Jack Porter is a Professor of Economics and holds the Richard E. Stockwell Distinguished Chair at the University of Wisconsin-Madison's Department of Economics. Based in the Social Sciences Building, he actively contributes to the department's research and teaching missions with a focus on advanced econometric methodologies. His research centers on Econometrics, particularly Structural Econometrics, Microeconometrics, Treatment Effects, Instrumental Variables, and Moment Inequalities. Porter's work bridges theoretical econometric frameworks with empirical applications in microeconomic contexts, emphasizing identification strategies and methodological innovations for complex economic models. Analysis of Porter's publication record reveals consistent contributions to high-impact econometric theory, with recurring themes in moment inequality frameworks, treatment effect estimation, and instrumental variable methods. His work in Econometrica and the Journal of Econometrics demonstrates rigorous theoretical development with practical implications for empirical economic research. Scientific Awards: Richard E. Stockwell Distinguished Chair
Vasilis Syrgkanis is an Assistant Professor at the Department of Management Science and Engineering , Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. He leads the Stanford Causal AI Lab and is an Associated Director of the Stanford Causal Science Center . His research spans machine learning, causal inference, econometrics, online learning, reinforcement learning, game theory/mechanism design, and algorithm design . Education PhD in Computer Science, Cornell University (advised by Eva Tardos) Diploma in EECS, National Technical University of Athens Research Focus Develops methods for causal machine learning , including courses on Applied Causal Inference and Foundations of Causal ML. Works on treatment effect estimation , instrumental variable regression , and robust policy learning under unobserved heterogeneity. Explores intersections of game theory and machine learning , particularly in auction design and strategic exploration. Scientific Contributions Proposes neural causal partial identification and adaptive instrument design frameworks. Develops doubly robust learning and minimax IV regression algorithms with theoretical guarantees. Introduces incentive-aware synthetic control and structure-agnostic causal effect estimation methods. Awards & Recognition 2023 Bodossaki Distinguished Young Scientist Award in Applied Sciences 2022 Amazon Research Award in Machine Learning Algorithms and Theory Best Paper Awards at COLT 2019, EC 2015, NeurIPS 2015, and others PhD Advisees Ravi Sojitra (MS&E, co-advised with Guido Imbens) Hui Lan (ICME) Jikai Jin (ICME) Jiyuan Tan (MS&E, co-advised with Jose Blanchet) Keertana Veeramony Chidambaram (MS&E) Contact Email: vsyrgk@stanford.edu Office: Huang Engineering Center, Room 252, Stanford, CA 94305
Azeem M. Shaikh is the Ralph and Mary Otis Isham Professor and Chairman of the Department of Economics at the University of Chicago. He co-directs the Big Data Initiative at the Becker Friedman Institute. His research spans econometric theory, causal inference, and experimental design, with applications to early childhood education and economic mobility. B.S. in Mathematics, Duke University Ph.D. in Economics, Stanford University (2006) His research interests include: Randomization and resampling methods (bootstrap, subsampling) Multiple hypothesis testing and partial identification Design and analysis of experiments with matched pairs Evaluation of social programs like the HighScope Perry Preschool Recent publications focus on: Advances in randomization inference and stratified experiments Ranking methodologies for political parties and neighborhoods Handling imperfect compliance in experimental settings Software tools like csranks for statistical inference Scientific awards include: Dennis J. Aigner Award for Applied Econometrics Hoover National Fellowship Alfred P. Sloan Fellowship Elected Fellow of the Econometric Society (2018) Elected Fellow of the International Association for Applied Econometrics (2018) Grants from the National Science Foundation, Stanford Institute for Economic Policy Research, and the Hoover Institution supported his work. He held editorial positions at Journal of Political Economy , Econometrica , and Journal of Econometrics .
Shu Shen is an Associate Professor and Graduate Program Chair in the Department of Economics at the University of California, Davis, specializing in econometric theory and its applications in microeconometrics. His academic credentials include: Ph.D. in Economics, University of Texas at Austin, 2011 M.S. in Economics, University of Texas at Austin, 2008 B.A. in Economics, Fudan University, Shanghai, 2006 Shen's research centers on advancing econometric methodologies for causal inference, with emphasis on regression discontinuity designs, GMM identification, multiple testing frameworks, and distributional/quantile analysis. His work bridges theoretical rigor with practical applications in program evaluation and treatment effect heterogeneity. His publications reveal consistent contributions to methodological econometrics, particularly in developing robust techniques for regression discontinuity, treatment effect analysis, and multiple hypothesis testing. These works frequently employ nonparametric and semiparametric approaches to address distributional questions in empirical economics, appearing in leading journals like the Review of Economics and Statistics and Journal of Econometrics . Shen's research impact is recognized through prestigious awards: Hellman Fellow, University of California, Davis (2015) Individual Research Grant, UC Davis Institute for Social Sciences (2014) Small research grant, University of California, Davis (2012–2014, 2019) Hale Fellowship, University of Texas at Austin (2008) As Graduate Program Chair, he oversees doctoral training in Economics while securing research funding from institutional sources. His teaching portfolio spans undergraduate econometrics (ECN 102, 140) and graduate-level courses (ECN 240D, 240F), emphasizing both theoretical foundations and empirical applications.
Professor Jean-Marie Dufour holds the William Dow Chair in Economics at McGill University's Department of Economics and is a Research Professor at the Halle Institute for Economic Research (IWH). His research focuses on econometrics, macroeconomics, finance, and public finance, with a particular emphasis on dynamic models, policy analysis, and financial asset pricing. He has held prestigious roles such as Bank of Canada Research Fellow and Fellow of the Econometric Society. Education: PhD in Economics (University of Chicago, 1979), M.Sc. Mathematics (Université de Montréal, 1973) Affiliations: CIRANO, CIREQ, and invited researcher at IWH Major awards include the Killam Prize (2006), Officer of the Order of Canada (2008), and Fellowships from the Royal Society of Canada and the Econometric Society. His work bridges theoretical econometrics with applied macroeconomic and financial analysis. Editorial roles include Associate Editorships at Econometrica and Journal of Econometrics . His research outputs span structural equation modeling, causality, and volatility analysis in financial markets.
Dr. David Kang is an Associate Professor (Senior Lecturer) in the Department of Economics at Lancaster University since 2016. He holds a PhD in Economics from the University of Wisconsin-Madison, an MA in Economics from Seoul National University, and a BS in Mathematics from KAIST. His research focuses on econometric theory and applied econometrics, with expertise in nonparametric methods, model selection, and GMM models. Education: PhD in Economics, University of Wisconsin-Madison (USA) MA in Economics, Seoul National University BSc in Mathematics, KAIST (South Korea) Research Interests: Dr. Kang's work emphasizes nonparametric series estimation, tuning parameter selection, and robust econometric inference. His current projects explore misspecification-robust methods and post-model selection techniques in econometric frameworks. Teaching & Supervision: He teaches Econometrics (ECON330), Data Science in Economics (ECON337), and Microeconometrics (ECON803). He actively supervises PhD students in theoretical and applied econometrics, focusing on microeconometric applications. Grants & Activities: He led the project "Robust Econometric Inference and Economic Applications" (2019–2022). He frequently participates in international conferences and delivers invited talks at institutions like the University of Cambridge, KAIST, and Seoul National University. Labs/Teams: Affiliated with the Economics Research Group, Industrial Organisation, and Labour, Education and Health Economics teams at Lancaster University.
Dr. Maria Grith is an Assistant Professor in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. Her research focuses on econometric methodologies applied to financial markets, with particular emphasis on machine learning techniques in finance, risk management, and time series analysis. She actively contributes to academic supervision, as evidenced by her involvement in bachelor thesis projects in Econometrics and Economics. Her research interests span quantitative finance, including applications of neural networks to volatility forecasting, cryptocurrency risk analysis, and reinforcement learning in high-dimensional settings. She also explores traditional econometric challenges such as delta-hedged options pricing, sovereign default risk, and macroeconomic persistence heterogeneity. Her publication record includes over 20 peer-reviewed articles, with recent work addressing cutting-edge topics like neural tangent kernels in finance and graphical models for multivariate time series. While no scientific awards are explicitly mentioned, her prolific output reflects a strong research trajectory in financial econometrics and computational methods.
Timothy Christensen is a Professor of Economics at Yale University, previously holding a Professorship at University College London. He earned a Ph.D. in Economics from Yale University in 2014. His research focuses on theoretical and applied econometrics, financial econometrics, and statistics/data science, with recent work exploring the integration of machine learning and unstructured data into economic modeling. He has secured grants from the National Science Foundation and the European Research Council to support his research. His recent work includes advancements in optimal estimation and inference in nonparametric models using instrumental variables, with applications to firm export elasticities in trade models. Research Interests: Christensen’s interdisciplinary approach bridges econometrics and machine learning, addressing challenges in modeling unstructured data. His methods emphasize data-driven sieve dimension choices and uniform confidence bands for robust inference. This work has implications for understanding firm heterogeneity in international trade. Grants & Awards: His grants include funding from the NSF and ERC. No specific scientific awards are listed in the provided information. Labs/Teams: No specific lab or team affiliations are explicitly mentioned.
Gaurab Aryal is an Associate Professor of Economics at Boston University, with affiliations at Washington University in St. Louis, University of Virginia, and Australian National University. His research spans Industrial Organization , Econometrics , and Auction Theory , focusing on empirical frameworks for market structures and decision-making under ambiguity. PhD in Economics from Pennsylvania State University (2010) M.S. in Quantitative Economics from Indian Statistical Institute (2004) B.A. (Honors) in Economics from Sri Ram College of Commerce (2002) His research interests include: Empirical analysis of auction mechanisms and pricing strategies Identification of multidimensional screening models Collusion detection in asymmetric markets Policy implications for antitrust and procurement Recent publications highlight work on airline pricing, insurance econometrics, Cournot oligopolies, and pharmaceutical innovation valuation. Articles like "Common Subcontracting and Airline Prices" (2025) and "Econometrics of Insurance with Multidimensional Types" (2025) demonstrate methodological rigor and real-world applications. Scientific awards include grants from the Weidenaum Center (2022-2023) Bankard Fund for Political Economy (multiple grants) National Science Foundation (as Research Fellow) Advising and grants show extensive mentorship of PhD students across institutions like University of Virginia and Washington University, with 12+ advisees. He has secured over $300,000 in competitive research grants.
Han Hong is a Professor of Economics at Stanford University's Department of Economics. He holds a Ph.D. in Economics (1998), M.S. in Computer Science (1998), and M.S. in Statistics (1997) from Stanford University, and a B.A. in International Trade from Zhongshan University (1993). His research focuses on econometric methodology, health econometrics, statistical modeling, and computational economics. He has developed innovative approaches for panel data analysis, structural estimation, and decision-making algorithms. Professor Hong has received numerous honors including the Willard G. Manning Memorial Award (2017), Arrow Award Honorable Mention (2015), and multiple NSF grants. He serves as Co-Editor of the Journal of Econometrics and is a Fellow of the Econometric Society. He teaches courses in Advanced Econometrics, Data Science, and Econometric Methods, and mentors students through honors thesis research and directed reading programs.
Alexander Torgovitsky is a Professor in the Kenneth C. Griffin Department of Economics at the University of Chicago, where he has served since 2017. He holds a Ph.D. from Yale University (2012) and serves as Director of Graduate Admissions in the Economics Department. His research focuses on microeconometrics, applied econometrics, and causal inference, with a particular emphasis on instrumental variables methods and policy evaluation. Key contributions include work on nonparametric demand estimation in health insurance markets, sensitivity analysis in semiparametric models, and software development for instrumental variables analysis (e.g., the ivmte and ivcrc packages). He has published in top journals such as Econometrica, the American Economic Review, and the Journal of Econometrics. Collaborations with researchers like Magne Mogstad and Christopher R. Walters highlight his engagement with policy-relevant questions and methodological innovations in causal inference.
Xiaohong Chen is the Malcolm K. Brachman Professor of Economics at Yale University, previously holding positions at the University of Chicago, London School of Economics, and New York University. She earned her PhD in Economics from the University of California, San Diego. Her research focuses on econometrics, particularly penalized sieve estimation and nonparametric/semiparametric models, with contributions to time series analysis and causal inference. Chen has authored influential papers in top journals like Econometrica and Annals of Statistics , and her work has won several awards, including the 2017 China Economics Prize and the Econometric Theory Multa Scripsit Award (2012). She serves as editor of the Journal of Econometrics and has held editorial roles at numerous top journals. Her research interests include semiparametric models, empirical asset pricing, copula methods, and measurement error analysis. Recent work addresses scalable algorithms like stochastic GMM and neural network applications in treatment effect estimation. Chen’s contributions span theoretical econometrics and applied methods, with applications in finance, macroeconomics, and climate modeling. Awards: Fellowships from the Econometric Society, American Academy of Arts and Sciences, and multiple best paper awards. Grants/Editorial Roles: Editor of Journal of Econometrics (2019–present), associate editorships across leading journals, and grants supporting methodological innovations.
Hyunseung Kang is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, affiliated with the Department of Educational Psychology (Quantitative Methods), Department of Biostatistics and Medical Informatics (BMI), Center for Demography and Ecology (CDE), and Center for Demography of Health and Aging (CDHA). He completed his PhD in Statistics at the Wharton School (2015), with postdoctoral training at Stanford University (2015-2016). Education: PhD in Statistics, University of Pennsylvania (2010-2015) MS in Statistics, Stanford University (2009-2010) Bachelor of Science in Mathematical and Computational Science, Stanford University (2006-2010) Research: Focuses on causal inference methods using instrumental variables, econometrics, semi/nonparametric techniques, network analysis, and machine learning. Applications span genetics, epidemiology, health policy, education, and microeconomics. Recent grants include support from NSF, NIH, and UW-Madison initiatives. Teaching: Teaches advanced courses like Causal Inference (Stat 992), emphasizing theoretical foundations and practical applications. Software: Developed R packages like ivmodel for instrumental variables analysis, published in Observational Studies . Actively contributes to open-source tools for causal inference. Awards: Recognized through grants and editorial roles, including Associate Editor for Biometrics and Journal of the American Statistical Association .
Xinyi (Cindy) Zhang is a Postdoctoral Researcher in Biostatistics at Johns Hopkins University, mentored by Professors Brian Caffo and Zheyu Wang. She earned her Ph.D. in Statistics from the University of Toronto in 2023 under Professors Dehan Kong, Linbo Wang, and Stanislav Volgushev, following a Master's degree from UC Berkeley and dual Bachelor's degrees from the University of Toronto in Statistics and Mathematical Application in Economics and Finance. Ph.D. in Statistics, University of Toronto, 2018–2023 M.S. in Statistics, University of California, Berkeley, 2017–2018 B.Sc. in Mathematical Application in Economics and Finance, University of Toronto, 2014–2017 B.Sc. in Statistics, University of Toronto, 2014–2017 Her research develops statistical and machine learning methods for high-dimensional, complex data structures with applications in causal discovery, neuroimaging, and personalized healthcare. Key challenges addressed include unmeasured confounders, incomplete data, and massive-volume datasets, with recent expansion into deep learning for brain imaging in Alzheimer's disease detection. Methodological innovations focus on causal inference frameworks, latent variable modeling, and multi-view data integration. Her 13 publications (2022-2024) reveal a cohesive trajectory in biostatistical methodology, emphasizing causal inference techniques for observational studies and neuroimaging applications. Notable themes include instrumental variable methods for invalid instruments, fMRI multiple testing procedures, and Alzheimer's disease biomarker modeling using MRI and deep learning. The work bridges theoretical statistics with real-world healthcare challenges, particularly in neurodegenerative disease progression. Dr. Zhang has received significant recognition during her graduate training: Ontario Trillium Scholarship (2018–2022) SSC Annual Meeting Student Travel Grant (2022) SGS Conference Grant, University of Toronto (2020) Department Citation Award, UC Berkeley (2018) ASA Nonparametric Statistics Section Student Paper Award Finalist (2018) Dean’s List Scholar, University of Toronto (2015–2017) She has extensive teaching experience as a TA for 12+ statistics courses at the University of Toronto and UC Berkeley, covering mathematical statistics, probability, and data analysis. Her service includes journal reviewing for JASA and Scandinavian Journal of Statistics, conference session chairing at JSM and ICSA symposia, and peer review for UAI and IEEE conferences. No independent student advising or grant leadership is documented.
Ching-Yun Wang is an Affiliate Professor in Biostatistics at the University of Washington and a Professor in the Biostatistics Program at Fred Hutchinson Cancer Research Center (Fred Hutch). He holds roles in the Public Health Sciences Division and the Translational Data Science Integrated Research Center (TDS IRC). His research focuses on methodological advancements in measurement error, missing data, survival analysis, and clinical trials, with applications in nutritional epidemiology and cancer prevention. He collaborates extensively with researchers on trials involving diet, physical activity, and colorectal cancer screening strategies. Education: PhD in Statistics from Texas A&M University (1993) and MS from National Tsing-Hua University (1985). Research interests include semi-parametric models, joint modeling of survival and longitudinal data, and high-dimensional biomarker analysis. Key research contributions include developing robust statistical methods for handling covariate errors in survival models and analyzing data from large-scale clinical trials. His work has been applied in studies like the Women’s Health Initiative and colorectal cancer screening interventions. Publications highlight methodological innovations in regression calibration, longitudinal data analysis, and missing data frameworks. Collaborations span multiple disciplines, including oncology, epidemiology, and public health. His lab (C-Y Wang Group) actively engages in translational data science projects.
