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Alexander Schwing is an Associate Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research focuses on machine learning and computer vision with applications in 3D scene understanding, generative modeling, and multi-agent systems. Education: Diploma in Electrical Engineering and Information Technology, Technical University of Munich (TUM) PhD in Computer Science, ETH Zurich Postdoctoral Fellow, University of Toronto Research Interests: Structured prediction in deep learning Generative adversarial networks and stability Multi-modal vision-language models 3D scene reconstruction from single images Embodied agent collaboration Semantic segmentation with temporal coherence Recent Publications: Highlight trends in neural rendering, video object segmentation, and reinforcement learning with applications to 3D modeling and multi-agent systems. Notable innovations include SAIL-VOS dataset for amodal segmentation and NeRFDeformer for single-view scene transformation. Scientific Awards: NSF CAREER Award, 3M and Amazon research awards, multiple student recognition awards, ETH Zurich PhD medal, and best paper at Intelligent Tutoring Systems 2014. Teaching: Offers graduate courses in Pattern Recognition (ECE 544) and Machine Learning (CS 446/ECE 449). Previously taught at University of Toronto and ETH Zurich. Labs & Collaborations: Leads research at Coordinated Science Laboratory (UIUC) with collaborations across University of Toronto, ETH Zurich, and industry partners like Samsung SAIT and Amazon.

• Machine Learning
• Computer Vision
• Structured Prediction
• +7 more

Zaid Harchaoui is an Adjunct Professor in the Department of Statistics at the University of Washington. His research focuses on machine learning, generative AI, and algorithmic optimization, with applications spanning ecology, neuroscience, and artificial intelligence. He explores learning under distributional shifts and develops tools for scalable generative models in language and vision domains. University: University of Washington Department: Statistics Research Focus: Learning from data with computational, inferential, and mathematical rigor; distributional shift adaptation; generative model scaling Email: zaid@uw.edu His recent work emphasizes generative AI applications in ecology and neuroscience, stochastic optimization for robustness, and algorithmic efficiency in large-scale learning. Key contributions include techniques for distributionally robust optimization, interpretable authorship obfuscation, and uncertainty quantification in behavior classification. Scientific awards and honors are not explicitly mentioned in the provided text. Collaborative efforts often intersect with nonlinear control algorithms, spectral analysis, and privacy-preserving machine learning frameworks.

• Machine Learning
• Artificial Intelligence
• Algorithms
• +3 more

David Alvarez-Melis is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He leads the Data-Centric Machine Learning (DCML) group and holds affiliations with the Kempner Institute, Harvard Data Science Initiative, and the Center for Research on Computation and Society. His research focuses on making machine learning more data-efficient and trustworthy, with applications in natural and medical sciences. He also serves as a researcher at Microsoft Research New England. Affiliations: SEAS, Kempner Institute, Harvard Data Science Initiative, CRCS Education: PhD in Computer Science (MIT), MS in Mathematics (NYU Courant), BSc in Applied Mathematics (ITAM) Research Interests: Optimal Transport, dataset distillation, interpretable AI, medical imaging, robustness, and large language models. His work bridges theory and applications, emphasizing geometric and probabilistic methods. Recent Trends in Publications: Focused on advancing optimal transport for data manipulation, distributional deep equilibrium models, and repurposing LLMs for specialized domains. Key themes include synthetic dataset generation, gradient flows in probability spaces, and robust interpretability frameworks. Awards: Aramont Fellowship, Dean’s Competitive Fund, Top Reviewer awards at major conferences (ICLR, NeurIPS, ICML). Grants: Supported by the Aramont Fund and Harvard’s Dean’s Fund. His lab advises students across Harvard and MIT, with notable contributions to medical imaging, NLP, and foundational ML theory. He actively mentors interns and fosters collaborations with industry and academia.

• Machine Learning
• Artificial Intelligence
• Optimal Transport
• +4 more

Eric V. Mazumdar is an Assistant Professor at the California Institute of Technology (Caltech), jointly appointed in the departments of Computing and Mathematical Sciences and Economics. He holds a B.S. from MIT (2015) and a Ph.D. from UC Berkeley (2021), co-advised by Michael Jordan and Shankar Sastry. His research bridges machine learning and economics, focusing on deploying algorithms into societal systems through theoretical and practical lenses. Key areas include strategic classification, multi-agent reinforcement learning, and distributionally robust optimization, with applications in healthcare, online markets, and intelligent infrastructure. Education: B.S., Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2015 Ph.D., Electrical Engineering and Computer Science, University of California, Berkeley, 2021 Research Interests: Mazumdar’s work emphasizes understanding learning algorithms in strategic environments, including min-max optimization, game theory, and multi-agent systems. He explores how algorithms interact with human and algorithmic agents in dynamic systems, with practical applications in healthcare delivery, e-commerce, and autonomous systems testing. Awards: NSF CAREER Award (2023) Simons Institute Research Fellowship in Learning in Games Grants & Funding: Supported by NSF, DARPA, Amazon, and other organizations. His NSF CAREER grant focuses on strategic interactions in societal-scale systems. Teaching: Courses include Networks: Structure & Economics (CMS/CS/EE/IDS 144) and Topics in Learning and Games (CMS/Ec 248). At UC Berkeley, he contributed to courses like Data, Inference, and Decisions (DS 102). Students & Postdocs: Current students: Lauren Conger, Tinashe Handina, Yizhou Zhang Postdocs: Zaiwei Chen, Laixi Shi, Kishan Panaganti (co-advised with Adam Wierman)

• Machine Learning
• Economics
• Optimization
• +4 more

Mikaela Iacobelli is an Associate Professor in the Department of Mathematics at ETH Zürich. During the 2024-25 academic year, she was a von Neumann Fellow at the Institute for Advanced Study in Princeton. Previously, she held faculty positions at Durham University and a research fellowship at the University of Cambridge. Her educational background includes: PhD in Mathematics from Sapienza University of Rome and École Polytechnique in Paris (2015) Master's degree from Sapienza University of Rome (2012) Bachelor's degree from Sapienza University of Rome (2009) Mikaela's research lies at the interface of analysis, kinetic theory, and statistical mechanics. She studies partial differential equations that model the collective behavior of many-particle systems, with a focus on Vlasov-type plasmas and gravitational dynamics. Her current projects range from quasineutral and singular-limit problems for Vlasov-type systems to quantization of measures, ultrafast diffusion, and gradient-flow structures that link microscopic particle models to macroscopic fluid descriptions. She makes extensive use of PDEs techniques, optimal transport, probability, calculus of variations, and Riemannian geometry in her work. Her recent publications demonstrate a strong focus on Vlasov-type equations, particularly examining quasineutral limits, stability properties, and connections to other physical systems like Euler equations and magnetohydrodynamics. She has made significant contributions to understanding Landau damping, quantization problems on manifolds, and the mathematical foundations of plasma physics. Her notable scientific awards include: SNSF Starting Grant (Swiss ERC) Challenges and Breakthroughs in the Mathematics of Plasmas (2025-2030) von Neumann Fellow at the Institute for Advanced Study, Princeton (2024-2025) Invited speaker at the International Congress of Mathematical Physics (2021) CO-PI of the Germaine de Staël Funding Program for French-Swiss cooperation (2021-2023) L'Oréal prize for Women in Science (2015) Mikaela actively mentors postdocs, PhD, Master's, and Bachelor's students. Her current mentees include postdocs Dennis Chemnitz, Rishabh Gvalani, Stefano Rossi, and Simon Becker, as well as PhD students Thérèse Moerschell, Ata Deniz Aydin, and Antoine Gagnebin. She has served as PI for the Starting Research Grant from the University of Rome Sapienza and is currently the PI for the SNSF Starting Grant. She co-organizes several academic seminars including the Zurich Colloquium in Mathematics, the PDE and Mathematical Physics seminar at ETH Zürich and UZH, and the Analysis Seminar. She also serves on various committees including as Chair of the European Mathematical Society Committee for Women in Mathematics.

• Analysis
• Kinetic theory
• Statistical mechanics
• +7 more

Karl-Theodor Sturm is a Professor of Mathematics at the University of Bonn, holding this position since 1997. He is affiliated with the Institute for Applied Mathematics and leads the Cluster of Excellence Hausdorff Center for Mathematics. His academic journey includes a PhD (1989) and habilitation (1993) from the University of Erlangen-Nürnberg, followed by postdoctoral positions at Zurich, Erlangen-Nürnberg, and the Max Planck Institute for Mathematics in the Sciences (MPI Leipzig). He has held visiting professorships at Stanford, Toulouse, Paris, and Bonn. Sturm's research focuses on stochastic analysis and geometric analysis, particularly in optimal transport, metric measure spaces, synthetic curvature bounds, and diffusion processes. His work on synthetic Ricci curvature bounds, developed in competition with Cédric Villani, has been highly influential. He received the ERC Advanced Grant (2016-2022) for research on metric measure spaces and Ricci curvature, and was a Plenary Speaker at the 2020 European Congress of Mathematics. His leadership roles include Vice Chairman of Collaborative Research Center SFB 611 (2002–2012), Managing Director of the Institute for Applied Mathematics (2007–2010), and Coordinator of the Hausdorff Center for Mathematics (2012–2019). Awards include the Heisenberg Fellowship (1994) and recognition through numerous invited lectures and editorial roles. His mentorship has shaped the careers of prominent researchers such as Nicola Gigli and Jan Maas.

• Optimal transport
• Metric measure spaces
• Probability theory
• +4 more

Prof. Dr. Martin Burger is a leading scientist at DESY and a Full Professor in the Department of Mathematics at Universität Hamburg, where he leads the Computational Imaging Group. His research bridges applied mathematics, imaging sciences, and machine learning, with a focus on inverse problems, mathematical modeling, and partial differential equations. He has held professorial positions at Universität Münster and FAU Erlangen-Nürnberg prior to his current dual appointment. Full Professor, Universität Hamburg (2023–present) Leading Scientist, DESY, Hamburg (2023–present) Full Professor, FAU Erlangen-Nürnberg (2018–2023) Full Professor, Universität Münster (2006–2018) His research interests include inverse problems, variational regularization, optimal transport, kinetic models, and mathematical modeling in biology and social sciences. He has made significant contributions to imaging reconstruction, sparse neural networks, and the analysis of transformer architectures. His work often integrates theoretical analysis with computational methods, influencing both pure and applied mathematics. The most recent articles reflect a strong trend toward interdisciplinary applications, combining deep learning with PDE-based modeling, analyzing social and biological systems via kinetic and mean-field models, and advancing mathematical imaging through graph-based and optimal transport methods. His publications span high-impact venues in applied mathematics and computational science. Calderon Prize, Inverse Problems International Association (IPIA) ERC Consolidator Grant (2014) Invited speaker at ECM (2021), ICM (2022), and ICIAM (2023) Editor-in-Chief, European Journal of Applied Mathematics (since 2017) Prof. Burger has supervised numerous PhD students and postdoctoral researchers, many of whom appear as co-authors in his publications. His research is supported by major grants, including funding from the German Federal Ministry of Education and Research (BMBF). He is actively involved in collaborative projects across mathematics, physics, and engineering disciplines. He leads the Computational Imaging Group at DESY, fostering a collaborative environment for developing novel mathematical tools in imaging science. The group works on both theoretical foundations and practical implementations, contributing to advancements in tomography, machine learning, and data analysis.

• Inverse Problems
• Applied Mathematics
• Mathematical Modelling
• +15 more

Qin Li is an Associate Professor in the Mathematics Department at the University of Wisconsin-Madison. She holds affiliations with the Wisconsin Institutes for Discovery and serves as a senior PI at the Institute for Foundations of Data Science. Her research focuses on numerical analysis, scientific computing, and inverse problems, with a strong emphasis on kinetic theory and multiscale PDEs. Her work spans computational methods for inverse transport and radiative transfer equations, Bayesian approaches in optical tomography, and optimization techniques for solving stochastic and deterministic PDEs. Recent publications highlight applications of diffusion models, Wasserstein gradient flow, and random sampling in inverse problems, as well as control theory for Vlasov-Poisson systems and reconstruction of chemotaxis kernels. She leads a research group within the Mathematics Department and has received funding from the National Science Foundation (NSF), the Office of Naval Research (ONR), and the Wisconsin Alumni Research Foundation (WARF). Her lab, Kinetic At Madison, explores nonlinear hyperbolic PDEs and their applications. She also contributes to teaching as a TA Supervisor.

• Numerical Analysis
• Scientific Computing
• Inverse Problems
• +8 more

Marco Cuturi is a Research Scientist at Apple ML Research in Paris and Professor of Statistics at CREST-ENSAE, Institut Polytechnique de Paris. His work bridges machine learning , optimal transport , and optimization , with applications in time-series analysis , kernels , and multiresolution methods . He has held academic roles at Kyoto University and Princeton University, and previously worked in the financial industry. Research Interests: Optimal transport theory and computational methods Kernel design for structured data and histograms Time-series alignment and soft-DTW Entropic regularization in optimization Applications to computer vision and genomics Teaching: Cuturi has taught courses on linear optimization at Princeton, geometric methods in machine learning at Kyoto, and scientific English. He has also organized machine learning summer schools in Kyoto, Les Houches, and other international venues. Recent Trends: His 2024-2025 publications focus on entropic optimal transport solvers, disentangled representation learning via Gromov-Monge gaps, and applications to text-to-image diffusion models. Collaborative work with institutions like Google Research, MIT, and University of Tokyo highlights his interdisciplinary impact.

• Machine Learning
• Optimal Transport
• Optimization
• +4 more

Prof. Jalal Etesami is an Assistant Professor in the Department of Computer Science at Technical University of Munich (TUM), leading the Decision Sciences & Systems group. He holds a Ph.D. in Industrial and Systems Engineering from the University of Illinois at Urbana-Champaign and was a Postdoctoral Fellow at EPFL in Switzerland. His research focuses on machine learning, causal inference, multi-agent systems, and game theory, with applications to systemic risk modeling and market design. He teaches advanced courses such as Causal Inference in Time Series , Algorithmic Game Theory , and Optimization, Learning, and Market Design . Notable contributions include work on causal structure learning, stochastic optimization, and non-Gaussian causal models. Recent research explores causal effect identification under confounding, neural networks for market analysis, and optimal experiment design. Prof. Etesami’s work appears in top venues like NeurIPS, AAAI, and IEEE journals. He actively contributes to the academic community, organizing seminars and workshops on topics ranging from causal reasoning to computational social choice.

• Machine Learning
• Statistical Decision Making
• Causal Inference
• +4 more

Fabio Cavalletti is a Full Professor at the University of Milan, specializing in geometric analysis, optimal transport theory, and synthetic curvature bounds. His work bridges differential geometry, mathematical physics, and functional inequalities, with particular focus on Lorentzian spaces and Ricci curvature. He has organized international workshops and conferences on optimal transport and geometric analysis. Research interests include optimal transport in Lorentzian and Riemannian settings, synthetic Ricci curvature conditions, isoperimetric inequalities, and geometric flows. Recent work explores timelike Ricci curvature bounds, null hypersurfaces in general relativity, and applications to quantum information geometry. Key contributions include foundational results on displacement convexity, quantitative Obata's theorem, and optimal transport in singular spaces. He has collaborated extensively with researchers like Andrea Mondino and Nicola Gigli, producing over 35 peer-reviewed publications. Notable activities include organizing the 2024 School and Conference on Metric Measure Spaces, 2025 Workshop on Optimal Transport and Metric Geometry, and mentoring junior researchers in geometric analysis. Current projects address synthetic timelike curvature in Lorentzian spaces and applications to general relativity.

• Optimal Transport
• Geometric Analysis
• Ricci Curvature
• +3 more

Yuan Gao is an Assistant Professor of Mathematics at Purdue University's Department of Mathematics (College of Science). His research focuses on analysis and computations of PDEs in materials science, biology, and microfluidics, with recent emphasis on optimal control, Hamilton-Jacobi equations, and non-equilibrium chemical reactions. His work is supported by NSF awards DMS-2204288 and DMS-2440651. Previously, he held the William W. Elliott Assistant Research Professor position at Duke University (2019-2021). Research interests include PDE analysis in materials science (crystal growth, dislocation dynamics), numerical methods for interface dynamics, applied stochastic analysis (Langevin dynamics, transition path theory), and mean-field games for fluid systems. He organizes the PSU-Purdue-UMD Joint Seminar on Mathematical Data Science. Key publications span topics like dislocation evolution, Wasserstein gradient flows, and stochastic algorithms for rare events. Awards include NSF CAREER funding recognizing his contributions to mathematical analysis of non-equilibrium systems.

• Partial Differential Equations
• Computational and Applied Mathematics
• Stochastic Analysis
• +7 more

Jia-Jie Zhu is a machine learner and applied mathematician currently serving as head of an independent research group at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin, with an upcoming appointment as tenured associate professor at KTH Royal Institute of Technology in Stockholm. Previously, he conducted postdoctoral research in machine learning at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, following doctoral studies in optimization and numerical analysis at the University of Florida. His research focuses on the mathematical foundations of machine learning and optimization, particularly at the intersection of computational algorithms, dynamical systems, and probability theory. Key areas include: Robust probabilistic machine learning algorithms Kernel methods for distribution manipulation Variational methods for optimization over probability distributions Gradient flows and optimal transport theory Wasserstein and Fisher-Rao geometry Applications in generative modeling and causal inference Dr. Zhu's recent work reveals deep connections between partial differential equations, kernel methods, and machine learning, resulting in theoretically grounded algorithms for handling distribution shifts. His publications demonstrate a consistent trajectory from foundational optimization theory to cutting-edge applications in robust learning and generative modeling, with increasing emphasis on the mathematical structures underlying modern ML systems. He has secured significant research funding, including a DFG Project on 'Optimal Transport and Measure Optimization Foundation for Robust and Causal Machine Learning' within the Priority Program 'Theoretical Foundations of Deep Learning' (SPP 2298), and actively organizes academic events such as the Workshop on Optimal Transport from Theory to Applications (OT-DOM) and upcoming sessions at ICSP 2025 and SwissMAP. As an educator, Dr. Zhu teaches nonparametric statistics at Humboldt University of Berlin and serves as area chair for major conferences including AISTATS 2025. He maintains an active research group with opportunities for master's students, PhD candidates, and postdoctoral researchers interested in the mathematical frontiers of machine learning.

• Machine Learning
• Optimization
• Optimal Transport
• +5 more

Max Fathi is a Professor of Mathematics at Université Paris Cité, affiliated with the Laboratoire Jacques-Louis Lions (LJLL) and Laboratoire de Probabilités, Statistique et Modélisation (LPSM). He concurrently holds a part-time teaching position at the Department of Mathematics and Applications (DMA) at École Normale Supérieure (ENS). Since 2023, he has been a member of the Institut Universitaire de France (IUF), a prestigious national research fellowship in France. He completed his PhD in 2013 at Université Pierre et Marie Curie under Cédric Villani, followed by a postdoctoral position at the University of California, Berkeley with Lawrence C. Evans and Fraydoun Rezakhanlou. Previously, he was a CNRS researcher at the Institut de Mathématiques de Toulouse before joining Université Paris Cité. His habilitation thesis (2019) focuses on optimal transport applications in analysis and probability. Fathi's research centers on optimal transport theory, particularly its applications to analysis, probability, and statistical physics. Key topics include interacting particle systems, functional inequalities (e.g., Poincaré, log-Sobolev), high-dimensional phenomena, Ricci curvature in discrete/continuous spaces, Stein's method, concentration of measure, and numerical methods for stochastic dynamics. His work is supported by the ANR project 'Conviviality.' He has delivered courses on functional analysis at ENS and participated in summer schools, including an MSRI course on functional inequalities and localization techniques. His teaching materials include lecture notes on optimal transport and stochastic processes. His contributions have been recognized through awards such as the IUF membership. Notable research collaborations include work with Thomas Courtade, Matthias Erbar, and Gabriel Stoltz on topics ranging from stability estimates of inequalities to hypocoercivity and numerical analysis of stochastic systems.

• Optimal Transport
• Probability Theory
• Statistical Physics
• +9 more

Anna Korba is an Assistant Professor at École Polytechnique, specifically affiliated with ENSAE/CREST in the Statistics Department since September 2020. She is also a co-administrator of the Master Data Science program at École Polytechnique. Her academic journey has positioned her as a leading researcher in machine learning, with particular expertise in kernel methods, optimal transport, and statistical optimization. Dr. Korba received her PhD from Telecom ParisTech in 2018 under the supervision of Prof. Stephan Clémençon. Prior to her current position, she was a postdoctoral researcher at University College London's Gatsby Computational Neuroscience Unit working with Arthur Gretton from December 2018 to August 2020. Her academic foundation includes a Master's degree in Machine Learning and Computer Vision (MVA) from ENS Cachan and ENSAE in 2015. Anna Korba's research primarily focuses on machine learning with emphasis on kernel methods, optimal transport, optimization, particle systems, and preference learning. Her work bridges theoretical statistics with practical machine learning applications, particularly in developing novel sampling and optimization methods. She has made significant contributions to understanding Wasserstein gradient flows, density ratio estimation, and variational inference techniques. Her publication record demonstrates a strong trajectory in top-tier machine learning conferences including ICML, NeurIPS, AISTATS, and ICLR. Her research shows a clear evolution from foundational work on ranking and preference learning during her PhD to more recent contributions in Wasserstein-based optimization, sampling methods, and deep probabilistic modeling. The interdisciplinary nature of her work connects statistics, optimization theory, and practical machine learning applications. Top 10% Oral Presentation at AISTATS 2022 Top 15% Long Oral Presentation at ICML 2021 She actively mentors PhD students and postdoctoral researchers, currently advising seven PhD candidates and having successfully guided several alumni to prestigious positions. Dr. Korba also contributes to the academic community through her role in administering the Master Data Science program and collaborating with researchers across institutions worldwide. As part of the CREST research center, Dr. Korba works within a vibrant team of researchers focused on statistics, machine learning, and their applications to economic and social sciences. Her research group includes current PhD students and postdocs working on various aspects of her research interests, creating a dynamic environment for advancing the field of statistical machine learning.

• Machine Learning
• Nonparametric Statistics
• Kernel Methods
• +5 more