Jason Kerwin is Alberta C. Corkery Professor of Economics at University of Washington, Affiliated Professor at J-PAL, and IZA Research Fellow. He earned his PhD from University of Michigan in 2015. His research examines economic decision-making in developing countries regarding health, education, employment and savings. Fieldwork includes Malawi, Uganda, India, and Egypt using randomized experiments and causal inference methods. He has published in American Economic Review, Journal of Econometrics, and Journal of Development Economics. Current projects study savings constraints, health commitment devices, and education program effectiveness. He received the Philip Leverhulme Prize, AXA Research Award, and is Brimmer Distinguished Scholar.
John Geweke is the Charles R. Nelson Endowed Professor in Economics at the University of Washington, with joint appointments at the University of Technology Sydney and emeritus status at the University of Iowa. His research focuses on econometric theory, Bayesian modeling, and applications in macroeconomics and finance. He has supervised over 30 doctoral students and received prestigious fellowships including the Alfred P. Sloan Research Fellowship. Education: Ph.D. in Economics, University of Minnesota (1975) B.S. in Social Sciences, Michigan State University (1970) His research explores econometric methods for time series analysis, Bayesian inference in complex models, and financial econometrics. Geweke's work integrates theoretical rigor with practical applications in macroeconomic forecasting and decision-making under uncertainty. Geweke's publications demonstrate consistent focus on Bayesian econometrics, time series analysis, and financial modeling, with recent emphasis on model comparison, prediction under uncertainty, and computational methods for high-dimensional problems. Awards and Honors: Fellow of the Econometric Society Fellow of the American Statistical Association Alfred P. Sloan Research Fellow (1982-1984) H. I. Romnes Faculty Fellowship He has mentored numerous PhD students and secured significant research funding from NSF and ARC for projects on Bayesian computation and economic modeling. Geweke leads methodological developments at the intersection of statistics and economics.
Gerda Claeskens is a full professor of Statistics at the Faculty of Economics and Business (FEB) at KU Leuven, Belgium. She holds positions in the Operations Research and Statistics Research Group (ORSTAT) and is affiliated with the Leuven Statistics Research Center. Her academic journey includes a Licentiate in Mathematics (Summa Cum Laude) from the University of Antwerp and a Ph.D. in Statistics from Limburgs Universitair Centrum (now Hasselt University). Research Interests: Her work focuses on model selection, post-selection inference, nonparametric methods, and high-dimensional statistics. She has contributed extensively to methodologies like focused information criteria, penalized splines, and quantile regression. Awards and Honors: Notable accolades include Fellow of the American Statistical Association (2019), Goodeve Medal (2017), and Medallion Lecturer (2016). She has also held editorial roles in top journals such as Biometrika and Journal of the American Statistical Association . Teaching and Mentoring: She has advised over 20 PhD students and mentored postdoctoral researchers. Her teaching spans advanced statistical methods, probability theory, and business statistics. She has delivered invited lectures globally, including at the European Meeting of Statisticians and the International Society for NonParametric Statistics. Key Contributions: Her book Model Selection and Model Averaging (2008) is a seminal work in statistical methodology. Her research addresses challenges in high-dimensional data, survival analysis, and model averaging, with applications in finance, insurance, and biostatistics.
Eun Yi Chung is an Associate Professor in the Department of Economics at the University of Illinois at Urbana-Champaign and serves as the DEI and Climate Officer for the department. Her research focuses on econometric methodology, particularly permutation tests and nonparametric statistical methods, with applications to treatment effects analysis and causal inference. She holds affiliations with the College of Liberal Arts & Sciences and contributes to interdisciplinary research in statistical theory and policy evaluation. Her work emphasizes rigorous statistical testing frameworks, including quantile-based approaches and methodologies addressing heterogeneous treatment effects. Recent contributions include advancements in permutation tests at nonparametric rates and robust hypothesis testing techniques. Chung’s articles appear in top-tier journals such as the Journal of the American Statistical Association , Journal of Applied Econometrics , and Journal of Econometrics . No scientific awards are explicitly mentioned in the provided materials. Her research has explored permutation tests in diverse contexts, from causal inference in social experiments to multivariate analysis and U-statistics-based frameworks. While no advising or grant details are listed, her publications highlight a sustained focus on methodological innovation in econometrics and statistics.
Anil K. Bera is a Professor at the University of Illinois at Urbana-Champaign, holding joint appointments in the Departments of Economics, Finance, Agricultural and Consumer Economics, and Statistics within the College of Liberal Arts & Sciences. His research focuses on econometrics, spatial statistics, statistical theory, and applied statistical methodologies. He has made significant contributions to the understanding of volatility models, spatial market inefficiency, and the historical development of statistical tests. His work often intersects interdisciplinary fields, such as applying spatial quantile regression in real estate economics and documenting the legacy of renowned statisticians like C.R. Rao. Bera’s recent publications emphasize methodological advancements and historical retrospectives in statistics and economics. Bera’s academic career spans over five decades, with notable collaborations and foundational research in statistical theory. While specific grants or advising details are not detailed here, his research is widely recognized in academic circles, particularly in econometrics and spatial statistics.
Joseph Antonelli is an Assistant Professor of Statistics at the University of Florida , where he has been since 2018. He holds a PhD in Biostatistics from Harvard University (2015) and completed postdoctoral training there until 2018. His research focuses on causal inference, high-dimensional modeling, Bayesian methods, spatial statistics, and applications in environmental health and criminology. Education: B.S. in Statistics, University of Florida (2011) M.S. in Biostatistics, Harvard University (2013) Ph.D. in Biostatistics, Harvard University (2015) Research Interests: Professor Antonelli develops statistical methods to address complex causal questions in health and social sciences. His work emphasizes robust approaches to confounding adjustment, spatial and environmental data analysis, and policy evaluation. He applies these methods to study air pollution effects, policing policies, and opioid policy impacts, bridging theoretical advancements with real-world applications. Key Contributions: His articles span causal inference techniques, Bayesian modeling, and spatial analysis, with recent work addressing racial bias in policing and air pollution mixtures. His research has been recognized with awards from the Health Effects Institute, JSM Biometrics, and ISBA. Awards: 2020 Health Effects Institute Young Investigator Award 2020 Journal of Speech, Language, and Hearing Research Editors Award 2014 ENAR Distinguished Student Paper Award Advising and Grants: He advises over 15 graduate students and has led grants totaling $3.5M, including NIH funding for auditory deficits in children and CDC studies on firearm policy effects. He also serves as Associate Editor for Bayesian Analysis .
In-Koo Cho is the Asa Griggs Candler Professor of Economics at Emory University. He holds a PhD from Princeton University (1986) and a BA from Seoul National University (1981). His research focuses on economic theory, game theory, and machine learning applications in strategic contexts. He has been a key organizer of the Asian School in Economic Theory, a prestigious event hosted by the Econometric Society, since 2013. His work bridges theoretical economics with practical implications in market design, learning models, and policy analysis. Cho’s academic contributions include foundational studies on competitive equilibrium, learning dynamics, and model validation. He has led international collaborations through his involvement in summer schools and conferences, fostering global exchange in economic theory. His research also addresses societal challenges, such as energy policy and market stability, reflecting a commitment to interdisciplinary impact. Key areas of his expertise include signaling games, algorithmic pricing, and the integration of machine learning into economic modeling. His career has spanned institutions like the University of Illinois and Hanyang University, though his current affiliation is with Emory University.
Zheng Fang is an Associate Professor in the Department of Economics at Emory University. His research focuses on econometrics, with expertise in statistical inference, nonparametric methods, and hypothesis testing. He holds a Ph.D. in Economics from the University of California, San Diego (2015), an MS in Statistics from the same institution (2013), and a BA and MA in Economics from Tsinghua University (2008–2010). His work emphasizes methodological advancements in econometrics, including quantile regression, shape-restricted inference, and large-scale linear system analysis. Recent contributions address topics like matrix rank testing and convex cone frameworks for hypothesis testing. Fang's research bridges theoretical developments with practical applications, often implemented in statistical software (e.g., Stata). His articles cover diverse subfields, including nonparametric estimation, bootstrapping, and concavity testing, reflecting his commitment to advancing statistical tools for economic analysis. No specific awards or grants are listed, but his CV highlights ongoing contributions to econometric theory and methodology.
Caleb Kemere is an Associate Professor in the Departments of Electrical and Computer Engineering and Bioengineering at Rice University. His work bridges neuroscience, engineering, and computer science, focusing on neuroengineering, neural decoding, and real-time brain-computer interfaces. He holds a B.S. in Electrical Engineering (with Honors) and a B.A. in Economics from the University of Maryland, College Park, and a Ph.D. in Electrical Engineering from Stanford University. Before joining Rice in 2011, he was a postdoctoral fellow at the Keck Center for Integrative Neurosciences at UCSF. His research explores hippocampal function in spatial navigation and memory, signal processing for neural interfaces, and developing technologies like miniature microscopes and low-power sensors. He has pioneered frameworks such as Spyglass for reproducible neuroscience research and RealtimeDecoder for online neural decoding. Kemere has received prestigious awards including the NSF CAREER Award (2013), HFSP Young Investigator Award (2014), and BRAIN: EAGER Award (2015). His grants include a five-year NSF grant to study Deep Brain Stimulation (DBS) and a neural engineering IGERT grant. His lab, the Realtime Neural Engineering Lab (RNEL), develops tools for understanding and modulating neural activity. Ongoing work addresses closed-loop brain stimulation, environmental uncertainty modeling in foraging behavior, and sleep-based memory consolidation.
Ashesh Rambachan serves as Assistant Professor of Economics at the Massachusetts Institute of Technology (MIT), with a visiting appointment at Stanford University's Department of Economics and SIEPR for the 2025-2026 academic year. His primary institutional affiliation remains MIT's Department of Economics. His research program integrates econometric theory with machine learning to advance causal inference methodologies, particularly in quasi-experimental designs and observational data analysis. Key focus areas include algorithmic fairness, structural model robustness, and the application of foundation models to economic problems. This interdisciplinary approach bridges theoretical econometrics with real-world policy evaluation challenges. Analysis of his publication trajectory reveals increasing engagement with AI-driven methodologies since 2020, marked by a shift toward foundation models and large language models in economic contexts. His work consistently addresses credibility gaps in causal estimation while expanding into behavioral economics and algorithmic decision-making frameworks, reflecting evolving priorities in computational social science. Scientific recognition includes: NeurIPS 2024 Spotlight Paper for "Evaluating the World Model Implicit in a Generative Model" No explicit details regarding student advising, grant funding, or laboratory affiliations appear in current public records. His teaching portfolio includes MIT PhD courses on Algorithms and Behavioral Science (14.163) and Statistical Methods in Economics (14.380), alongside undergraduate instruction in Algorithmic and Human Decision-Making (S.6041).
Changbao Wu is a Professor and Chair of the Department of Statistics and Actuarial Science at the University of Waterloo, within the Faculty of Mathematics. His research focuses on complex survey design and analysis, with expertise in empirical likelihood methods, resampling techniques, and missing data problems. He has developed R packages to implement these methods and is a Fellow of the American Statistical Association and Elected Member of the International Statistical Institute, receiving the 2012 CRM-SSC Prize in Statistics. Education: PhD in Statistics (1999) from Simon Fraser University under Prof. Randy Sitter. He has been at Waterloo since 1999. Research Interests: Design and analysis of complex surveys Semiparametric/nonparametric methods Empirical likelihood methods Resampling (jackknife/bootstrap) Missing data and measurement error Survey sampling methodology Professional Roles: Associate Editor: Survey Methodology (2006–), Biometrika (2008–), Journal of Nonparametric Statistics (2011–) Member: Statistics Canada’s Advisory Committee on Statistical Methods Recent Publications Trends: Focus on non-probability survey samples, causal inference, and pseudo-empirical likelihood methods. Recent work addresses challenges in combining non-probability and probability samples, doubly robust estimation, and calibration techniques. Awards: Fellow of ASA (since 2020) Elected ISI Member (since 2012) CRM-SSC Prize (2012) Grants and Advising: Extensive grants related to survey methodology and statistical inference. Advises on the International Tobacco Control (ITC) China Survey and collaborates on large-scale studies like the Canadian Longitudinal Study on Aging (CLSA). Labs/Teams: Leads research in survey statistics and statistical methodology at the University of Waterloo, contributing to interdisciplinary projects involving health, economics, and social sciences.
Arman Oganisian is an Assistant Professor of Biostatistics at Brown University, holding the Thomas J. & Alice M. Tisch Professorship. He earned his PhD in Biostatistics from the University of Pennsylvania (2021), and prior degrees from the same institution (MS, 2018) and Providence College (BA, 2013). His research focuses on Bayesian nonparametric methods for causal inference in sequential treatment strategies, particularly in diseases requiring long-term management like oncology and HIV. Research & Grants: His work is funded by PCORI contracts for Bayesian methods in sequential treatments (AML leukemia) and incomplete covariates. Notable grants include NIH R01 awards for HIV care cascade analysis and osteoporosis pharmacotherapy. He develops tools like the causalBETA R package for survival outcome causal inference. Awards: Salomon Faculty Research Award (2022), Saul Winegrad Dissertation Award (2021), ENAR Student Paper Award (2020). Teaching: Leads courses on Bayesian methods and statistical inference at Brown. Service: Serves on NIH review panels, PCORI, and academic committees, including Brown’s MPH admissions. Labs & Collaboration: Engages in collaborative projects on Bayesian causal modeling and nonparametric methods, with a focus on healthcare applications in oncology and rheumatology.
Christian Bontemps is Professor of Economics at ENAC & TSE, specializing in theoretical econometrics (set identification, testing) and structural micro-econometrics. He has published extensively on these topics in journals including the Journal of Econometrics and Review of Economics and Statistics. His current research develops geometric approaches to inference in entry games and analyzes airline industry mergers. He has consulted on projects with significant policy impacts, including contributions to Microsoft's Windows Error Reporting platform.
Stefan Radev is an Assistant Professor in the Cognitive Science department at Rensselaer Polytechnic Institute . His work focuses on developing Bayesian methods with generative neural networks and computational models for complex systems like cognition and disease outbreaks. He is the core developer of the BayesFlow framework, which enables amortized Bayesian inference using deep learning. Radev's research addresses computational challenges in Bayesian workflows, such as rapid parameter estimation and model validation through neural networks. His primary research interests include Deep Learning , Probabilistic Modeling , and their applications in computational neuroscience and biomedical engineering. He collaborates with the Center for Modeling, Simulation and Imaging in Medicine (CEMSIM) and contributes to open-source projects like BayesFlow, which supports multi-backend frameworks (PyTorch/TensorFlow/JAX). Recent publications highlight advancements in amortized inference, simulation-based calibration, and robust Bayesian workflows. His work spans domains from cognitive modeling to biomedical imaging, emphasizing interdisciplinary applications of Bayesian methods. No scientific awards are explicitly listed, though his contributions to open-source tools and impactful research indicate significant academic recognition.
Corine Jackman Burden is an Assistant Professor at UMBC's Department of Chemical, Biochemical and Environmental Engineering. Her research focuses on microfluidic platforms for studying host-microbe interactions and disease pathogenesis. Education includes: PhD in Chemical Engineering (University of Michigan, 2020) MS in Chemical Engineering (University of Michigan, 2019) BS in Chemical Engineering (Howard University, 2013) Research utilizes microdroplet technology to investigate cell-cell communication in women's health and respiratory diseases through single-cell analysis. Publications demonstrate strong focus on machine learning applications in cancer research and healthcare analytics.
