جست‌وجوی استاد و پژوهشگر

Yee Whye Tehمشاهده پروفایل

استاد

• Statistical Machine Learning
• Probabilistic Learning
• Deep Learning
• +۴ مورد دیگر

Yee Whye Teh is a Professor at the Department of Statistics, University of Oxford, and a research scientist at DeepMind. His work focuses on statistical machine learning, including probabilistic learning, Bayesian nonparametrics, deep learning, and Monte Carlo methods. He co-directs the ELLIS programme on Robust Machine Learning and has held roles such as Programme Co-chair for ICML 2017. Teh has delivered keynotes at UAI 2019, an IMS Medallion Lecture at JSM 2019, and the Breiman Lecture in 2017. His research emphasizes scalable inference algorithms, hierarchical models, and applications in genetics and natural language processing. Teh's educational background includes a PhD from the University of Toronto (2003) and a Master's from the same institution (2000). He has contributed to widely used software tools like the Sequence Memoizer and has been recognized for his work through prestigious lectureships. Research interests span Bayesian nonparametric models, MCMC methods, and their applications in genetics and data compression. His lab collaborates on projects like fragmentation-coagulation processes for genetic variation modeling and Mondrian forests for online learning. Teh advises students through Oxford's graduate programs, though he notes high demand for mentorship. His work often bridges theory and practice, addressing challenges in big data learning and small data problems.

Joshua D. Angristمشاهده پروفایل

استاد

• Econometrics
• Labor Economics
• Causal Inference
• +۳ مورد دیگر

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.

Niki Kilbertusمشاهده پروفایل

استادیار

• Machine Learning
• Causal Inference
• Ethics in AI
• +۵ مورد دیگر

Prof. Niki Kilbertus is an Assistant Professor at the Technical University of Munich (TUM) in the Department of Informatics, and Group Leader at Helmholtz AI. His research focuses on causal machine learning, ethical AI systems, and applications in healthcare, climate science, and dynamical systems. He earned his PhD from the University of Cambridge (2020) and has held positions at DeepMind, Google, and Amazon during his studies. His research interests include causal discovery, fairness in AI, counterfactual reasoning, and integrating physics-based constraints into neural networks. Key contributions include foundational work on fair machine learning (e.g., avoiding discrimination through causal models) and developing methods for causal inference in complex systems like healthcare and climate modeling. Recent work emphasizes generative models for causal interventions, robust treatment effect estimation, and physically consistent neural differential equations. He leads a large interdisciplinary group with over 20 students and postdocs working on projects funded by Helmholtz Association, ERC, and industry collaborations. Notable awards include the Leopoldina Prize for Young Scientists (2024) and membership in the Junge Akademie. His lab maintains active partnerships with ELLIS, MCML, and the Zuse Institute Berlin.

• Complex Systems
• Financial Mathematics
• Statistical Physics
• +۶ مورد دیگر

Tomaso Aste is a Professor of Complexity Science at the Department of Computer Science, University College London (UCL). He founded the Financial Computing and Analytics group and co-founded the UCL Centre for Blockchain Technologies. His work bridges complex systems, data science, and finance, with applications in blockchain, fintech, and market modeling. Education: PhD in Physics (Politecnico di Milano, 1994); Laurea in Physics (University of Genoa, 1990) Prior Appointments: Reader at University of Kent's School of Physics; Associate Professor at Australian National University's Applied Mathematics His research focuses on data-driven modeling of complex systems , particularly financial systems, complex networks, and statistical physics. He has pioneered information filtering networks and topological machine learning methods for financial applications, including portfolio optimization, risk assessment, and cryptocurrency analysis. Recent publications highlight his expertise in financial time-series analysis, blockchain technology, and AI-driven modeling. Articles explore topics like limit order books, cryptocurrency market fragility, and topological neural networks. His work has influenced regulatory technology (RegTech) frameworks and digital economy strategies. Scientific Awards : Marie Curie Individual Fellowship University of Genoa graduate study specialization Fellowship Bacheflor Boncompagni-Ludovisi Foundation Fellowship Awarded fellowships from European Commission and academic foundations support his interdisciplinary research. He has held editorial roles at journals like Philosophical Magazine and Granular Matter , and contributed to professional societies including American Physical Society and Australian Research Council panels. Teaching & Academic Leadership : Co-created four UCL Master's programs: Financial Risk Management, Computational Finance, Financial Technologies, Emerging Digital Technologies Coordinates executive training on AI, blockchain, fintech, and regtech for regulators and private firms Teaches graduate-level courses in Data-Driven Modeling, Data Science, and Advanced AI

Peter Bühlmannمشاهده پروفایل

استاد

• High-dimensional Statistics
• Causal Inference
• Machine Learning
• +۳ مورد دیگر

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.

• Economics
• Microeconomics
• Macroeconomics
• +۳ مورد دیگر

Asta Seirup Særkjær serves as an Instructor in the Department of Economics at the University of Copenhagen and participates in the Department of Economics Study Council, contributing to academic governance and curriculum oversight. Her research profile, inferred from departmental affiliation, spans core economic disciplines with emphasis on: Microeconomic theory and applications Macroeconomic policy analysis Quantitative econometric methods Development economics frameworks Labor market dynamics Comparative economic systems No scientific awards were documented in the source material. Student advising activities and research grant management details were not provided in the available text, though her Study Council role suggests involvement in academic mentorship structures.

Ariel Pakesمشاهده پروفایل

استاد

• Econometrics
• Industrial Organization
• Economics of Technological Change
• +۳ مورد دیگر

Ariel Pakes is the Thomas Professor of Economics at Harvard University, part of the Faculty of Arts and Sciences and the Department of Economics. He previously held the Charles and Dorothea Dilley Professorship at Yale University and tenured roles at institutions including the University of Wisconsin and the Hebrew University of Jerusalem. His research focuses on industrial organization (I.O.), the economics of technological change, and econometric theory. Pakes has advised notable students who now lead economics departments globally and has received prestigious awards including the Frisch Medal (1986) and election to the American Academy of Arts and Sciences (2002). Education: Pakes earned his Ph.D. in Economics from Harvard University in 1980. His academic journey includes roles as a lecturer at Harvard and positions at the University of Jerusalem. Research Interests: Pakes has pioneered methodologies for analyzing I.O. models, including demand and cost system estimation. Recent work examines AI’s impact on pricing strategies, antitrust policy implications of market dynamics, and healthcare cost structures. His theoretical contributions include simulation estimators and frameworks for dynamic oligopolies. Awards: Frisch Medal (Econometric Society, 1986) Fellow of the Econometric Society (1988) Fellow of the American Academy of Arts and Sciences (2002) Best Graduate Student Advisor Award (Yale University, 1996) Advisory and Grants: Pakes has served on NSF panels, including the Economics Advisory Panel and Presidential Fellow Advisory Board. His consulting work spans government agencies and large firms. He edited the RAND Journal of Economics and co-chaired the AEA Census Advisory Panel. Labs/Teams: Collaborates with NBER as a Research Associate and contributes to interdisciplinary projects like the PNAS issue on 'Science, Technology and the Economy.'

Nancy Margaret Reidمشاهده پروفایل

استاد

• Theoretical Statistics
• Likelihood Inference
• Design of Studies
• +۳ مورد دیگر

Nancy Margaret Reid is a University Professor of Statistical Sciences at the University of Toronto, holding the Canada Research Chair in Statistical Theory and Applications. She has served as Scientific Director of the Canadian Statistical Sciences Institute (2015–2019) and led the Department of Statistical Sciences as Chair (1997–2002). Her research focuses on theoretical statistics, particularly likelihood inference and foundational aspects of statistical methodology. Reid earned her PhD from Stanford University (1979) under Rupert G. Miller, with Brad Efron and Vernon Johns on her committee. Reid's accolades include Fellowships from the Royal Society, Royal Society of Canada, and National Academy of Sciences, as well as the Guy Medal in Gold (2022) and David R. Cox Award (2023). She has authored influential books like *Theory of the Design of Experiments* and contributed to courses on mathematical statistics and likelihood inference. Active in academic service, she teaches graduate-level courses and has advised numerous students and postdocs in theoretical and applied statistical research.

• Public Economics
• Industrial Organization
• Government Procurement
• +۳ مورد دیگر

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.

Todd Sorensenمشاهده پروفایل

عضو هیئت علمی

• Labor Market Structure
• Immigration
• Discrimination

Todd Sorensen is an Associate Teaching Professor at the University of California, Merced (UC Merced), affiliated with the School of Social Sciences, Humanities & Arts and the Economics and Business Management department. He holds fellowships at IZA (Institute of Labor Economics) and GLO (Global Labor Organization). Prior to UC Merced, he served as an Associate Professor at the University of Nevada, Reno (2014–2023) and an Assistant Professor at UC Riverside (2007–2014). His professional journey includes a visiting role as Visiting Associate Professor at UC Merced before his current appointment in 2022. Education: PhD in Economics, University of Arizona, 2007 Research Interests: Todd's work focuses on labor market structures , exploring topics like monopsony power and wage-setting dynamics; immigration , including its demographic and policy implications; and discrimination , particularly in criminal sentencing and labor markets. His research has been published in journals like ILR Review , Labour Economics , and the Journal of Population Economics , with findings cited in the U.S. Economic Report of the President. Scientific Awards: Fellow at IZA (2022–present) Fellow at GLO (2022–present) Advising & Grants: Todd has advised students across various levels but no specific advisees are listed. His grants and funding sources are not detailed in the provided text. He collaborates with interdisciplinary teams and maintains affiliations across UC campuses through his and his spouse’s academic careers. Labs/Teams: Engaged with research networks at IZA and GLO, focusing on labor market policies and migration studies. Active in collaborative projects analyzing immigration trends, labor market competition, and discrimination metrics.

Martin T. Wellsمشاهده پروفایل

استاد

• Applied Statistics
• Theoretical Statistics
• Bayesian Statistics
• +۷ مورد دیگر

Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.

Sendhil Mullainathanمشاهده پروفایل

استاد

• Behavioral Science
• Economics
• Machine Learning
• +۷ مورد دیگر

Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at the University of Chicago Booth School of Business and a Professor of Economics and the Peter de Florez Professor of EECS at the Massachusetts Institute of Technology . His work bridges machine learning , behavioral science , and computational medicine , focusing on social problems like discrimination , poverty , and health equity . Research Interests : Behavioral economics, algorithmic fairness, poverty, AI in healthcare, and policy evaluation. Teaching : Courses on Artificial Intelligence and Algorithmic Solutions to Human Problems. Publications : Over 150 papers in journals like Science , Quarterly Journal of Economics , and Nature Medicine , with recent work on AI-driven healthcare disparities and behavioral economics. Scientific Awards : MacArthur ‘Genius’ Grant, Infosys Prize, ‘Top 100 Thinker’ (Foreign Policy Magazine), ‘Young Global Leader’ (World Economic Forum). Organizations : Co-founder of ideas42 (behavioral science non-profit), J-PAL (randomized trials in development), and Dandelion Health (healthcare data for AI). Serves on the MacArthur Foundation board.

• Behavioral Economics
• Decision Theory
• Game Theory
• +۸ مورد دیگر

Jose Apesteguia is an ICREA Research Professor at the Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain. His research focuses on behavioral economics, decision theory, experimental economics, and game theory, with particular emphasis on topics such as behavioral heterogeneity, rationality measures, stochastic choice models, and social preferences. Apesteguia has collaborated extensively with scholars like Miguel A. Ballester and Jörg Oechssler, producing influential work on topics ranging from imitation dynamics to the impact of language on moral decisions. His research integrates theoretical frameworks with experimental methods, exploring how individuals make decisions under uncertainty, time preferences, and social contexts. Key contributions include the development of measures of rationality and welfare, as well as analyses of behavioral adaptation and the role of information in competitive environments. His work frequently bridges economic theory with empirical validation, addressing real-world issues such as rule compliance in public institutions and team performance in organizations. Apesteguia’s publications span top journals including the American Economic Journal: Microeconomics , Journal of Economic Theory , and Econometrica . He teaches advanced courses on bounded rationality and behavioral decision theory at the undergraduate and graduate levels. His research has been applied to fields like finance (e.g., copy trading behavior) and public policy (e.g., promoting compliance in libraries).

• Causal Inference
• Representation Learning
• Generative Models
• +۵ مورد دیگر

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.

• International Economics
• Network Analysis
• Trade Policy
• +۵ مورد دیگر

Luca De Benedictis is a Professor of International Economics and Network Analysis at the University of Macerata's Department of Economics and Law. His research focuses on international trade empirics, including trade specialization measurement, network analysis, and causal models. He has authored numerous articles on topics like gravity models, migration impacts, and historical trade networks. His work spans journals such as the Journal of the Royal Statistical Society and Network Science . He teaches courses in International Economics and Network Analysis. His research interests include economic geography, policy evaluation, and applied econometrics. Notable projects include analyzing the Erasmus Program's inclusivity, Roman road networks' legacy, and immigration's effect on trade. De Benedictis has secured funding from EU initiatives like COSTNET and GeComplexity, focusing on network data science and economic systems. He serves on editorial boards of journals like Italian Economic Journal and Journal of Historical Network Research . His work bridges theoretical models with empirical applications in trade, migration, and policy.