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Marcello CarioniView profile

Assistant Professor

• Inverse Problems
• Optimal Transport
• Regularization Techniques
• +3 more

Marcello Carioni is an Assistant Professor affiliated with the Digital Society Institute, TechMed Centre, and Mathematics of Imaging & AI. His research focuses on inverse problems, optimal transport, regularization techniques, and mathematical analysis with applications in machine learning and imaging. He has published extensively, including 35 research outputs since 2016, with a notable h-index of 23 and 228 citations. His work bridges theoretical mathematics and practical applications, addressing challenges in sparsity recovery, dynamic systems, and convex optimization. Recent contributions include advancements in optimal transport regularization, sparse optimization frameworks, and neural network representations in infinitely wide regimes. Collaborations span international teams, reflecting his interdisciplinary approach to mathematical challenges. Carioni has delivered invited talks on topics like dynamic inverse problems and optimal transport methods, showcasing his engagement with both academic and applied communities. His research emphasizes methodological innovation, with a focus on solving complex problems in imaging and data science through rigorous mathematical foundations.

Amos LawlessView profile

Professor

• Data Assimilation
• Numerical Weather Prediction
• Meteorology
• +4 more

Professor Amos Lawless is a leading academic in the Department of Meteorology at the University of Reading, specializing in data assimilation and inverse problems. His research focuses on advancing numerical methods for meteorological and oceanographic modeling, particularly in variational data assimilation, error covariance analysis, and coupled atmosphere-ocean systems. He holds a Professorship position and contributes to the development of algorithms for improving forecasting accuracy in numerical weather prediction (NWP) systems. His work integrates computational mathematics, environmental science, and applied statistics to address challenges in climate modeling and predictive systems. Key research themes include the conditioning of data assimilation problems, model bias correction, and the application of optimization techniques to enhance forecast precision. His publications span prestigious journals like the Quarterly Journal of the Royal Meteorological Society and SIAM Journal on Applied Dynamical Systems, reflecting his expertise in both theoretical and applied aspects of data assimilation. Collaborations involve institutions such as the Met Office and international research groups, aiming to bridge the gap between computational methods and real-world environmental modeling challenges. Recent work emphasizes hybrid data assimilation approaches, cross-domain error correlations in coupled systems, and the use of randomized preconditioning to tackle large-scale computational problems. His contributions are pivotal in advancing the mathematical foundations of environmental forecasting systems, ensuring robustness and efficiency in modern NWP frameworks.

Ibrahim EkrenView profile

Associate Professor

• Stochastic Control Theory
• Stochastic Partial Differential Equations
• Mathematical Finance
• +3 more

Ibrahim Ekren is an Associate Professor of Mathematics at the University of Michigan, specializing in stochastic control, PDEs, and mathematical finance. He holds a Diplôme d'Ingénieur from École Polytechnique (2009), an M.Sc. from Université Paris VI (2010), and a Ph.D. from the University of Southern California (2014). His research integrates stochastic analysis with applications in finance and machine learning, including market microstructure modeling, online learning algorithms, and high-dimensional optimization. Recent publications demonstrate a focus on quantitative finance (Kyle-Back models, liquidity effects) and machine learning (regret minimization, adversarial prediction). He actively advises doctoral students (including Lu Vy and Liwei Huang) and postdoctoral researchers. His NSF grants include DMS-2406240 (2024-2027) and DMS-2007826 (2020-2024). He serves as associate editor for Finance and Stochastics, Applied Mathematics and Optimization, and Advances in Continuous and Discrete Models.

Pavan AduriView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +4 more

Pavan Aduri is a Professor and Interim Department Chair at Iowa State University's Department of Computer Science. His research focuses on theoretical computer science, computational complexity, algorithms, and machine learning. Key interests include statistical similarity estimation, submodular optimization, and probabilistic inference. He has contributed significantly to topics like #P-completeness, data stream processing, and fairness in optimization. His work spans algorithm design, computational hardness analysis, and applications in data science. Notable publications address total variation distance estimation, forgetting mechanisms in data streams, and monotone k-submodular maximization. He has explored connections between model counting and distinct element estimation in streams. Aduri's recent work (2023-2025) emphasizes geometric rounding techniques, Sperner's lemma variants, and algorithmic approaches to probabilistic inference. His research often bridges foundational theory with practical algorithmic challenges in machine learning and big data analysis. No scientific awards are explicitly listed, but his extensive publication record reflects sustained academic contributions.

• Uncertainty Quantification
• Ensemble Kalman Methods
• Bayesian Inverse Problems
• +3 more

Xin T. Tong is Associate Professor in the Department of Mathematics at the National University of Singapore, specializing in uncertainty quantification, machine learning, and operations research. His research develops theoretical foundations and methodologies for structured problem-solving in high-dimensional settings. Current investigations focus on ensemble Kalman methods, Bayesian inverse problems, sampling algorithms, and stochastic optimization with non-i.i.d. data. Research emphasizes mathematical analysis of algorithm efficiency and structure-aware computational methods. Professional background includes postdoctoral work at NYU's Courant Institute and Ph.D. from Princeton University.

Yang WangView profile

Assistant Professor

• Computational Phonology
• Morphophonology
• Language Structure
• +3 more

Yang Wang is an Assistant Professor in the Linguistics Department at the University of Utah, specializing in computational phonology. She received her PhD in Linguistics from UCLA under the supervision of Tim Hunter and Bruce Hayes. Research Interests: Structural properties of natural languages, especially (morpho)phonology How linguistic structures are represented, computed and learned Converging evidence from traditional/formal linguistic analyses, mathematical investigations, computational modeling, and experimental studies Her research examines the learning and computational properties of morphophonological copying (i.e. reduplication). Her recent publications focus on the typology of reduplication and regular copying languages. Professional Activities: Presented a poster titled 'The emergent typology of reduplication: Universals and variations in learning biases.' at AMP in November 2024 Published 'On regular copying languages' in the Journal of Language Modelling in July 2023 (joint work with Tim Hunter)

Leon BungertView profile

Professor

• Machine Learning
• Applied Mathematics
• Partial Differential Equations
• +5 more

Leon Bungert is a W2 Professor with Tenure Track to W3 Professorship for Mathematics III (Mathematics of Machine Learning) at the University of Würzburg, appointed since 2023. He leads the Mathematics of Machine Learning group within the Institute of Mathematics. His academic journey includes a Ph.D. (summa cum laude) from the University of Erlangen-Nürnberg in 2020, focusing on nonlinear spectral theory with variational methods, followed by postdoctoral research at TU Berlin, Bonn, and Erlangen-Nürnberg. Research Interests: Bungert's work bridges applied analysis and machine learning, emphasizing PDEs on graphs, adversarial robustness, inverse problems, optimization, and nonlinear eigenvalue problems. His research often employs variational methods and explores the intersection of geometry, probability, and numerical analysis. Notable areas include adversarial machine learning, Lipschitz learning on graphs, and regularization techniques for inverse problems. Professional Activities: He serves as a guest editor for the European Journal of Applied Mathematics , associate editor for Advances in Continuous and Discrete Models , and co-organizes major conferences like MIA'25 and SSVM 2025. His work has been published in top journals such as Annals of Applied Probability , Journal de Mathématiques Pures et Appliquées , and SIAM Journal on Imaging Sciences . Upcoming Engagements: Invited speaker and organizer at events including the IHP Paris conference (January 2025), Osaka workshop on machine learning and numerics (March 2025), and the BIRS workshop on adversarial machine learning (August 2025). His research team collaborates with institutions globally, including MIT, University of Cambridge, and University of Bonn. Lab/Team: The Mathematics of Machine Learning group at Würzburg focuses on theoretical and applied aspects of machine learning, with active projects in adversarial robustness, PDE-based optimization, and geometric data analysis. Bungert’s team includes researchers like Eloi Martinet and Tim Roith, advancing interdisciplinary work in mathematical foundations of AI.

Christian KanzowView profile

Professor

• Optimization
• Complementarity Problems
• Variational Inequalities
• +6 more

Christian Kanzow is a Full Professor at the University of Würzburg , holding the Chair of Mathematics VII (Numerical Mathematics and Optimization) . His research focuses on optimization problems, complementarity, variational inequalities, generalized Nash equilibrium problems, mathematical programs with equilibrium constraints, sparse optimization, and cardinality constraints, with algorithmic developments for large-scale nonconvex optimization. Academic Affiliation: Faculty of Mathematics and Computer Science, University of Würzburg Research Interests: Optimization theory, numerical methods for equilibrium problems, sparse control, infinite-dimensional optimization His recent publications emphasize proximal gradient methods, augmented Lagrangian techniques, and nonsmooth DC programming, often analyzing convergence under the Kurdyka–Łojasiewicz property or C2-cone reducibility. He has received multiple best paper awards, including the Charles Broyden Prize (2008, 2022) and COAP Best Paper Award (2021) . He served as Dean of the Faculty of Mathematics and Computer Science (2017–2019) and Vice Dean (2015–2017), with extensive involvement in university governance and appointment committees.

Annalisa MassaccesiView profile

Associate Professor

• Geometric Measure Theory
• Calculus of Variations
• Partial Differential Equations
• +3 more

Annalisa Massaccesi is an Associate Professor at the Department of Mathematics 'Tullio Levi-Civita' , University of Padova. Her research focuses on Geometric Measure Theory, Calculus of Variations, and Partial Differential Equations. She has contributed to topics such as mass-minimizing currents, optimal transport, nonlocal diffusion models, and Carnot-Carathéodory spaces. Her work often bridges geometric analysis with advanced analytical techniques. Key areas include: Geometric structures of currents in sub-Riemannian settings Optimal transport problems with multi-material systems Regularity of solutions to PDEs in non-smooth contexts Her articles often explore theoretical frameworks for variational problems, with recent trends emphasizing Frobenius theorem applications and unrectifiability in non-involutive distributions (2025). She has organized conferences on Geometric Measure Theory and collaborates on interdisciplinary projects, including numerical calibration of Steiner trees and biological population dynamics. Education includes a PhD in Mathematical Analysis (2015) with a thesis on modeling dislocations via 1-currents. Active in seminars and workshops, she maintains academic engagement through events like the 'School in Geometric Measure Theory' (2025) and the 'Vito Volterra Meeting in Calculus of Variations' (2023).

• Calculus of Variations
• Nonlinear Analysis
• Partial Differential Equations
• +3 more

Giovanni Pisante is an Associate Professor at the Department of Mathematics and Physics (Università degli Studi della Campania 'Luigi Vanvitelli'). His research focuses on Calculus of Variations, Nonlinear Analysis, and Partial Differential Equations, with emphasis on regularity theory, optimal design problems, and free boundary phenomena. He has contributed to studies involving anisotropic functionals, geometric inequalities, and stability of functional inequalities. Education: Advanced degrees in Mathematics, likely from prestigious Italian institutions (not explicitly stated). His work bridges analytical techniques with geometric problems, addressing topics like degenerate elliptic equations, elasticity theory, and convex geometry. Recent publications explore multiphase optimal design and epsilon-regularity in anisotropic settings. He has participated in conferences on Nonlinear Analysis, PDEs, and Variational Problems, including roles as speaker and organizer of events such as 'START for Analysis' and workshops on L∞ constrained variational problems. No scientific awards are explicitly listed, though his active publication record and conference involvement suggest sustained research engagement. He advises no listed students but collaborates with researchers like N. Fusco, M. Barchiesi, and L. Esposito. His work has implications for both theoretical mathematics and applied fields like material science.

• Geometric Measure Theory
• Geometric Analysis
• Functional Inequalities
• +3 more

Ivan Yuri Violo is a post-doctoral researcher at the Department of Mathematics of the University of Pisa, Italy. His research focuses on geometric measure theory, geometric analysis, functional inequalities, and metric spaces with curvature bounds. He holds a PhD in Mathematical Sciences from an unspecified institution (PhD thesis: Functional and geometric rigidities of RCD spaces and bi-Lipschitz Reifenberg's theorem in metric spaces , 2021). His work explores stability of Sobolev inequalities on Riemannian manifolds, topological regularity of isoperimetric sets, and the interplay between Ricci curvature bounds and geometric properties in non-smooth settings. He has contributed to understanding p-Laplacian equations in RCD spaces, Carleson-type lemmas for uniform rectifiability, and nodal domain theorems in non-smooth geometries. Violo's articles frequently intersect with geometric analysis, partial differential equations, and metric geometry, emphasizing applications to functional inequalities and curvature constraints. He maintains a homepage with further details and is reachable at ivan.violo@sns.it .

• Calculus of Variations
• Partial Differential Equations
• Geometric Analysis
• +3 more

Luca Esposito is an Associate Professor at the University of Salerno, specializing in advanced mathematical analysis with a focus on calculus of variations, partial differential equations, and geometric analysis. His research explores topics such as quasiconvex energies, regularity theory, optimal design problems, and free boundary phenomena. He has contributed to groundbreaking work on Poincaré-Sobolev inequalities and spectral geometry, often collaborating with leading researchers in the field. Esposito has presented his findings at workshops like the 2016 'Workshop on Calculus of Variations and Nonlinear PDEs.' His work bridges theoretical mathematics with applications in material science and optimization, addressing challenges in anisotropic materials and nonlinear elliptic systems. Despite his extensive publication record, no awards or grants are explicitly mentioned in the provided text.

Aurélia FRAYSSEView profile

Associate Professor

• Signal Processing
• Image Reconstruction
• Inverse Problems
• +3 more

Aurélia FRAYSSE is an Associate Professor at CentraleSupélec, affiliated with the Laboratoire des Signaux et Systèmes (L2S). Her research focuses on inverse problems, signal and image processing, with applications in electromagnetics, astrophysics, and computational imaging. She earned her HDR (Habilitation) in 2017 on methodological contributions using sparsity for inverse problems, and her PhD in 2005 from Université Paris XII Val de Marne, specializing in multifractal analysis. Her work emphasizes Bayesian methods, sparse representations, and optimization algorithms in challenging imaging scenarios. Her research areas include variational Bayesian approaches for image reconstruction, sparse coding techniques, and applications in gravitational wave detection, electromagnetic imaging, and multispectral data processing. She has collaborated on projects involving wavelet-based methods, low-rank approximations, and machine learning for inverse problems. Key contributions include advancements in contrast source inversion methods for nonlinear electromagnetic imaging, efficient algorithms for sparse gradient priors, and the development of small-scale networks for seismic pattern classification. Her work bridges theoretical foundations (e.g., minimax theory, Sobolev space regularity) with practical applications in engineering and astrophysics. Dr. FRAYSSE has published extensively in IEEE journals and conferences, including Transactions on Antennas and Propagation, Signal Processing, and European Signal Processing Conferences. Her research also extends to the energy, industry, and health domains through transversal axes at L2S. She is actively involved in the lab’s initiatives for the future of industry and sustainable energy solutions.

Filippo SantambrogioView profile

Professor

• Optimal Transport
• Calculus of Variations
• Partial Differential Equations
• +4 more

Filippo Santambrogio is a Full Professor at Université Claude Bernard - Lyon 1, affiliated with the Institut Camille Jordan. His research focuses on optimal transport theory, calculus of variations, partial differential equations, and gradient flows. He has authored influential books such as *Optimal Transport for Applied Mathematicians* and contributed to foundational work on the JKO scheme and Mean Field Games. Santambrogio has been awarded an ERC Advanced Grant for his project on the JKO scheme and collaborates with institutions like Huawei's Lagrange Center. He actively organizes workshops and lectures internationally, including a 2023 LMS mini-course. His recent work explores applications in crowd dynamics, congestion models, and numerical methods for optimal transport. Notable awards include the ERC Advanced Grant (2023). His teaching includes courses on variational methods and optimal transport, and he advises students like Annette Dumas.

• Calculus of Variations
• Partial Differential Equations
• Material Science
• +3 more

Emilio Acerbi is a Full Professor at the Department of Mathematics, University of Parma. His research focuses on the Calculus of Variations, Partial Differential Equations, and their applications to Material Science and Geometric Analysis. He has organized conferences such as the 2018 'Recent advances in the Calculus of Variations and PDEs' and spoken at events like the 2024 'Frontiers of the Calculus of Variations.' Key research themes include nonlocal sharp interface models, curvature-dependent energies, and stability analysis in materials. His work bridges mathematical rigor with physical phenomena, addressing problems in phase transitions, pattern formation, and geometric flows. Recent contributions explore bifurcation phenomena in interface dynamics and lamellar configurations in reaction-diffusion systems. Awarded grants and fellowships are not explicitly listed, but his extensive publication record reflects sustained academic impact. He maintains an active role in the academic community through organizing workshops and delivering keynote lectures.