Peter Bühlmann is a Professor at ETH Zürich within the Seminar für Statistik , focusing on high-dimensional statistics, causal inference, and machine learning. His work bridges theoretical advancements with practical software implementations in R packages like pcalg , mboost , and glmmlasso , impacting fields such as genomics, proteomics, and intensive care analytics. Key Contributions : Causal structure learning, stability selection, anchor regression, and deconfounding. Software : Developed widely used R packages for statistical modeling and causal inference. Teaching : Courses on high-dimensional statistics at ETH Zürich and international institutions. Research Trends : Recent articles emphasize causal robustness, domain adaptation, and applications in medicine. His work addresses challenges in heterogeneous data, missing values, and covariate shifts using methods like spectral deconfounding and residual prediction tests. Scientific Recognition : Co-author of a paper designated as a New Hot Paper (Meinshausen and Bühlmann, 2006) by Essential Science Indicators, indicating significant impact in high-dimensional multiple testing.
Arthur Gretton is a Professor at University College London (UCL), leading the Gatsby Computational Neuroscience Unit and serving as director of the Centre for Computational Statistics and Machine Learning. He also works as a Research Scientist at Google DeepMind. His research focuses on causal inference, representation learning, and nonparametric hypothesis testing with applications in machine learning and computational neuroscience. Academic Affiliations Gatsby Computational Neuroscience Unit, UCL Centre for Computational Statistics and Machine Learning, UCL Google DeepMind Arthur's research spans several key areas in modern machine learning, including: Kernel methods for causal effect estimation and two-sample testing Deep learning architectures for proxy causal learning with complex confounding Adaptive gradient flows for generative modeling Nonparametric statistical tests with theoretical guarantees Applications in distributional reinforcement learning and Bayesian inference His work addresses both methodological advancements and practical implementations, particularly for high-dimensional data settings. Recent publications demonstrate his focus on causal inference with hidden confounders (AISTATS 2025), deep learning adaptivity in instrumental variable regression (ICLR 2025), and novel approaches to two-sample testing with adaptive kernel selection (NeurIPS 2024). He has also contributed extensively to distributional reinforcement learning and hypothesis testing frameworks. As an advisor, he supervises multiple PhD students including Jakub Wornbard, Zikai (Steve) Shen, and Zonghao (Hudson) Chen, while co-supervising others. His methodological contributions are implemented in various software packages available at the Gatsby Unit, including tools for kernel-based covariate shift correction, independence testing, and maximum mean discrepancy calculations.
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.
Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington, where he conducts research at the intersection of causal inference, statistical methodology, machine learning, and artificial intelligence. He is also a data science fellow at the eScience Institute and affiliate faculty of the Center for Statistics and the Social Sciences, demonstrating his interdisciplinary approach to causal methodology. Dr. Cinelli received his Ph.D. in Statistics from the University of California, Los Angeles, advised by Chad Hazlett and Judea Pearl, two prominent figures in causal inference. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences, with particular attention to challenges faced by social and health scientists. His work spans theoretical developments in causal identification, sensitivity analysis frameworks, and practical software implementations that enable researchers to assess the robustness of their causal conclusions. Cinelli's research program addresses fundamental questions about how unobserved confounding affects causal estimates and develops tools to quantify how sensitive findings are to potential violations of causal assumptions. His work on omitted variable bias frameworks has been particularly influential across multiple disciplines. Through his publications, Cinelli has established himself as a leading researcher in causal inference methodology, with papers appearing in top journals across statistics, machine learning, epidemiology, and social sciences. His work demonstrates both theoretical rigor and practical relevance, often accompanied by open-source software implementations that make his methods accessible to applied researchers. Best paper award at SBE 2024 in Econometrics Royalty Research Fund (RRF) Award recipient NSF/MMS research support As an advisor, Cinelli has successfully guided PhD students like Nick Irons to dissertation completion. He actively seeks new students with strong interests in causal inference. His research is supported by multiple funding sources including the National Science Foundation and the University of Washington's Royalty Research Fund. Cinelli contributes to the academic community through editorial work for the Journal of Causal Inference and by developing widely used software packages like sensemakr for sensitivity analysis.
Zhipeng Liao is a Professor of Economics at the University of California, Los Angeles (UCLA), where he contributes to the Department of Economics. He holds a Ph.D. from Yale University and specializes in econometric theory and applied econometrics. His research focuses on developing statistical methods for evaluating economic models, nonstationary time series analysis, and robust inference in semi/nonparametric frameworks. Professor Liao's work has been published in leading journals such as the Annals of Statistics , Econometrica , and the Review of Economic Studies . He serves on the editorial boards of several prestigious journals, including Econometric Reviews , Econometric Theory , and Journal of Business & Economic Statistics . His research interests span econometric theory, time series analysis, panel data modeling, and nonparametric inference, with applications to financial economics and macroeconomic modeling. His recent publications emphasize methodological advancements in hypothesis testing, model selection, and robust estimation techniques. These include contributions to the analysis of spatially dependent panel data, instrumental variables methods, and the evaluation of macro-finance models. His work bridges theoretical econometrics with practical applications, addressing challenges such as endogeneity, model misspecification, and computational efficiency. Liao’s editorial roles reflect his influence in shaping the direction of econometric research. His research has implications for policy analysis, financial market modeling, and empirical studies requiring rigorous statistical foundations. Despite the breadth of his contributions, no specific awards or grants are explicitly mentioned in the provided text.
Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.
Karim Chalak is Professor of Econometrics at the University of Manchester, specializing in econometric theory and causal inference methods. His research addresses identification challenges in models with agent interactions and measurement error. Education: PhD in Social Science from University of California, San Diego (2007) Research Interests: Develops methods for causal inference in environments with strategic interactions, omitted variables, and measurement error. Applications include returns to education, corporate finance decisions, and firearm policy impacts. Maintains international collaborations and visiting positions at leading institutions. Publications Analysis: Recent work demonstrates consistent focus on partial identification strategies, measurement error correction techniques, and applications to policy-relevant economic questions. Methodological innovations include nonparametric bounds and proxy variable frameworks.
Steven Berry is the David Swensen Professor of Economics at Yale University and the inaugural Faculty Director of the Tobin Center for Economic Policy at Yale. He specializes in empirical analysis of markets in equilibrium, with a focus on industrial organization, product differentiation, and dynamic market structures. He holds a PhD from the University of Wisconsin-Madison (1989) and a BA from Northwestern University (1980). His research explores competition policy, environmental economics, international trade, and labor market power. He has served as Economics Department Chair at Yale and Director of the Division of Social Sciences. Berry is a Research Associate at the National Bureau of Economic Research (NBER) and has advised governments on antitrust, environmental, and trade policies. He is an elected Fellow of the Econometric Society and a member of the American Academy of Arts and Sciences, having won the Frisch Medal in 2017. His work integrates micro and macro data to analyze markets like automobiles, airlines, and media, emphasizing structural econometric methods. Key Research Themes: Empirical industrial organization, demand estimation, dynamic policy analysis. Awards: Frisch Medal, Distinguished Fellow of the Industrial Organization Society. Consulting: Government and private-sector antitrust policy, environmental regulation.
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.
Daniel Wilhelm is a Professor of Statistics and Econometrics at LMU Munich, with a courtesy appointment in the Department of Economics. His research focuses on econometric theory, nonparametric methods, measurement error modeling, and statistical inference. He leads the Statistics and Econometrics Group at LMU and holds affiliations with the Centre for Microdata Methods and Practice (CeMMAP), Institute for Fiscal Studies (IFS), and the Centre for Research and Analysis of Migration (CReAM). Wilhelm’s work includes groundbreaking contributions to NPIV estimation, robust statistical testing, and the development of R and Stata packages for rank inference and econometric analysis. His recent publications address topics like rank-based inference, measurement error detection, and high-dimensional independence testing. He organizes academic events such as the Munich Econometrics Seminar and the LMU-Todai Econometrics Workshop. His research emphasizes methodological rigor and practical applications, with a focus on improving statistical techniques for social science and policy analysis.
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.
Ruli Xiao serves as Associate Professor and Director of Graduate Studies in the Department of Economics at Indiana University Bloomington's College of Arts and Sciences. Her academic office is located in Wylie Hall (Room 349), with contact details including email rulixiao@iu.edu and phone (812) 855-3213. Her academic credentials include: B.S. in Statistics from Tongji University M.A. in Economics from Shanghai University of Finance and Economics Ph.D. in Economics from Johns Hopkins University (2014) Dr. Xiao's research program emphasizes Empirical Industrial Organization and Micro-econometrics , specializing in methodological solutions for complex economic modeling scenarios. Her work develops identification frameworks for finite action games where multiple equilibria coexist with unobserved market heterogeneity, advancing estimation techniques for real-world industrial applications. Her publication profile demonstrates consistent focus on econometric theory development, particularly in dynamic modeling with unobservables as evidenced by her 2017 Journal of Econometric Methods paper. Current research trajectories indicate continued innovation in nonparametric methods for structural industrial organization models. As Director of Graduate Studies, Dr. Xiao oversees all graduate programs including M.A., M.S., and Ph.D. tracks, guiding curriculum development and student progression through rigorous economics training.
Stefan Hoderlein is a Professor in the Department of Economics at Emory University. His expertise lies in econometrics, with a focus on nonparametric methods, panel data analysis, and structural models. He holds a PhD from Bonn University and the London School of Economics (2002), and a Diplom Volkswirt from Bonn University (1997). His research interests include advanced econometric techniques such as instrumental variable estimation, demand analysis, and random coefficient models. He has contributed to methodologies addressing unobserved heterogeneity, endogeneity, and identification challenges in economic data. His work often explores applications in consumer behavior, market structure, and policy evaluation. Recent research trends in his publications emphasize nonparametric identification strategies, panel data methodologies, and the integration of big data into econometric frameworks. His technical contributions include Stata modules for statistical testing and frameworks for analyzing aggregate demand and welfare effects. While no specific awards are listed, his extensive publication record reflects sustained scholarly impact in econometric theory and applied economics. Advising details and grant information are not explicitly provided in the sources, though his work often involves collaborative research teams. His office is located in the R. Rollins Building (R428), and he maintains an active academic website.
Yong Liu is an Assistant Professor in the Department of Agricultural Economics at Texas A&M University. His research focuses on applied econometrics, Bayesian methodologies, machine learning, and agricultural policy with emphasis on crop insurance and risk management. He holds a Ph.D. in Agricultural Economics from the University of Guelph (2016), preceded by dual M.S. degrees in Economics and Agricultural Economics from Yunnan University and the University of Guelph, respectively, and a B.S. in Chemistry from Yunnan University. His research explores econometric techniques for agricultural policy evaluation, including causal inference methods and behavioral economics applications. Recent work analyzes climate change impacts on crop yields, the efficacy of insurance subsidies, and the integration of weather data into risk assessment models. Liu’s publications demonstrate a strong focus on improving crop insurance mechanisms through statistical innovations like Bayesian model averaging and spatial-temporal forecasting. His academic contributions span theoretical advancements in density estimation and practical applications in agricultural finance. While no specific awards or grants are listed, his active publication record indicates sustained research engagement in agricultural economics and quantitative methods.
