His professional activities include extensive peer review for top journals and organization of major econometrics conferences including the 2026 Econometric Society Winter Meeting.

• Econometrics
• Causal Inference
• Instrumental Variables
• +4 more

Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.

• Probabilistic Machine Learning
• Deep Generative Models
• Artificial Intelligence
• +4 more

Daniel Kreisman is an Associate Professor of Economics at Georgia State University and a faculty affiliate at the University of Milan. His research focuses on labor economics, education finance, and policy, particularly examining school funding, career and technical education (CTE), and student loan repayment systems. Kreisman founded the Career & Technical Education Policy Exchange (CTEx), a multi-state consortium under Georgia Policy Labs, which analyzes CTE policy impacts. He holds a Ph.D. in Public Policy from the University of Chicago and a B.A. in History and Philosophy from Tulane University. Prior to academia, he taught high school English in New Orleans. His education includes a PhD from the University of Chicago (Public Policy) and a BA from Tulane University (History and Philosophy). Research Interests: Educational Finance and School Funding Mechanisms CTE Program Design and Equity Student Loan Repayment Behaviors Labor Market Signaling and Economic Outcomes Public Policy Evaluation CTEx collaborations include state partners in Massachusetts, Michigan, Montana, Tennessee, Texas, Washington, and Atlanta, focusing on data-driven policy to enhance CTE programs. Advising & Grants: Kreisman’s work bridges academia and policy, with grants supporting research on CTE alignment, financial aid impacts, and loan repayment systems. His lab affiliations enable applied policy analysis. Labs/Teams: Director of CTEx and active member of Georgia Policy Labs, emphasizing evidence-based education policy.

• Education Policy
• Labor Economics
• Education Finance
• +3 more

Harold D. Chiang is an Assistant Professor in the Department of Economics at the University of Wisconsin-Madison. His research focuses on econometric theory and methods, particularly robust inference techniques for clustered and network data, machine learning applications, and causal inference frameworks like regression discontinuity/kink designs. He employs computational statistics and asymptotic theory to address methodological challenges in high-dimensional and complex datasets.

• Econometric Theory
• Machine Learning/Nonparametrics
• Robust Inference for Clustered & Network Data
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Lu Yi is a Professor at the Department of Economics, Tsinghua University School of Economics and Management , holding a CITIC Chair Professor since 2022. He is also Director of multiple research centers , including the Tsinghua University China Economic and Social Data Research Center and the Center for International Economics. A doctoral supervisor , he has guided students across institutions and academic programs. Lu Yi's education includes a BA from Fudan University's School of Life Sciences (1999) , an MA from Fudan University's School of Management (2003) , and a PhD from the University of Hong Kong's Faculty of Business and Economics (2007) . His research focuses on Chinese economy, public finance, and global economic governance , with particular emphasis on trade liberalization, institutional quality, and environmental policy. Key research areas : Trade policy impacts, productivity gains from trade, environmental regulation effects, and labor market responses to technological change Leadership roles : Editorial board member of American Economic Journal: Economic Policy , Co-Editor of China & World Economy , former Deputy Editor-in-Chief of Economic Journal Policy advisory : Expert for National Healthcare Security Administration's DIP payment system, National Council Member for Social and Economic Council His scientific awards span national and provincial levels , including the First Sun Shangqing Young Scholar Award , Pushan World Economics Outstanding Paper Youth Award , and multiple provincial social science achievement awards in Shandong, Liaoning, Zhejiang, Fujian, and Sichuan. His publications in top journals like the Journal of Public Economics , Review of Economics and Statistics , and Nature Climate Change demonstrate his influence in applied economics and policy research.

• Chinese Economy
• Public Finance
• Global Economic Governance
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Christopher Walters is a Professor at the Kenneth C. Griffin Department of Economics, University of Chicago, and previously served as an Assistant Professor at UC Berkeley (2013-2025). He is a Research Associate at the National Bureau of Economic Research, Research Fellow at IZA, and Faculty Affiliate at MIT's School Effectiveness and Inequality Initiative (SEII). PhD in Economics, MIT (2013) B.A. in Economics and Philosophy, University of Virginia (2008) Walters specializes in Labor Economics and the Economics of Education , focusing on school choice, early childhood interventions, and program evaluation. His work combines applied econometric methods with discrete choice modeling to analyze educational investments and labor market outcomes. Walters' recent publications examine class size effects, teacher quality impacts, and school finance policies, reflecting his interest in improving educational equity through rigorous empirical analysis. Research Fellow at IZA He collaborates with institutions like J-PAL North America and MIT Blueprint Labs, contributing to evidence-based policy design in education and labor economics.

• Labor Economics
• Economics of Education
• Applied Econometrics
• +5 more

Akihiko Nishimura is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He holds a PhD from Duke University (2017) and MS and BS degrees from Stanford University (2011 and 2010). His research focuses on Bayesian methods, statistical computing, and public health data science, with applications in precision medicine and observational health data analytics. PhD, Duke University, 2017 MS, Stanford University, 2011 BS, Stanford University, 2010 Nishimura's research centers on developing advanced statistical and computational methodologies for real-world health data. His work emphasizes Bayesian inference, large-scale computing, and software development for reproducible research. He is particularly interested in using observational health data to improve clinical decision-making and advance precision medicine. He co-leads the Bayesian Learning and Spatio-Temporal modeling group (BLAST Group) and the inHealth/OHDSI Lab , collaborating with clinicians and data scientists across institutions. His recent publications reflect a strong trend in methodological innovation in Monte Carlo methods (e.g., Hamiltonian and Zigzag samplers), scalable Bayesian inference, and applications in pharmacovigilance, diabetes management, and infectious disease modeling. The articles span disciplines including biostatistics, computational statistics, public health, and bioinformatics, demonstrating a consistent focus on high-impact, computationally intensive problems in health data science. Nishimura actively contributes to the scientific community through methodological development and open science. He develops statistical software and shares teaching materials on GitHub, emphasizing reproducibility and performant computing. His involvement in the OHDSI community enables large-scale, multi-institutional studies that would not be feasible with single-source data. His work has been recognized through publications in top-tier journals such as the Journal of the American Statistical Association , Biometrika , and JAMA Ophthalmology , and has been picked up by numerous news outlets and social media platforms, indicating broad scientific and public impact. Nishimura teaches courses on performant statistical computing and advanced Monte Carlo methods, training the next generation of data scientists in efficient algorithm and software design. He mentors students and collaborators in statistical methodology and software development, fostering a culture of rigorous, reproducible, and impactful research.

• Bayesian Statistics
• Statistical Computing
• Public Health Data Science
• +9 more

Dominik Hangartner is a Professor of Political Analysis at ETH Zurich and co-director of the Stanford-Zurich Immigration Policy Lab. His research combines fieldwork and statistical analysis to evaluate migration policies and political institutions. Education: Doctorate in Social Sciences, University of Bern (2011) Research Interests focus on: Immigrant integration Effects of direct democracy Discrimination monitoring Refugee policy evaluation Political economy of migration Publications span Science , Nature , and American Political Science Review , with recent work analyzing refugee return dynamics, welfare migration, and media effects during crises like the Syrian refugee influx and the pandemic. Scientific Awards: Philip Leverhulme Prize National Latsis Prize ERC Starting Grant Grants & Projects include EU and Swiss National Science Foundation funding for immigration policy experiments. He collaborates with the Immigration Policy Lab on algorithmic integration strategies and field experiments.

• Migration Policy
• Political Institutions
• Public Policy
• +3 more

Rocío Titiunik is a Professor of Politics at Princeton University and Director of the Data-Driven Social Science Initiative. She holds affiliations with the School of Public and International Affairs, 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. Her work bridges quantitative methodology, political economy, and statistical analysis, focusing on causal inference and program evaluation through regression discontinuity (RD) designs. She earned her undergraduate degree at the Universidad de Buenos Aires and a Ph.D. in Agricultural and Resource Economics from UC-Berkeley (2009). Before joining Princeton, she served as faculty at the University of Michigan’s Department of Political Science, where she was affiliated with the Center for Political Studies and the Michigan Institute for Data Science. Rocío’s research emphasizes the application of quasi-experimental methods to study political institutions, democratic accountability, and party systems in developing democracies. Her methodological contributions include advancements in RD designs, synthetic controls, and uncertainty quantification. Recent substantive work investigates charismatic leaders’ impact on democratic stability and the effects of voter registration reforms on public safety. Her scholarly achievements include the 2016 Emerging Scholar Award from the Society for Political Methodology and 2020 fellowship in the same society. She currently serves as an associate editor for Science Advances and a Board member for Science, while previously holding roles at the American Journal of Political Science and the NSF’s Social, Behavioral, and Economic Sciences Directorate. Rocío’s advising and grant activities include co-leading the EITM Summer Institute and securing federal research funding for projects on political methodology and democracy. She teaches advanced quantitative analysis courses and collaborates across disciplines to strengthen empirical research practices. Her work is anchored in collaborative initiatives like the Center for Statistics and Machine Learning, which integrates computational tools with social science inquiry, and the Program in Latin American Studies, reflecting her commitment to regional and methodological innovation.

• Political Science
• Political Economy
• Quantitative Methodology
• +7 more

Anant Nyshadham is an Associate Professor of Business Economics and Public Policy at the Ross School of Business, University of Michigan. He holds positions as a Research Associate at the National Bureau of Economic Research (NBER), Affiliate of BREAD, Research Affiliate of the International Growth Centre (IGC), and J-PAL affiliated professor. He also serves as co-founder and Chief Strategy Officer of the Good Business Lab, a nonprofit promoting worker welfare initiatives. Education: BSc in Economics from the University of Pennsylvania (2006), PhD in Economics from Yale University (2012). Research focuses on enterprise dynamics, labor markets, managerial quality, and health outcomes in developing economies. Key themes include worker training, labor contracting, and the impact of environmental factors on productivity. His fieldwork spans countries like India, Kenya, Colombia, and Mexico. Recent publications analyze topics such as relational contracts in firms, health costs of energy policies, and the long-term effects of early-life interventions. He has received grants and awards for projects addressing organizational behavior and worker welfare. Labs/Initiatives: Leads research collaborations through the Good Business Lab and Ross School of Business, focusing on scalable solutions for improving labor practices and worker well-being.

• Development Economics
• Organizational Behavior
• Health Economics
• +4 more

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.

• Causal Inference
• Treatment Effect Heterogeneity
• Cluster Randomized Trials
• +4 more

Ivan Canay is a Professor of Economics and Director of the Mathematical Methods in the Social Sciences Program at Northwestern University’s Weinberg College of Arts & Sciences. He holds a PhD from the University of Wisconsin, Madison (2008). His research focuses on econometric theory, particularly developing statistical methods for assessing partially identified models, including tests for moment inequalities and randomization-based inference techniques. Recent work addresses challenges in clustered data analysis, covariate-adaptive randomization, and regression discontinuity designs. Canay’s academic contributions include advancing methodologies for handling non-ignorable cluster sizes and improving the robustness of inference in settings with limited data. He serves as an associate editor for the Journal of Econometrics , Journal of Business and Economic Statistics , and Econometrics Journal . His work bridges theoretical econometrics with practical applications in policy evaluation and causal inference. Key research themes include: Partially identified models and moment inequality frameworks Bootstrap methods for clustered data Covariate-adaptive randomization in clinical trials Statistical software development (e.g., Stata modules) His publications emphasize methodological rigor while addressing real-world complexities in economic data. Current projects likely expand his work on inference under structural constraints and improving accessibility of econometric tools for applied researchers.

• Econometric Theory
• Partially Identified Models
• Moment Inequalities
• +5 more