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Ariel PakesView profile

Professor

• Econometrics
• Industrial Organization
• Economics of Technological Change
• +3 more

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.'

Erniel Bayhon BarriosView profile

Professor

• Computational Statistics
• Nonparametric Methods
• Data Science/Big Data Analytics
• +5 more

Erniel Bayhon Barrios is a Professor at the Malaysia School of Business, Monash University. Formerly a professor at the University of the Philippines Diliman and a visiting scholar at Karlstad University (Sweden) and the Asian Development Bank Institute (Japan). He holds a PhD in Statistics (1990) from the University of the Philippines Diliman. His research focuses on computational statistics, nonparametric methods, data science, and computational econometrics, with applications in spatiotemporal modeling, time series, and financial markets. He has contributed to high-dimensional data analysis, volatility modeling, and robust statistical techniques. Key projects include the National Mental Health Survey and Well-Being (2019–2021) and building a data-driven organization for the Habib Group (2025–2026). He is an elected member of the International Statistical Institute (2012), associate editor of Communications in Statistical Applications and Methods , and served on the board of the International Association of Statistical Computing (2022–2025). He advises PhD students on topics like stochastic frontier models, data assignment in big data, and high-frequency time series analysis. His work aligns with UN Sustainable Development Goals related to education and economic growth.

Thomas HartmanView profile

Professor

• Quantum Gravity
• Quantum Field Theory
• String Theory
• +4 more

Thomas Hartman is a Professor of Physics in the College of Arts and Sciences at Cornell University. He received his A.B. in Physics from Princeton University in 2004 and his Ph.D. in Physics from Harvard University in 2010. His professional journey includes being a Member of the School of Natural Sciences at the Institute for Advanced Study (2010-2013), Research Associate at the Kavli Institute for Theoretical Physics, UCSB (2013-2014), Assistant Professor at Cornell University (2014-2020), Associate Professor at Cornell University (2020-2022), and Professor at Cornell University (2022-present). Hartman's research focuses on theoretical aspects of quantum gravity and quantum field theory, with particular emphasis on black hole information and strongly interacting quantum fields. His work explores four major interconnected areas: gauge/gravity duality (examining how quantum field theory degrees of freedom organize into fluctuating spacetime), black hole information paradox (investigating the relationship between classical black hole solutions and quantum statistical systems), new approaches to quantum field theory using dualities and entanglement dynamics, and the physics of de Sitter space with implications for early universe cosmology. His research employs techniques from string theory, holographic duality, general relativity, and quantum information theory. Analysis of Hartman's publication record reveals a strong focus on resolving fundamental questions in quantum gravity, particularly through the development of replica wormhole techniques that address the black hole information paradox. His work spans both highly mathematical approaches to quantum gravity and connections to potentially observable phenomena, with increasing emphasis on connections between quantum information science and gravitational physics in recent years. Member, School of Natural Sciences, Institute for Advanced Study, 2010-2013 Hartman has advised graduate students including Jeevan Chandra Namburi and Wan Zhen Chua, contributing to the next generation of theoretical physicists. His research group actively investigates the emergence of spacetime from quantum information principles and develops new mathematical frameworks for understanding quantum gravity. The group maintains strong connections with other leading institutions through collaborative projects and participates in major theoretical physics initiatives including Snowmass planning for future research directions in high energy physics. Hartman's research program represents a vital bridge between abstract theoretical concepts in quantum gravity and potential experimental tests, working to develop frameworks that could ultimately connect quantum gravity to observable phenomena in both high-energy physics and cosmological observations.

David KutasovView profile

Professor

• String Theory
• Quantum Field Theory
• Supersymmetry Breaking
• +6 more

David Kutasov is a Professor in the Department of Physics at the University of Chicago, affiliated with the Enrico Fermi Institute. His research focuses on string theory and quantum field theory, particularly addressing dynamics of strongly coupled systems, supersymmetry breaking, black hole physics, and cosmological singularities. Kutasov has contributed to understanding the interplay between string theory and field theory, including mechanisms for vacuum selection in early universe scenarios and brane dynamics. His work explores theoretical frameworks such as holography, time-dependent backgrounds, and tachyon condensation, with applications to particle physics and cosmology. Key research directions include analyzing string theory's predictions for nature and applying string-based insights to experimental particle physics and cosmic phenomena. Notable contributions span topics like D-brane interactions, non-supersymmetric vacua, and dualities in Chern-Simons theories. Kutasov's publications often bridge abstract string theory constructs with observable phenomena, emphasizing tools for analyzing string theory's implications in diverse physical contexts. Despite extensive contributions, no specific scientific awards are explicitly listed in the provided materials. His research remains active across multiple frontiers of theoretical physics, maintaining a strong focus on foundational questions in high-energy physics.

Ruihong HuangView profile

Associate Professor

• Natural Language Processing (NLP)
• Information Extraction
• Machine Learning
• +6 more

Ruihong Huang is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University, part of the College of Engineering. She holds a Ph.D. from the University of Utah (2014) and completed a postdoctoral fellowship at Stanford University. Her research focuses on Natural Language Processing (NLP) with emphasis on information extraction, event recognition, media bias analysis, and ethical AI applications. She teaches courses like Information Storage and Retrieval (CSCE 470), Natural Language Processing (CSCE 638), and has advised numerous PhD, Master’s, and undergraduate students. Key research contributions include work on event coreference resolution, discourse analysis, and detecting media bias through event relation graphs. Huang has developed benchmark datasets like UAL-Bench and EMONA, and her work spans applications in disaster management, fake news detection, and moral reasoning in LLMs. She is a recipient of the NSF CAREER Award and serves on program committees for top conferences like ACL and EMNLP. Her academic service includes roles as Area Chair for ACL 2024 and Senior Area Chair for EMNLP 2024. She maintains an active lab group focusing on NLP fundamentals and real-world applications, with projects involving propaganda identification, polarity calibration for opinion summarization, and multimodal dialog act classification. Huang has published over 80 papers in top-tier venues such as NAACL, EMNLP, ACL, and NeurIPS.

John DuchiView profile

Associate Professor

• Statistical Learning
• Optimization
• Privacy in Machine Learning
• +3 more

John Duchi is an Associate Professor at Stanford University, holding positions in the Department of Statistics and the Department of Electrical Engineering (with a courtesy appointment in Computer Science). He is affiliated with the School of Engineering. His research focuses on statistical learning, optimization, information theory, and computation, with an emphasis on balancing computational efficiency, privacy, and robustness. Key interests include developing algorithms for large-scale optimization, privacy-preserving techniques, and tools for evaluating machine learning systems' validity. Education: BS and MS in Computer Science (Stanford University, 2007–2008), MA in Statistics (UC Berkeley, 2012), and PhD in EECS (UC Berkeley, 2014). Research Interests: His work addresses three core areas: (1) optimizing trade-offs between computational resources and statistical performance, (2) creating scalable optimization methods, and (3) quantifying confidence in machine learning systems. Recent publications highlight contributions to privacy in federated learning, robust statistical validation, and uncertainty quantification. Articles: His recent work explores topics like distribution-free M-estimation, private federated learning, and instance-optimal mechanisms for statistical estimation. These studies emphasize privacy, robustness, and scalable solutions for modern data challenges. Advising & Grants: While no advisees are listed, his research has been supported by grants focused on optimization, privacy, and statistical theory.

Brett KolesnikView profile

Research Fellow

• Probability Theory
• Random Structures
• Bootstrap Percolation
• +4 more

Brett Kolesnik is a Research Fellow in the Department of Statistics at the University of Warwick. His research focuses on probability theory, random structures, bootstrap percolation, and interactions with combinatorics. He has held postdoctoral fellowships at UC Berkeley, San Diego, and the University of Oxford, and was a Senior Demy at Magdalen College. His work includes organizing workshops on bootstrap percolation and collaborating with leading researchers in probability and combinatorics. Education: PhD in Mathematics from the University of British Columbia (advised by Omer Angel). Notable awards include the NSERC Postdoctoral Fellowship and the Florence Nightingale Bicentennial Fellowship in Statistics. Research interests span bootstrap percolation models, random graph dynamics, and stochastic processes. Recent work includes studies on Brownian map geometry, tournament score sequences, and Coxeter group structures. Selected articles explore topics such as critical beta-splitting processes, Catalan percolation, and random walks on algebraic structures. His publications appear in top journals like Electronic Journal of Probability and Annals of Applied Probability . Awards include the Florence Nightingale Fellowship and NSERC Postdoctoral Fellowship. Professional involvement includes organizing the 2024 BIRS workshop on Bootstrap Percolation and contributing to interdisciplinary collaborations in probability and combinatorics.

Simone GiombiView profile

Professor

• High Energy Theoretical Physics
• Quantum Field Theory
• String Theory
• +3 more

Simone Giombi is a Professor of Physics at Princeton University and currently serves as Associate Chair and Director of Graduate Studies. He holds a B.Sc. in Theoretical Physics from the University of Bologna, Italy, and a Ph.D. in Physics and Astronomy from Stony Brook University (2007). His research focuses on high-energy theoretical physics, quantum field theory, string theory, and their interconnections, particularly exploring higher-spin gravity and holographic dualities. He has held postdoctoral positions at Harvard University and the Perimeter Institute for Theoretical Physics. Giombi's work includes groundbreaking contributions to AdS/CFT correspondence, Wilson loop defects, and quantum M2 branes. He has been recognized with prestigious awards, including the New Horizons in Physics Prize (2017) and the SIGRAV Prize (2014). His recent articles (2022–2025) emphasize non-planar corrections in ABJM theory, boundary reparametrizations in AdS2, and RG interfaces from double-trace deformations. His research also engages with fermionic CFTs, line defects, and quantum fluctuations in Wilson loops. Awards: New Horizons in Physics Prize (2017), SIGRAV Prize (2014) Advising: Students include Yagmur Erhan and Jieru Shan Labs/Teams: Active in Princeton's High Energy Theory Group

Silviu PufuView profile

Professor

• Quantum Field Theory
• String Theory
• Gravity
• +3 more

Silviu Pufu is a Professor of Physics at Princeton University, where he earned both his A.B. (2007) and Ph.D. (2011) in Physics. Prior to his faculty position, he was a Pappalardo Postdoctoral Fellow at MIT (2011–2013). His research focuses on quantum field theory, string theory, and gravity, with emphasis on conformal field theory, gauge/gravity duality, and lattice gauge theory. He has received the Alfred P. Sloan Research Fellowship (2017) and led the Simons Collaboration for Nonperturbative Bootstrap (2016–2023). His work explores advanced topics such as AdS/CFT correspondence, M-theory corrections, and non-perturbative bootstrap methods. Pufu advises three graduate students: Ross Dempsey, Debaditya Pramanik, and Benjamin Søgaard. His research outputs span theoretical frameworks like super-Yang-Mills theories, M-theory orbifolds, and lattice Hamiltonian formulations of QCD. His 2020–2025 publications highlight contributions to bootstrap techniques, holographic calculations, and precision studies of strongly coupled systems.

• Applied Statistics
• Big Data
• Economic Models and Forecasting
• +2 more

Prof Terry O'Neill serves as Executive Dean at the Bond Business School , Bond University, while also holding the title of Professor and Director at the Centre for Data Analytics. His research spans applied statistics , big data analytics , and financial modeling , with over 80 publications (20% in A* journals). Grants: Lead investigator on five ARC Discovery/Linkage grants totaling $2.99M, including studies on retirement savings, climate change modeling, and financial crisis resilience. Research Trends: Focus on financial literacy , retirement economics , and myelopoiesis , bridging finance with computational biology through collaborations with his spouse Helen O'Neill. Academic Leadership: Established Bond University's University Centre in Actuarial and Financial Big Data Analytics to elevate institutional research prominence in data science.

Yiping LuView profile

Assistant Professor

• Machine Learning
• Scientific Machine Learning (AI4Science)
• Numerical Algorithms
• +7 more

Yiping Lu is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. His research focuses on developing interdisciplinary approaches combining domain knowledge (differential equations, stochastic processes), machine learning, and experiments. Key interests include scientific machine learning (AI4Science), stochastic simulation, and robust machine learning. Education: Ph.D. in Applied and Computational Mathematics, Stanford University (2023) B.S. in Computational Mathematics, Peking University (2019) Research Highlights: Hybrid research integrating ML with scientific domains like PDEs and inverse problems Development of Physics-Informed Learning frameworks Contributions to deep learning theory (ResNets, neural collapse) Advances in kernel operator learning and adversarial robustness Awards: CPAL Rising Star Award (2024) University of Chicago Data Science Rising Star (2022) Stanford Interdisciplinary Graduate Fellowship (2021) Labs/Teams: SCALE Lab (Scientific Computation and Learning at Northwestern) Collaborations with NYU's Courant Institute and Stanford

Claire DonnatView profile

Assistant Professor

• Statistics
• Machine Learning
• Graph Neural Networks (GNNs)
• +7 more

Claire Donnat is an Assistant Professor in the Department of Statistics at the University of Chicago, specializing in statistical and machine learning methods for graph-structured and high-dimensional data. Her work bridges theoretical innovation with applications in biomedical research, environmental science, and public health. Education: B.S. and M.S. in Applied Mathematics from Ecole Polytechnique; Ph.D. in Statistics from Stanford University (2020). Her research focuses on three methodological directions: (1) statistical foundations for graph neural networks (GNNs), (2) structured estimation with graph constraints, and (3) multimodal data integration with uncertainty quantification. Key applications include thermotolerance in photosynthetic microbes, family network analysis for child welfare, and spatial transcriptomics. The 15 most recent publications highlight her work in GNNs, CCA, tensor modeling, and epidemic analysis, with keywords spanning statistics, machine learning, and network science. Her methodological contributions address challenges in sparsity, graph topology, and heterogeneous data fusion. Scientific Awards: Facebook Research Award (2021), C3.AI COVID Grand Challenge winner (2020), Lumiata hackathon winner (2020), Stanford Centennial Award (2019), and others. Claire's research group actively recruits postdocs and students for projects involving graph-based modeling, data integration, and biomedical applications. She also provides research consulting in statistical methodology and graph modeling for life sciences.

• Nonparametric Statistics
• High Dimensional Data Analysis
• Statistical Methods in Finance
• +7 more

Vladimir Spokoiny is a Professor at the Departments of Mathematics and Economics of the Humboldt University of Berlin and Head of the Research Group "Stochastic Algorithms and Nonparametric Statistics" at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany. His research spans multiple areas of statistics, machine learning, and financial mathematics, with significant contributions to nonparametric statistics, high-dimensional data analysis, and statistical methods in finance. Spokoiny received his M.Sc. in applied mathematics from the Moscow Institute of Railway Engineering in 1981 and his Ph.D. in mathematics from Lomonosov Moscow State University in 1988. He completed his Habilitation at Humboldt University in 1996. His academic career includes positions at the All-Union Institute of Railway Transport in Moscow, the Institute for Information Transmission Problems in Moscow, and the Institute for Applied Analysis and Statistics in Berlin before joining the Weierstrass Institute and Humboldt University where he has been a professor since 2002. Spokoiny's research focuses on adaptive nonparametric smoothing and hypothesis testing, high dimensional data analysis, statistical methods in finance, image analysis with applications to medicine, classification, and nonlinear time series. His work often addresses the challenges of nonstationarity in time series data and develops innovative methods for volatility estimation and risk management. He has made significant contributions to the development of adaptive weights smoothing procedures, which have applications in image processing, community detection, and manifold learning. His recent work has expanded into high-dimensional statistics, Bayesian inference, and optimization methods for machine learning, with publications demonstrating novel approaches to Gaussian approximation, Laplace methods, and statistical inference in non-Euclidean spaces. Spokoiny has supervised numerous PhD students including Oliver Reiss, Danilo Mercurio, Ying Chen, Elmar Diederichs, and Mstislav Elagin, whose research has focused on mathematical finance, time series analysis, and statistical methods. He serves as an Associate Editor for The Annals of Statistics (since 2004) and Statistics and Decisions (since 2002), and has previously served on the editorial board of the Journal of Statistical Planning and Inference. His professional activities include reviewing for major statistical journals including Annals of Statistics, Bernoulli, Econometrica, and Journal of American Statistical Association, as well as reviewing grant proposals for the National Science Foundation (USA), German Research Foundation, and Netherlands Organisation for Scientific Research. Spokoiny is a member of several professional societies including the International Statistical Institute, American Statistical Association, Institute of Mathematical Statistics, and Bernoulli Society. He is fluent in Russian (mother tongue), English, and German, and has good knowledge of French. His research group at WIAS focuses on developing novel statistical methodologies with applications across various scientific domains, particularly emphasizing adaptivity and robustness in complex data environments. The group's work has significant implications for financial risk management, medical imaging, and machine learning applications, with recent publications addressing fundamental questions in high-dimensional statistics and nonparametric inference.

Xiaofeng ShaoView profile

Professor

• Econometrics
• Time Series Analysis
• Change-Point Analysis
• +3 more

Xiaofeng Shao is a Professor of Statistics & Data Science at Washington University in St. Louis, with a joint appointment in the Department of Economics. He holds a PhD from the University of Chicago and previously served at the University of Illinois at Urbana-Champaign for 18 years. He is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association. His research focuses on econometrics, time series analysis, change-point detection, high-dimensional statistics, nonparametric methods, and functional data analysis. Recent work emphasizes object-valued time series modeling and machine learning applications in high-dimensional and imaging data. Notable contributions include the dependent wild bootstrap method and self-normalization techniques for time series inference. Key awards include Fellowships from leading statistical societies. His publications span over 20 years, addressing topics like change-point detection in climate projections, statistical methods for COVID-19 infection trends, and high-dimensional dependence testing.

Roman FeimanView profile

Assistant Professor

• Cognitive Development
• Cognitive Psychology
• Cognitive Science
• +3 more

Roman Feiman is the Thomas J. and Alice M. Tisch Assistant Professor of Cognitive, Linguistic, and Psychological Sciences and an Assistant Professor of Linguistics at Brown University. He directs the Brown Language and Thought Lab, focusing on how humans combine words into meaningful sentences and develop logical reasoning abilities. His research integrates methods from cognitive developmental psychology, psycholinguistics, and formal semantics. Feiman holds a PhD in Psychology from Harvard University (2015), followed by postdoctoral training at Harvard and UC San Diego. His work explores the cognitive systems underlying language and thought, including negation comprehension, quantifier scope, and the development of exact equality concepts. He teaches courses such as Language Processing in Humans and Machines and Logic in Language and Thought . His research interests span cognitive development, linguistic pragmatics, and the language of thought hypothesis. Notable findings include studies on children’s understanding of negation and how logical principles shape early language acquisition. Feiman has been recognized with the 2023 Henry Merritt Wriston Fellowship. His lab investigates topics like word referencing, semantic development, and the interplay between language and nonverbal reasoning. Recent work examines how neural networks might model human cognitive processes, bridging AI and psychological theory.