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Stefano Vigogna is an Associate Professor in the Department of Mathematics at the University of Rome Tor Vergata with significant contributions to theoretical machine learning. He is affiliated with the Rome Center on Mathematics for Modeling and Data Sciences (RoMaDS), focusing on the mathematical foundations of learning algorithms. His research expertise spans: Machine Learning Statistical Learning Theory Harmonic Analysis Professor Vigogna's publication record demonstrates deep theoretical work connecting advanced mathematics to machine learning. His research investigates the spectral properties, geometric structure, and convergence behavior of neural networks using functional analysis and harmonic analysis techniques. Notable publications include his 2022 ICML paper on multiclass learning with exponential convergence rates and numerous works exploring the mathematical properties of deep learning systems through reproducing kernel spaces. He teaches Statistica for the Master's program in Environmental Biology and Statistical Learning for the Master's program in Pure and Applied Mathematics, reflecting his dual expertise in mathematical theory and practical data science applications. Professor Vigogna maintains active collaborations with leading researchers including Lorenzo Rosasco and Ernesto De Vito, advancing our fundamental understanding of learning algorithms through rigorous mathematical analysis. His work represents an essential bridge between pure mathematics and the theoretical foundations of modern artificial intelligence.

• Machine Learning
• Statistical Learning Theory
• Harmonic Analysis

Luciano Mari is an active mathematics researcher affiliated with the Class of Sciences, specializing in differential geometry and geometric analysis. His research encompasses both theoretical and applied aspects of mathematics with significant contributions to the field. Research interests focus on: Analysis of minimal submanifolds and their spectral properties Nonlinear partial differential equations with geometric applications Conformal geometry and Möbius transformations Spectral theory on Riemannian manifolds Extensions of classical geometric theorems Publication analysis reveals consistent work in geometric analysis since 2009, with recent focus on nonlinear equations, spectral estimates for submanifolds, and applications of maximum principles in Riemannian settings. The research shows progressive development from foundational geometric theory toward applications in PDEs and spectral analysis. Collaborative work spans multiple countries including Brazil, United States, and various European institutions. While specific awards aren't mentioned, the publication record in prestigious mathematics journals indicates significant recognition in the field. Current research directions appear to include nonlinear problems in conformal geometry, spectral properties of immersed submanifolds, and generalizations of classical geometric theorems to non-Euclidean settings.

• Differential Geometry
• Geometric Analysis
• Partial Differential Equations
• +2 more

Professor Aissa Wade is a full Professor of Mathematics at Pennsylvania State University, where she has served on the faculty since 2001 and was promoted to full professor in 2016. She concurrently held the presidency of the African Institute for Mathematical Sciences (AIMS) in Senegal from 2016 to 2018, becoming the first woman to lead this prestigious pan-African institution. Her academic career began with postdoctoral research at the Abdus Salam International Centre for Theoretical Physics. Dr. Wade's research specializes in differential geometry and mathematical physics, with foundational contributions to conformal Dirac structures. Her work explores the interplay between geometric structures and global analysis, particularly examining Dirac structures within conformal geometry frameworks. This research bridges theoretical mathematics with applications in geometric mechanics and mathematical physics. Her notable recognitions include: Election as a Fellow of the African Academy of Sciences in 2017 Feature as a Black History Month 2020 Honoree by Mathematically Gifted & Black for her groundbreaking contributions and leadership

• Differential Geometry
• Mathematical Physics
• Dirac Structures
• +3 more

Hugo Lavenant is an Assistant Professor in the Department of Decision Sciences at Bocconi University in Milan, Italy. His academic journey includes a PhD in mathematics from Université Paris-Sud under Filippo Santambrogio (2016-2019) and a postdoctoral fellowship at the University of British Columbia (2019-2020) working with Young-Heon Kim, Brendan Pass, Geoffrey Schiebinger, and Dave Schneider. Professor Lavenant's research spans theoretical and applied aspects of mathematical analysis, with a focus on optimal transport theory , calculus of variations , and Bayesian statistics . His work explores the geometry of the Wasserstein space, numerical solutions to dynamical optimal transport problems, and applications to biological data analysis. He has made significant contributions to understanding harmonic mappings in the Wasserstein space, connections between optimal transport and nonlinear elasticity, and the application of optimal transport to trajectory inference in biological systems. Analysis of Professor Lavenant's recent publications reveals a strong trend toward interdisciplinary applications of optimal transport, particularly in statistics and biology. His work bridges pure mathematical theory with practical computational methods, with increasing focus on developing tractable statistical tools based on optimal transport distances. The research spans theoretical mathematics, computational methods, and applications to real-world data analysis problems. Professor Lavenant actively supervises graduate students, currently advising PhD candidates George Kanchaveli and Francesco Mascari (both co-advised with Marta Catalano), as well as Master's students Mathis Hardion and Niccolò Bargellini. His teaching portfolio includes Mathematical Analysis 2, Real Analysis I, and Advanced Analysis and Optimization 1 at Bocconi University. His scholarly contributions demonstrate a consistent focus on advancing both the theoretical foundations and practical applications of optimal transport, with growing emphasis on statistical methodology and biological applications in recent years.

• Optimal Transport
• Calculus of Variations
• Mathematical Analysis
• +4 more

Giuseppe Conti is an Associate Professor of Mathematical Institutions at the Faculty of Architecture, University of Florence, where he has been teaching since 1980. Born in Florence on February 27, 1947, he graduated in Mathematics from the University of Florence in 1972 with highest honors (110 cum laude). His career has spanned multiple institutions including the University of Calabria where he taught from 1973-1980 before returning to Florence. Professor Conti's research interests bridge pure and applied mathematics, with primary focus on Nonlinear Functional Analysis, Differential and Integral Equations, and the mathematical applications to architecture, particularly the analysis of Brunelleschi's Dome of Santa Maria del Fiore. His work also extends to mathematical applications in music, photogrammetry, and economics. He has developed specialized techniques for photogrammetric analysis of architectural structures and co-authored a significant text on analytical photogrammetry of non-metric architectural images. His publication record shows consistent output through 2025, with recent work focusing on geometric structures in architecture, Fibonacci numbers in art and music, and continued theoretical work in integral equations. His publications demonstrate a unique interdisciplinary approach that connects mathematical theory with practical architectural applications. Pirelliaward 2002 for scientific dissemination via the internet (first mathematician to win this award) Professor Conti has supervised numerous theses on both pure mathematics topics and mathematical applications to architecture. His teaching spans multiple departments at the University of Florence, including Architecture and Engineering, where he teaches mathematical analysis and specialized courses connecting mathematics to architecture and music. He has also been active in professional development, conducting numerous courses organized by the Veneto and Friuli regions with European Social Fund sponsorship. His research group focuses on mathematical applications to architectural analysis, particularly the geometric modeling of historical structures. This work has led to significant insights into Brunelleschi's construction techniques and has been featured in national television programs, architectural journals, and major Italian newspapers.

• Nonlinear Functional Analysis
• Differential and Integral Equations
• Mathematical Applications to Architecture
• +5 more

Massimo Furi is a mathematician whose research spans nonlinear analysis, functional differential equations, and topological methods. His work extensively explores bifurcation theory, spectral properties of operators, and global continuation techniques. Key contributions include the study of perturbed eigenvalue problems in Hilbert spaces, retarded functional equations on manifolds, and applications of degree theory. His research integrates fixed-point theory with dynamical systems, focusing on existence and persistence theorems for oscillations and eigenvectors under perturbations. Recent publications (2012–2023) emphasize topological persistence, nonlinear spectral theory, and forced oscillations in constrained systems. Furi collaborates with mathematicians globally, co-authoring works with peers from Italian and international institutions. His 55-year publication record demonstrates expertise in both theoretical frameworks and applied mathematical problems.

• Nonlinear Analysis
• Functional Differential Equations
• Topological Methods
• +3 more