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Alexandre KohlhasView profile

Associate Professor

• Macroeconomics
• Monetary Policy
• Economic Uncertainty
• +3 more

Alexandre Kohlhas is a University of Oxford Associate Professor in the Department of Economics and a William R Miller Fellow at St Edmund Hall . Previously an Assistant Professor at Stockholm University's Institute for International Economic Studies, his research bridges macroeconomics , monetary policy , and information economics with a focus on expectation formation and uncertainty. Doctorate from Pembroke College, University of Cambridge His work explores economic uncertainty and information asymmetry , analyzing how heterogeneous expectations and asymmetric attention shape macroeconomic outcomes. Recent publications examine data-driven macroeconomic modeling , wealth heterogeneity , and higher-order beliefs in monetary policy contexts. Key research trends include expectation formation in uncertain environments, monetary transmission mechanisms , and information aggregation in markets. His 14 most recent articles demonstrate consistent engagement with theoretical and applied macroeconomics through behavioral and informational lenses. William R Miller Fellow Tutorial Fellow in Economics Governing Body Fellow Reach Alexandre via email for academic collaboration or supervision inquiries.

Thomas ColasView profile

Researcher

• Theoretical Cosmology
• Open Quantum Systems
• Effective Field Theories
• +3 more

Dr. Thomas Colas is a theoretical cosmologist at the University of Cambridge's Department of Applied Mathematics and Theoretical Physics (DAMTP), part of the Relativity and Gravitation research group. His work focuses on Open Effective Field Theories (OEFT) and non-unitary effects in the early universe, leveraging quantum information theory and non-equilibrium quantum field theory techniques. His research bridges cosmology with condensed matter physics and quantum optics to understand inflationary dynamics and quantum fluctuations' role in cosmic inhomogeneities. Key research themes include modeling uncertainties in early universe physics through OEFT, studying decoherence and dissipation in cosmological systems, and analyzing quantum-to-classical transitions during inflation. His work addresses foundational questions about how quantum mechanics operates in extreme cosmological contexts, such as the primordial vacuum's imprint on the cosmic microwave background and large-scale structures. Recent publications highlight advancements in OEFT frameworks for light-matter interactions, entropy calculations in curved spacetimes, and recoherence mechanisms in expanding universes. These contributions advance our understanding of how hidden sectors and unaccounted degrees of freedom influence cosmological observables. Dr. Colas maintains active collaborations through his research group and engages with experimental cosmology via data analysis tools like the SDSS/BOSS dataset, bridging theoretical insights with observational tests of early universe models.

Jessica HullmanView profile

Professor

• Artificial Intelligence
• Human-Computer Interaction
• Uncertainty Quantification
• +4 more

Jessica Hullman is the Ginni Rometty Professor of Computer Science at Northwestern University's McCormick School of Engineering and a Faculty Fellow at the Institute for Policy Research. Her research develops theoretical frameworks and interfaces for human-AI collaboration, focusing on uncertainty quantification, statistical modeling, and decision-making in domains like scientific research and AI-assisted analysis. Education: PhD in Information (Visualization), University of Michigan (2013) MS in Information Analysis, University of Michigan (2008) BA in Comparative Studies, Ohio State University (2003) Tableau Postdoctoral Fellowship, UC Berkeley (2015) Research Focus: Hullman's work bridges formal models of rational inference (e.g., Bayesian decision theory) with real-world applications. Key areas include: human-AI complementarity in decision-making, visualization of uncertainty, statistical reform, and LLM applications in behavioral science. Her research consistently addresses the alignment of data-driven interfaces with human cognitive capabilities. Publication Trends: Recent work demonstrates a strong emphasis on human-AI collaboration frameworks, decision-theoretic evaluation of visualizations, and methodological rigor in machine learning and social science. Key themes include uncertainty quantification (conformal prediction, privacy tradeoffs), behavioral experiments in AI-assisted tasks, and critical analyses of scientific practices. Awards & Honors: Microsoft Faculty Fellow (2019) Google Faculty Award NSF CAREER, Medium, and Small Awards Multiple best paper/honorable mention awards at top HCI/visualization venues (CHI, VIS) Funding & Labs: Principal Investigator for NSF-funded projects including HCC: Medium on visualization tools. Previously affiliated with University of Washington's Interactive Data Lab and DataLab. Current research includes NSF-supported work on improving data visualization for reasoning about analytical assumptions.

Zhibin ChenView profile

Assistant Professor

• Transportation Network Modeling and Optimization
• Intelligent Transportation Systems
• Discrete Optimization
• +4 more

Zhibin Chen is an Assistant Professor of Engineering at NYU Shanghai and concurrently a Global Network Assistant Professor within the broader New York University system. Since January 2019 he has led research and teaching activities at the Division of Engineering and Computer Science in Shanghai, while maintaining university-wide collaborations through his Global Network appointment. Education Ph.D. in Transportation Engineering, University of Florida (2017) Research Interests Dr. Chen’s scholarship centres on Transportation Network Modeling and Optimization , Intelligent Transportation Systems , and Discrete Optimization . He integrates operations research, data science, and engineering to address emerging challenges in electric mobility, autonomous vehicles, and large-scale urban networks. Recent thrusts include: Data-driven analytics of electric-vehicle charging behaviour under usage heterogeneity. Optimization of charging and swapping infrastructure for electric buses and trucks. Network-level deployment and control strategies for connected and automated vehicles. Day-to-day traffic dynamics and equilibrium models with elastic demand. Pricing, policy, and incentive design for sustainable transportation systems. Scientific Awards Stella Dafermos Best Paper Award – awarded at the 95th Transportation Research Board Annual Meeting. Ryuichi Kitamura Paper Award – also conferred at the 95th TRB Annual Meeting. Editorial & Professional Service Dr. Chen currently serves on the Editorial Advisory Board of Transportation Research Part C: Emerging Technologies , shaping the editorial direction of the leading journal in his field. Grants & Collaborations While specific grant identifiers are not disclosed in the provided text, Dr. Chen’s extensive publication record in top-tier journals ( Transportation Science , Transportation Research Parts B, C, D , IEEE ITS , Applied Energy ) and his editorial role indicate sustained research funding and active collaboration with international partners across North America and China. Laboratories & Teams Operating within the Division of Engineering and Computer Science at NYU Shanghai , Dr. Chen leads a research group focused on next-generation mobility analytics, leveraging the university’s interdisciplinary ecosystem and NYU’s Global Network resources to advance smart and sustainable transportation.

Mariana LaverdeView profile

Assistant Professor

• Applied Market Design
• Education Markets
• Economic Education
• +3 more

Mariana Laverde is an Assistant Professor of Economics at Boston College. She holds a Ph.D. from the University of Chicago and completed a postdoctoral position at Yale University’s Tobin Center. Her research focuses on applied market design with an emphasis on education markets, including topics like teacher market mechanisms, school choice systems, and spatial equity in education access. Education background: Ph.D. in Economics (University of Chicago, 2019), Master’s in Economics, and BSc in Mathematics (Universidad de los Andes, Colombia). Her work also extends to urban economics (e.g., housing price indices in Bogotá) and financial systems (systemic risk analysis in Colombia). Research highlights include analyzing two-sided teacher markets, evaluating school choice policies, and developing hedonic pricing methodologies. Her recent work (2022–2025) emphasizes education policy design and market efficiency. Earlier studies addressed banking regulation and regional development impacts of mining in Colombia. No scientific awards are explicitly listed, though her publications reflect impactful contributions to applied economics. She advises no students listed here and has no grants mentioned. Her work is affiliated with Boston College’s Economics Department and accessible via RePEc/Google Scholar.

Andreas S. SchulzView profile

Professor

• Mathematical Optimization
• Algorithm Design
• Operations Research
• +5 more

Prof. Dr. Andreas S. Schulz is a faculty member at Technische Universität München (TUM), holding a chair in the Department of Mathematics and the Department of Business and Economics. He previously served as the Patrick J. McGovern Chair of Management and Professor of Mathematics at MIT. His research focuses on mathematical optimization, algorithm design, and their applications in logistics, production, healthcare systems, and online advertising. He has held visiting professorships at institutions such as the Sauder School of Business (UBC) and ETH Zurich. Prof. Schulz’s research bridges operations research, theoretical computer science, and economics. He develops analytical methods to solve complex decision-making problems in business, including scheduling, resource allocation, and network optimization. A key interest is applying mathematical approaches to enhance healthcare delivery and system efficiency. Education: PhD in Operations Research (MIT), prior academic roles at MIT and visiting institutions. Key Achievements: Alexander von Humboldt Professorship (2014), Humboldt Research Award (2010), Glover-Klingman Prize (2006). Research Themes: Robust optimization, approximation algorithms, scheduling theory, and algorithmic game theory. His publications span topics like integer programming, optimal transport, and congestion games. He collaborates across disciplines, emphasizing practical applications of theoretical insights.

Ryan MurrayView profile

Assistant Professor

• Calculus of variations
• Partial Differential Equations
• Machine Learning
• +5 more

Ryan Murray is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State). His research focuses on developing mathematical tools to address problems in applied analysis, including calculus of variations, partial differential equations (PDEs), and their applications to machine learning, fluid dynamics, and control theory. He holds a PhD in Mathematics from Carnegie Mellon University (2016). His expertise spans regularization methods for machine learning, singular perturbations in materials science, algorithms for distributed optimization, and singularity formation in fluid dynamics. His work is supported by the National Science Foundation (NSF) and the Simons Foundation. He actively collaborates with researchers in data science, PDE analysis, and optimization. Key research areas include adversarial training in classification, geometric data analysis via statistical depths, and the analysis of vortex sheet singularities. His teaching experience includes courses on partial differential equations, optimal control theory, and linear control systems. Ryan has published extensively in journals such as SIAM Journal on Mathematics of Data Science , Archive for Rational Mechanics and Analysis , and Journal of Machine Learning Research . His articles explore topics ranging from graph-based learning to fluid dynamics instabilities.

Georgy ChabakauriView profile

Associate Professor

• Asset Pricing
• Portfolio Choice
• Risk Management
• +2 more

Dr. Georgy Chabakauri is an Associate Professor in the Department of Finance at the London School of Economics and Political Science. With a PhD in Finance from London Business School and a PhD in Mathematics from Moscow State University, his research focuses on asset pricing under frictions, portfolio choice, and risk management. His primary research interests include: Dynamic equilibrium models with heterogeneous investors Effects of portfolio constraints on asset prices Collateral requirements in financial markets Information asymmetry in multi-asset environments Mean-variance optimization techniques Chabakauri's publications demonstrate consistent focus on theoretical asset pricing, with recent work exploring informational efficiency, index investing effects, and cross-sectional return drivers. His articles frequently incorporate equilibrium modeling and empirical validation across different market conditions. Awards and honors: European Finance Association Best Conference Paper (2016) SFS Finance Cavalcade Best Paper in Asset Pricing JEDC Outstanding Referee Award Russian Academy of Sciences Medal for Best Student Paper (2001) He teaches core finance courses including Corporate Finance, Investments, and Asset Markets at both undergraduate and graduate levels, and serves as reviewer for leading finance journals.

Tianyi LinView profile

Assistant Professor

• Optimization
• Game Theory
• Machine Learning
• +3 more

Tianyi Lin serves as an Assistant Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia Engineering, Columbia University, a position he assumed in 2024. He holds dual affiliations as a verified Data Science Institute (DSI) Member and an Affiliated Member of both the Financial and Business Analytics Center and the Foundations of Data Science Center. His academic credentials include: Ph.D. in Electrical Engineering and Computer Science, UC Berkeley Postdoctoral Researcher, Laboratory for Information & Decision Systems (LIDS), MIT (2023-2024) M.S. in Operations Research, UC Berkeley M.S. in Pure Mathematics and Statistics, University of Cambridge B.S. in Mathematics, Nanjing University Dr. Lin's research spans optimization theory , game-theoretic models , and machine learning algorithms , with emphasis on nonconvex minimax problems , variational inequalities , and data science applications . His work bridges theoretical guarantees with practical implementations in high-dimensional settings, particularly focusing on convergence properties and computational efficiency in complex systems. Analysis of his 15 most recent publications (2022-2025) reveals dominant themes in high-order optimization methods , no-regret learning in games , and optimal transport algorithms . His contributions demonstrate consistent innovation in developing doubly optimal algorithms for monotone games, spectral regularization techniques for policy optimization, and structure-driven approaches for nonconvex problems, reflecting strong interdisciplinary connections between operations research, computer science, and applied mathematics. No scientific awards or honors were documented in the provided source material. Information regarding student advising and research grants remains unspecified in the current documentation, though his center affiliations suggest active participation in collaborative research initiatives. Dr. Lin maintains significant interdisciplinary engagement through his affiliations with Columbia's Data Science Institute and specialized research centers, positioning his work at the intersection of theoretical optimization and real-world data science applications.

Ralph BaileyView profile

Associate Professor

• Quantitative Methods
• Philosophy of Research and Enquiry

Ralph Bailey is an Associate Professor in Economics at the University of Birmingham , affiliated with the Birmingham Business School and Department of Economics . His work bridges quantitative methods and philosophy of research and enquiry , with methodological contributions to econometrics and mathematical economics. Education : BSc (Durham), MA (Lancaster), MSocSc (Birmingham) His publications focus on statistical modeling, time series analysis, and theoretical economics, including works on deunionization impacts , ARMA processes , and public good provision under ambiguity . Recent studies explore earnings dispersion, near-integration in regression, and macroeconomic sustainability frameworks. Contact : r.w.bailey@bham.ac.uk

Huan LeiView profile

Assistant Professor

• Computational Mathematics
• Numerical Analysis
• Scientific Machine Learning
• +4 more

Huan Lei is an Assistant Professor at Michigan State University, holding a joint appointment in the Department of Computational Mathematics, Science and Engineering and the Department of Statistics and Probability. He earned his Ph.D. in Applied Mathematics from Brown University in 2012 under George Karniadakis and a B.S. in Special Class for the Gifted Young from the University of Science & Technology of China in 2005. His research integrates scientific machine learning with numerical analysis to develop structure-preserving algorithms for partial and stochastic differential equations arising in multi-scale systems. His work spans multi-scale modeling , non-Markovian dynamics , coarse-grained molecular simulations , and data-driven parameterization . Recent publications focus on learning generalized Langevin equations with state-dependent memory, consensus-based free energy surfaces, and non-equilibrium coarse-grained models. His team applies these methods to fluid dynamics, biomolecular solvation, and climate systems. NSF CAREER Award (2021) Brown University Dissertation Fellowship (2012) He advises graduate and undergraduate researchers and seeks Ph.D. candidates with expertise in numerical analysis or scientific computing. His group receives funding from NSF, DOE, Ford, and MSU Foundation.

Marcelo Zouain PedroniView profile

Associate Professor

• Macroeconomics
• Public Finance
• Public Economics
• +6 more

Marcelo Zouain Pedroni serves as an Associate Professor of Economics at the University of Amsterdam and a Research Fellow at the Tinbergen Institute, focusing on macroeconomic theory and policy applications. His academic credentials include an MSc in Economics from the Getulio Vargas Foundation (EPGE) and a PhD from the University of Minnesota. Dr. Pedroni's research spans Public Finance , Information Economics , Environmental Economics , and Risk and Uncertainty , with particular emphasis on how dispersed information and ambiguity aversion shape macroeconomic outcomes. His work bridges theoretical modeling with empirical policy relevance, especially in fiscal and monetary contexts. Recent publications reveal a consistent trajectory in high-impact journals like the American Economic Review and Review of Economic Studies , demonstrating expertise in dynamic information aggregation, optimal taxation under uncertainty, and strategic interactions in macroeconomic models. These contributions address fundamental questions about learning from market signals and designing efficient redistribution mechanisms. Through his Tinbergen Institute affiliation, he participates in Europe's premier economics research network, contributing to collaborative projects in economic theory and policy analysis.

• Dark Matter
• Particle Astrophysics
• Cosmology
• +2 more

Pearl Sandick is a Professor in the Department of Physics and Astronomy and Interim Dean in the College of Science at the University of Utah. She has previously served as Associate Chair of the Department of Physics and Astronomy and Associate Dean for Faculty and Research in the College of Science. Her academic journey at the University of Utah began in 2011 as an Assistant Professor, progressing to Associate Professor in 2017, and achieving the rank of Professor in 2022. Her educational background includes: BA in Mathematics from New York University (2003) PhD in Physics from the University of Minnesota (2008) Sandick is a theoretical particle physicist whose research focuses on physics beyond the Standard Model, with particular emphasis on dark matter. Her work spans theoretical modeling, connections to astrophysical observations, and implications for experimental detection. She investigates various dark matter candidates and their potential signatures in current and future experiments, including collider searches, direct detection experiments, and indirect detection through astrophysical observations. Her research also extends to connections between particle physics and cosmology, including early universe phenomena and implications for cosmic structure formation. She has developed computational tools like MADHAT for dark matter analysis and has made significant contributions to understanding how stellar evolution can constrain axion physics. Her scholarly contributions have been recognized with several prestigious awards: University of Utah Early Career Teaching Award (2016) University of Utah Distinguished Mentor Award Linda K. Amos Award for Distinguished Service to Women University of Utah Presidential Scholar Sandick has been actively involved in mentoring graduate students, as evidenced by her teaching of PhD thesis research and Master's research courses. She has secured significant research funding from the National Science Foundation and other agencies to support her work on dark matter, dark energy, and new physics. Her grant portfolio includes projects on theoretical particle physics, connections to astrophysical observations, and studies on graduate education reform following a departmental tragedy. She is an active member of the American Physical Society, having served as Chair of the regional Four Corners Section in 2021-2022, demonstrating her commitment to the broader physics community and leadership in her field.

Holger SiegView profile

Professor

• Urban Economics
• Fiscal Policy
• Public Finance
• +5 more

Holger Sieg is the Baird Term Professor of Economics at the University of Pennsylvania's Department of Economics. His research focuses on urban economics, fiscal policy, public finance, education economics, and spatial economics. He has authored a widely adopted textbook, 'Urban Economics and Fiscal Policy,' integrating theoretical and empirical analyses for graduate and advanced undergraduate students. Teaching includes Econ 4470 (undergraduate urban economics) and Econ 8400 (Ph.D. seminar on urban and spatial economics). His work emphasizes policy analysis, particularly in migration controls, fiscal decentralization, and educational equity. Key research themes include electoral accountability, municipal pension funding, and the impact of local policies on human capital accumulation. Recent publications analyze topics such as time allocation models, electoral competition dynamics, and the effects of migration policies in China. His research bridges empirical and theoretical approaches, addressing contemporary issues like urban fiscal policies and access to education.

• Marketing
• Digital Platforms
• Consumer Behavior
• +4 more

Professor Jessica Fong is an Assistant Professor of Marketing at the University of Michigan Ross School of Business. Her work focuses on applying theoretical models and experimentation to analyze marketing data, with an emphasis on digital platform design, consumer behavior, and information economics. She holds a PhD from Stanford University and a BS in Computer Science from Caltech, and previously worked in product management at Fitbit. Education: PhD in Marketing, Stanford University (2019) BS in Computer Science, California Institute of Technology (2014) Her research interests include two-sided markets, platform mergers, consumer responses to misinformation, and the intersection of neuroscience with consumer choice. Her work has been published in top journals like Marketing Science and Management Science , and recognized with awards such as the Paul E. Green/Vithala R. Rao Award Finalist (2024). Recent articles highlight her exploration of network effects in platform mergers, the impact of truth disclosure in consumer finance, and the role of negative feedback in social media content generation. She actively contributes to academic discourse through blog posts, podcasts, and media engagements. Awards: Paul E. Green/Vithala R. Rao Award Finalist (2024) Her research bridges empirical analysis with practical implications for firms, addressing topics like suggested pricing strategies, product ranking effects, and pandemic-era social media usage patterns. She collaborates with interdisciplinary teams, integrating neuroscience insights into consumer behavior models.