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Tommy SveenView profile

Professor

• Monetary Economics
• Macroeconomics
• Open Economy Macroeconomics

Tommy Sveen is Professor of Economics at BI Norwegian Business School and serves as Head of the Department of Economics. He also holds an adjunct professorship at the Norwegian School of Economics (NHH) and directs the Centre for Monetary Economics. His career includes extensive experience at Norges Bank in roles including Assistant Director and Senior Advisor across Research and Monetary Policy departments. Education: PhD (Dr.Oecon) in Economics from Norwegian School of Economics (NHH), 2001 Master Cand. Oecon from Norwegian School of Economics (NHH), 1995 Master of Science in Business from BI Norwegian Business School, 1988 Research Focus: Professor Sveen specializes in Monetary Economics, Macroeconomics, and Open Economy Macroeconomics. His work examines monetary-fiscal policy interactions, investment dynamics under nominal rigidities, labor market adjustments to technological shocks, and exchange rate mechanisms. He frequently employs New Keynesian frameworks to analyze policy transmission and economic stabilization. Publication Trends: Recent research (2020-2025) focuses on optimal policy coordination in small open economies, monetary responses to export shocks, and labor market margins. Earlier work (2013-2018) established foundational insights into lumpy investment behaviors, Taylor rule applications, and real exchange rate determinants. His articles consistently integrate microeconomic foundations with macroeconomic policy analysis. Leadership: Directs the Centre for Monetary Economics and authors the independent evaluation report Norges Bank Watch , critically assessing Norwegian monetary policy.

• Microeconomic Theory
• Game Theory
• Mechanism Design
• +4 more

Shurojit Chatterji is a Professor of Economics at the School of Economics, Singapore Management University, where he conducts research at the intersection of microeconomic theory and social choice. His academic foundation includes a Ph.D. in Economics from SUNY at Stony Brook (1993) and a B.A. (Honours in Economics) from the University of Delhi (1988). His educational qualifications are: Ph.D. in Economics, SUNY at Stony Brook, 1993 B.A. (Honours in Economics), University of Delhi, 1988 Professor Chatterji's research centers on Mechanism Design , Social Choice Theory , and Game Theory , with deep investigations into strategy-proofness, preference domain structures, and efficient allocation mechanisms. His work rigorously examines single-peakedness in voting systems, multidimensional choice environments, and the welfare implications of redundant assets under heterogeneous forecasts. This theoretical framework extends to dynamic economic models involving Radner equilibria and imperfect foresight, bridging foundational microeconomic principles with practical design applications. Analysis of his 15 most recent publications (2020-2025) reveals consistent focus on strategy-proof mechanisms across unidimensional and multidimensional domains, probabilistic social choice, and the decentralizability of efficient allocations under uncertainty. Key trends include the taxonomy of non-dictatorial domains, decomposability properties in fractional allocation, and the role of redundant assets in welfare economics—demonstrating how theoretical insights address real-world market imperfections and institutional design challenges. As an academic advisor, he has mentored PhD student Paulo Daniel Salles Ramos. His extensive publication record across top economic journals indicates sustained research activity and scholarly influence, though specific grant details are not documented in available sources.

Aaron RothView profile

Professor

• Algorithms
• Machine Learning
• Differential Privacy
• +10 more

Aaron Roth is the Henry Salvatori Professor of Computer and Cognitive Science at the University of Pennsylvania, affiliated with the Department of Computer and Information Science in the School of Engineering and Applied Science. He holds a secondary appointment in the Department of Statistics and Data Science at the Wharton School and is associated with several research centers including PRiML, the Warren Center for Network and Data Sciences, and the AMCS program. He received his PhD from Carnegie Mellon University under Avrim Blum and was a postdoc at Microsoft Research New England. His research focuses on algorithms and machine learning, particularly in private data analysis, fairness in machine learning, game theory, mechanism design, and learning theory. His work bridges theoretical computer science with societal concerns, advocating for ethically aware algorithm design. He co-authored the book The Ethical Algorithm with Michael Kearns, which explores how to embed social values like privacy and fairness into algorithmic systems. His recent publications show a strong trend toward uncertainty quantification, multicalibration, conformal prediction, and fairness in reinforcement learning and high-dimensional settings. He frequently publishes in top-tier venues such as STOC, FOCS, ICML, NeurIPS, and COLT, often with a focus on rigorous theoretical foundations with practical implications. Hans Sigrist Prize Presidential Early Career Award for Scientists and Engineers (PECASE) Alfred P. Sloan Research Fellowship NSF CAREER award Google Faculty Research Award Amazon Research Award Yahoo Academic Career Enhancement award Roth has advised numerous PhD students and postdocs, many of whom now hold academic or industry research positions. He is also an Amazon Scholar at AWS and has served in advisory roles for companies like Apple, Facebook, Leapyear, and Spectrum Labs. He has been active in organizing workshops and tutorials on differential privacy, fairness, and adaptive data analysis, and has given keynotes at major conferences and institutions worldwide. He leads research groups and collaborates widely across Penn, focusing on responsible AI, privacy, and algorithmic fairness. His lab produces foundational work on calibration, unlearning, privacy-preserving learning, and equitable decision-making systems.

LING Chun KaiView profile

Assistant Professor

• Artificial Intelligence
• Decision Making & Planning
• Machine Learning
• +13 more

LING Chun Kai is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research focuses on multiagent systems, computational game theory, and machine learning applications in adversarial real-world domains like cybersecurity and logistics. Educational background includes a PhD in Computer Science (2017-2023) from Carnegie Mellon University and a First Class BEng in Computer Engineering (2015) from NUS. Previously, he was a Postdoctoral Research Scientist at Columbia University. Current research interests span computational game theory, machine learning for multi-agent systems, equilibrium characterization in imperfect information settings, and applications in network security, logistics, and recreational games. Key methodological contributions include scalable algorithms for game solving, differentiable game solvers, and copula-based statistical modeling. Recent publications focus on attacker-defender graph games, language negotiation agents, and modeling games with incomplete information. Collaborations include researchers from Columbia University, Carnegie Mellon, and institutions working on GameSec, AAAI, Neurips, and ICML venues. Scientific Awards: IJCAI 2018 Distinguished Paper Award GameSec 2023 Best Paper Award GameSec 2024 Best Paper Award Singapore Teaching and Academic Research Talent Scheme (2024) Teaching includes courses on AI Planning and Decision Making (CS4246, CS5446) and Advanced Topics in Artificial Intelligence (CS6208).

Tomasz StrzaleckiView profile

Professor

• Decision Theory
• Behavioral Economics
• Microeconomics
• +4 more

Tomasz Strzalecki is a Professor in the Department of Economics at Harvard University. His research centers on decision theory, with a focus on ambiguity aversion , temporal preferences , stochastic choice , and bounded rationality . He earned his PhD in Economics from Northwestern University in 2008. Education: PhD in Economics (2008), Northwestern University His scholarly work spans theoretical and applied economics, including key contributions to random utility models , dynamic decision-making , and neuroeconomic modeling . Recent publications, such as Stochastic Choice Theory (2025) and Variational Bayes and non-Bayesian Updating (2024), reflect his ongoing exploration of Bayesian inference and behavioral deviations. Earlier work in Econometrica and American Economic Review established foundational models for choice aversion , time inconsistency , and ambiguity evaluation . Tomasz’s research has been published in top journals like Econometrica , American Economic Review , and Proceedings of the National Academy of Sciences , covering themes such as probabilistic sophistication , decision timing , and collective action in development economics. His co-authors include prominent economists like Drew Fudenberg, Mira Frick, and Larry Epstein.

Sheryl GraceView profile

Associate Professor

• Aerodynamics
• Fluid Dynamics
• Acoustics
• +4 more

Sheryl Grace is an Associate Professor of Mechanical Engineering at Boston University, leading the Unsteady Fluid Mechanics & Acoustics Laboratory (UFMAL). Her primary appointment is in the Department of Mechanical Engineering within the College of Engineering. She holds a PhD from the University of Notre Dame. Her research focuses on unsteady aerodynamics, aeroacoustics, and fluid-structure interactions, with applications in aerospace systems, propulsion technologies, and biological acoustics. Notable projects include NASA-funded work on quieter vertical lift vehicles and computational modeling of gerbil hearing mechanics. Professor Grace’s research interests span aerodynamics, fluid dynamics, and acoustics. She develops analytical and computational models to predict sound and vibration generated by unsteady flows interacting with solid structures. Recent studies include noise reduction in aircraft wings, turbine blade fatigue analysis, and acoustic scattering in gerbil ears. Her work bridges theoretical models with practical engineering solutions, emphasizing cost-effective predictive tools for next-generation systems. Her publications highlight advancements in shock-droplet interactions, cavitation modeling, and machine learning applications in aeroacoustics. Collaborative projects include multi-institutional efforts to address urban air vehicle noise challenges. While no explicit awards are listed, her contributions to computational acoustics and fluid dynamics are recognized through extensive peer-reviewed output. Advising and grants: Professor Grace leads the UFMAL lab and has secured funding from agencies like NASA. Her research integrates fluid mechanics, acoustics, and computational methods to address industrial and environmental noise issues. She collaborates across disciplines, including mechanical engineering, aerospace, and biomedical acoustics.

Vinayak DixitView profile

Professor

• Transportation Risk Analysis
• Highway Safety
• Travel Time Uncertainty
• +6 more

Professor Vinayak Dixit serves as the IAG Chair of Risk in Smart Cities and Director of the Research Centre for Integrated Transport Innovation (rCITI) at the University of New South Wales (UNSW), within the School of Civil and Environmental Engineering. With a distinguished career spanning academia and research leadership, Professor Dixit has established himself as a leading expert in transportation risk analysis and smart city infrastructure. Professor Dixit's research focuses on studying risk in transportation infrastructure systems, with particular emphasis on highway safety, travel time uncertainty, and resilience against natural and man-made disasters. His work integrates cutting-edge approaches including quantum computing applications for transportation network optimization, analysis of connected and automated vehicles, and development of models for transportation resilience. His research interests span multiple disciplines, bridging civil engineering, transportation science, risk analysis, and computational methods. Professor Dixit has secured significant research funding from prestigious organizations including the United States National Science Foundation, Federal Highway Administration, and the Strategic Highway Research Program of the Transportation Research Board. His leadership extends to directing the Research Centre for Integrated Transport Innovation (rCITI), where he oversees a comprehensive research program addressing critical transportation challenges in smart cities through multiple focus areas including Connected Mobility Services, Deep Data and Digitization, Engineering Smart Cities & Logistics, Human-Centred And Automated Systems Design, and Integrated Infrastructure Strategic Planning. Professor Dixit previously served as the Associate Director of Research for the Gulf Coast Centre for Evacuation and Transportation Resiliency at Louisiana State University, where he founded the Driving Simulator Laboratory in collaboration with other faculty members. This demonstrates his longstanding commitment to innovative research infrastructure development and interdisciplinary collaboration across engineering, economics, computer science, and urban planning to address complex transportation challenges in the 21st century.

Noel T. ClemensView profile

Professor

• Aerothermodynamics
• Fluid Mechanics
• Hypersonics
• +7 more

Noel T. Clemens serves as a Professor and holds the prestigious Clare Cockrell Williams Centennial Chair in Engineering within the Aerospace Engineering and Engineering Mechanics Department at the University of Texas at Austin's Cockrell School of Engineering. He has been a faculty member since 1993 and served as department chair from 2012 to 2020. His research laboratory is part of the Center for Aeromechanics Research (CAR) where he directs the Flowfield Imaging Laboratory. Dr. Clemens' research focuses on experimental investigations of hypersonic flows, turbulent combustion, and advanced optical diagnostic techniques. His current work emphasizes 3D shock wave/boundary layer interactions, inlet unstart control, flashback in high-pressure combustors, turbulent combustion with non-equilibrium effects, and high-temperature ablation phenomena. He has pioneered laser-based measurement techniques for extreme environments, particularly for hypersonic flight applications where conventional measurement approaches fail. His recent publication record through 2025 demonstrates continued leadership in experimental fluid dynamics, with particular emphasis on plasma diagnostics for ablation studies, shock/boundary layer interaction physics, and advanced optical measurement techniques for extreme environments. The research spans fundamental fluid mechanics investigations to applied aerospace engineering problems relevant to hypersonic vehicle development. Elected to National Academy of Engineering (2024) AIAA Aerodynamic Measurement Technology Award (2022) Elected AIAA Fellow (2019) National Science Foundation Presidential Faculty Fellow (1996) Editor-in-Chief of Experiments in Fluids (2009-2013) Fellow of the American Physical Society Dr. Clemens has secured substantial research funding for his experimental investigations in hypersonics and combustion, leading multiple major research projects with government and industry partners. His laboratory facilities include advanced wind tunnels and state-of-the-art optical diagnostic systems for high-speed flow visualization. The Flowfield Imaging Laboratory at UT Austin serves as a national resource for advanced flow measurement techniques development. As an educator, he teaches core courses in compressible flow, viscous flow, combustion, experimental methods, and laser diagnostic techniques, training the next generation of aerospace engineers in both fundamental principles and cutting-edge measurement technologies.

Jaime PeraireView profile

Professor

• Computational Fluid Dynamics
• Finite Element Methods
• Aerodynamics
• +7 more

Jaime Peraire is the H.N. Slater Professor of Aeronautics and Astronautics at MIT, affiliated with the School of Engineering. He leads research in computational mechanics, aerodynamics, and numerical methods for partial differential equations, with key roles as former Department Head (2011-2018) and Director of the Aerospace Computational Design Lab (1993-2011). His expertise spans finite element methods, shock capturing algorithms, and high-order numerical techniques applied to hypersonic flows, space weather, and metamaterials. Education includes a Ph.D. from the University of Wales (1986) and engineering degrees from the University of Barcelona (1983, 1987). He holds prestigious awards like the T.J. Hughes Medal (2015) and the Ildefons Cerdá Medal (2015). His work bridges computational science and engineering, with contributions to discontinuous Galerkin methods, mesh adaptivity, and GPU-accelerated simulations. Research interests emphasize high-fidelity modeling of compressible flows, plasma dynamics, and terahertz spectroscopy. Notable projects include MIT’s space weather modeling initiative and metamaterial fabrication using atomic layer lithography. His labs collaborate across MIT’s Schwarzman College of Computing, IDSS, and CCSE to advance computational tools for aerospace and environmental systems. Awards: Over 10 major prizes, including NASA Exceptional Achievement (1997) and IACM Young Researchers Award (1998). Grants/Advising: Led NSF-funded space weather projects and advised numerous PhD students in computational engineering. Labs: Aerospace Computational Design Lab, MIT Schwarzman College of Computing collaborations.

Asuman OzdaglarView profile

Professor

• Optimization
• Machine Learning
• Game Theory
• +7 more

Asuman Ozdaglar is the MathWorks Professor of Electrical Engineering and Computer Science and Department Head of EECS at MIT. She also serves as Deputy Dean of Academics for the MIT Stephen A. Schwarzman College of Computing. Her research focuses on large-scale networked systems, including optimization, game theory, social networks, and distributed algorithms. Education: BS in Electrical and Electronics Engineering from Middle East Technical University (1996), SM (1998) and PhD (2003) in Electrical Engineering and Computer Science from MIT. Research emphasizes nonlinear optimization, machine learning, and network economics. She leads work on robust algorithms, misinformation dynamics, and networked systems. Affiliated with the Laboratory for Information and Decision Systems (LIDS) and the Operations Research Center (ORC). Her contributions span theoretical and applied domains, including distributed optimization methods, social network analysis, and privacy-preserving data mechanisms. Active in shaping academic policy through her roles in the College of Computing.

• Computational Fluid Dynamics
• Scientific Machine Learning
• Data-Driven Modeling
• +3 more

Shaowu Pan is an Assistant Professor of Aerospace Engineering at Rensselaer Polytechnic Institute (RPI), affiliated with the Future of Computing Institute (FOCI) and the Scientific Computation Research Center (SCOREC). He holds a Ph.D. in Aerospace Engineering and Scientific Computing from the University of Michigan and completed a postdoctoral fellowship at the University of Washington’s AI Institute in Dynamic Systems. Education: Ph.D., University of Michigan, 2021 M.S., University of Michigan, 2015 B.E. & B.S., Beihang University, 2013 Research Interests: His work focuses on the intersection of computational fluid dynamics, data-driven modeling, and scientific machine learning. Key areas include operator-theoretic modeling of fluid flows, generative AI for physical systems, and physics-informed neural networks. He develops novel algorithms for reduced-order modeling and stability-preserving surrogate models, with applications in turbulence, plasma physics, and aerodynamics. Key Contributions: Developed PyKoopman , an open-source Python package for Koopman operator approximation. Pioneered mesh-agnostic representation methods like Neural Implicit Flow for spatio-temporal data. Advanced physics-informed neural networks for solving Grad-Shafranov equations and plasma equilibrium problems. Awards & Recognition: John Tichy Junior Faculty Travel Grant (2024) Chinese Outstanding Student Abroad Award (2021) Richard and Eleanor Towner Prize Nominee (2019) Teaching & Mentorship: He teaches courses like MANE 2110: Numerical Methods and Programming for Engineers and mentors multiple Ph.D., master’s, and undergraduate students. His doctoral committee involvement spans interdisciplinary projects in fluid dynamics and AI. Grants & Software: Lead PI for NSF-funded projects on neural representation learning for turbulent flows. Developed software tools like spKDMD and Warp-DG for dynamics analysis and CFD simulations. Labs & Collaborations: Collaborates with institutions like Los Alamos National Laboratory and actively participates in conferences (e.g., AIAA SciTech, SIAM). His research bridges computational science, machine learning, and fluid dynamics to address complex nonlinear systems.

Christopher WaltersView profile

Professor

• Labor Economics
• Economics of Education
• Applied Econometrics
• +5 more

Christopher Walters is a Professor at the Kenneth C. Griffin Department of Economics, University of Chicago, and previously served as an Assistant Professor at UC Berkeley (2013-2025). He is a Research Associate at the National Bureau of Economic Research, Research Fellow at IZA, and Faculty Affiliate at MIT's School Effectiveness and Inequality Initiative (SEII). PhD in Economics, MIT (2013) B.A. in Economics and Philosophy, University of Virginia (2008) Walters specializes in Labor Economics and the Economics of Education , focusing on school choice, early childhood interventions, and program evaluation. His work combines applied econometric methods with discrete choice modeling to analyze educational investments and labor market outcomes. Walters' recent publications examine class size effects, teacher quality impacts, and school finance policies, reflecting his interest in improving educational equity through rigorous empirical analysis. Research Fellow at IZA He collaborates with institutions like J-PAL North America and MIT Blueprint Labs, contributing to evidence-based policy design in education and labor economics.

Salem LahlouView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• GFlowNets
• +7 more

Salem Lahlou is an Assistant Professor in the Machine Learning Department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), having joined in September 2024. He previously served as a Senior Researcher at the Technology Innovation Institute (TII) in 2024. His academic background includes a PhD from Mila and Université de Montréal (UdeM) under Yoshua Bengio (2023), with prior studies in applied mathematics at École Polytechnique and statistical learning at École Normale Supérieure Paris-Saclay. His research focuses on developing more capable and reliable AI systems through three interconnected pillars: Novel Method Development : Core contributions to Generative Flow Networks (GFlowNets), uncertainty estimation techniques (DEUP), and curriculum learning frameworks Large Language Model Advancement : Enhancing reasoning capabilities and alignment through preference optimization and trace-based learning Community Tooling : Creation of torchgfn library for GFlowNets and benchmarks including BabyAI, FinChain, and LLM-BabyBench Core research areas span Machine Learning, GFlowNets, Uncertainty Estimation, LLM Reasoning, Reinforcement Learning, and AI for Science. Recent publications (2023-2025) demonstrate strong emphasis on GFlowNet theory/improvements (8+ papers), LLM reasoning evaluation (FinChain, LLM-BabyBench), uncertainty quantification, and societal AI impacts. Key application domains include mathematical reasoning, financial systems, privacy preservation, and cognitive science. He currently advises graduate students including Junyi (privacy risks in SNNs) and Abhijith (LLM reasoning). His group collaborates with MBZUAI faculty (Nils Lukas, Alham Fikri, Mingming Gong, Martin Takac) and industry partners on projects involving Conversational AI, Personalization, and Affective AI.

Mark CrowleyView profile

Associate Professor

• Reinforcement Learning
• Deep Learning
• Ensemble Methods
• +6 more

Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo , with a cross-appointment in the Cheriton School of Computer Science . He is actively involved in the Waterloo Artificial Intelligence Institute (WAII) , the Waterloo Institute for Complexity and Innovation (WICI) , and serves as National Secretary for the Canadian Artificial Intelligence Association (CAIAC) , coordinating the Canadian Conference on AI . Research interests span the theoretical and applied aspects of Reinforcement Learning , Deep Learning , Manifold Learning , and Ensemble Methods . His work addresses challenges in domains with spatial dynamics, multi-agent systems, and uncertainty, particularly in Computational Sustainability (forest fire management, sustainable forestry), Autonomous Driving , Medical Imaging , and Material Design . Recent research focuses on integrating causal modeling with generative representation learning to improve out-of-distribution robustness in motion forecasting applications. Key publications include foundational work on ChemGymRL environments for safe chemical process reinforcement learning, Generative Causal Representation Learning for robust forecasting, and collaborative work on multi-advisor reinforcement learning in multi-agent settings. He co-authored a textbook Elements of Dimensionality Reduction and Manifold Learning (Springer, 2023) with Prof. Ali Ghodsi and Prof. Fakhri Karray. Teaching includes graduate and undergraduate courses in Algorithm Design , Computational Intelligence , Reinforcement Learning , and Data Modeling at the University of Waterloo since 2018. His research group has produced several notable graduates including Benyamin Ghojogh (2021), who continued as a postdoc until 2022.

Klaus RitzbergerView profile

Professor

• Game Theory
• Economic Theory
• General Equilibrium
• +3 more

Klaus Ritzberger is a Professor of Economics at the Department of Economics, Royal Holloway, University of London. He holds academic affiliations at multiple institutions, including the Institute for Advanced Studies (IHS) in Vienna and the Vienna Graduate School of Finance (VGSF). His career spans leadership roles, such as Chairman of the Department of Economics and Finance at IHS (1999–2005) and numerous visiting professorships globally. Education: Magister rer.soc.oec. (1982), University of Vienna Doktor rer.soc.oec. (1987), University of Vienna Habilitation in Economic Theory (1996), University of Vienna Research focuses on game theory, economic theory, and general equilibrium. His work explores strategic stability, evolutionary game theory, and institutional economics. Recent contributions include analyses of bankruptcy in uncertain equilibria and multi-lateral bargaining dynamics. Awards include the Economic Theory Fellow (2011) and multiple Oskar Morgenstern Awards. He has secured grants for projects like 'Foundations of the Theory of Extensive Form Games' (2009–2013) and serves on editorial boards of journals like Economic Theory and International Journal of Game Theory .