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Fabian WaleffeView profile

Professor

• Fluid Dynamics
• Turbulence
• Hydrodynamic Stability
• +5 more

Professor Fabian Waleffe is an Applied Mathematician at the University of Wisconsin, Madison, holding a joint appointment between the Department of Mathematics and the Department of Engineering Physics. His academic career spans multiple decades with extensive teaching experience at both UW Madison and MIT. Professor Waleffe's primary research focuses on the fundamental problem of turbulence in fluid flows and its relation to Exact Coherent States. He developed the Self-Sustaining Process theory for shear flows, which provides an ab initio method to discover families of 3D traveling wave solutions of the Navier-Stokes equations in all canonical shear flows. His research also encompasses geophysical flows and computational methods for solving partial differential equations, including spectral integration methods and numerical techniques for incompressible flows. His publication record demonstrates consistent focus on hydrodynamic instabilities, turbulence, and coherent structures. Recent work examines optimal heat transport in Rayleigh-Bénard convection, streak instability, and near-wall turbulence, integrating theoretical analysis, numerical computation, and physical insight to address fundamental questions in fluid dynamics. His research has evolved from early work on triad interactions in homogeneous turbulence to the current focus on exact coherent structures that form the 'backbone' of turbulent shear flows. Professor Waleffe has taught extensively across the mathematics and engineering curriculum. He has taught Math 321 (Vector and complex calculus for the physical sciences) for 29 semesters at UW Madison, along with numerous other undergraduate and graduate courses. His teaching spans from introductory calculus to advanced graduate topics in hydrodynamic instabilities and turbulence, reflecting his deep commitment to mathematical education in the physical sciences.

• Financial Mathematics
• Actuarial Mathematics
• Stochastic Processes
• +7 more

Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.

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

Andrea Pinamonti is an Associate Professor at the University of Trento. His research focuses on geometric analysis, partial differential equations, calculus of variations, and functional analysis in metric measure spaces, particularly in sub-Riemannian and Carnot group settings. He frequently collaborates with researchers from institutions such as the Universities of Pisa, Jyväskylä, and others, addressing topics like geometric measure theory, regularity of solutions, and nonlocal functionals. His recent work examines structures in Heisenberg groups, such as perimeter minimization, CR geometry, and differentiability theorems. He has also explored equations involving the p-Laplacian, fractional operators, and universal differentiability sets in non-Euclidean spaces. These studies reflect a sustained engagement with the interplay between geometry and analysis in sub-Riemannian frameworks. Events and Contributions: Speaker at Warsaw Analysis Days Event WADE25 (2025), Summer school in fluid dynamics (2024), and Workshop on Synthetic Curvature Bounds (2024). Organizer of Three days between Analysis and Geometry in Trento (2025, 2024) and EUregio School on Control Theory and Applications (2024). He has maintained a prolific publication record across high-impact journals such as Journal of Geometric Analysis , Advances in Mathematics , and Communications in Contemporary Mathematics . His academic activities include promoting collaborative research through workshops and open positions at his institution.

• Geometric Measure Theory
• Optimal Transport
• Calculus of Variations
• +3 more

Luigi Ambrosio is a Full Professor at the Scuola Normale Superiore di Pisa (SNS), specializing in geometric measure theory, optimal transport, and partial differential equations. His research focuses on the interplay between geometric analysis, functional analysis, and calculus of variations, with applications to metric measure spaces and stochastic processes. He has organized numerous conferences and schools on optimal transport and geometric analysis, including the 2025 'XXXV Convegno Nazionale di Calcolo delle Variazioni.' Key research interests include the theory of currents, regularity of flows, and the application of optimal transport to problems in probability and geometry. Notable contributions include foundational work on metric Sobolev spaces, RCD spaces, and the analysis of geometric flows. Ambrosio frequently collaborates with leading institutions and has supervised numerous seminars on topics ranging from gradient flows to non-smooth geometric structures. His publications span over 150 papers, addressing topics such as the regularity of vector fields, entropy flows in Carnot groups, and the stability of action functionals. Recent works (2021–2025) explore superposition principles for currents, sharp PDE estimates for random matching, and embedding theorems for metric spaces. Ambrosio is also active in academic leadership, contributing to editorial boards and international research networks.

Jonathan M. RosenbergView profile

Professor

• K-theory
• Noncommutative Geometry
• C*-algebras
• +7 more

Jonathan M. Rosenberg is a Professor of Mathematics at the University of Maryland, where he holds the Ruth M. Davis Professorship. His career spans several decades with significant contributions across multiple mathematical disciplines. He maintains active involvement in departmental activities including the Geometry/Topology Seminar, Algebra/Number Theory Seminars, Departmental Colloquium, and Geometry and Physics RIT. Rosenberg's research focuses on the deep connections between topology, geometry, and mathematical physics. His primary interests include K-theory, noncommutative geometry, C*-algebras, index theory of elliptic operators, and positive scalar curvature problems. His work bridges pure mathematics with theoretical physics, particularly in string theory applications. His research has evolved to address increasingly sophisticated questions in manifold theory, pseudomanifolds, and their connections to physical theories. His recent publications show a continued focus on positive scalar curvature problems across various geometric settings including spin c manifolds, pseudomanifolds, and manifolds with boundary. Rosenberg also maintains active collaborations with leading mathematicians like Boris Botvinnik, Varghese Mathai, and others, producing work that connects topology with mathematical physics through T-duality and related concepts. His publications span prestigious journals including Journal of Geometry and Physics, Communications in Mathematical Physics, and Advances in Theoretical and Mathematical Physics. Fellow of the American Mathematical Society Ruth M. Davis Professorship at University of Maryland Co-editor of influential volumes including "Novikov Conjectures, Index Theorems and Rigidity" Author of the graduate textbook "Algebraic K-Theory and its Applications" Principal speaker at multiple NSF/CBMS Regional Conferences Rosenberg serves on editorial boards of several prominent journals including Annals of K-Theory (as Managing Editor Emeritus), Journal of Topology and Analysis (as Managing Editor), Homology Homotopy and Applications, SIGMA, and The New York Journal of Mathematics. He previously served as Secretary of the K-Theory Foundation from 2010 to 2021. His teaching responsibilities span undergraduate courses including Calculus and Differential Equations to advanced graduate courses in K-theory, Homotopy Theory, and Noncommutative Geometry. He has maintained the Novikov Conjecture home page and the UMCP Math Department electronic journals web page as significant service contributions to the mathematical community.

Georgia ActonView profile

Lecturer

• Plasma Physics
• Stellarator Physics
• Theoretical Astrophysics
• +3 more

Georgia Acton is a Lecturer in Physics at Merton College, Oxford, affiliated with the Rudolf Peierls Centre for Theoretical Physics (RPC). Her research focuses on Stellarator optimisation and plasma turbulence in 3D magnetic fields, collaborating with Michael Barnes and Sarah Newton. She has presented at major conferences, including an invited talk at the 20th European Fusion Theory Conference (EFTC 2023). Teaching roles include tutoring third-year undergraduate Fluid Dynamics for three years, mentoring master's students in Kinetic Theory, and teaching Vector Calculus, Ordinary Differential Equations, and Statistical Mechanics at the undergraduate level. She designed the Dimensional Analysis course for the UNIQ Summer School and delivered public outreach lectures such as 'Stellarators: twisty tokamaks that could be the future of fusion.' Her involvement in outreach includes organising Physics Olympiad events, school visits, and developing teaching aids for secondary schools. She contributes to the Theoretical Astrophysics and Plasma Physics research group at RPC.

• Functional Analysis
• Generalized Functions
• Integral Transforms
• +7 more

Stevan Pilipović is a Full Professor at the Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad. His research spans generalized functions, integral transforms, pseudodifferential operators, and fractional calculus. He holds a PhD in Mathematics from the University of Novi Sad (1979) and has led key academic roles, including editorships in journals like Integral Transforms and Special Functions and Pseudo-Differential Operators and Applications . Education: BSc in Mathematics, University of Novi Sad (1973) MSc in Mathematics, University of Belgrade (1977) PhD in Mathematics, University of Novi Sad (1979) Professional Activities: Leader of the Generalized Product and Integral Transformations seminar Editorial roles in multiple international journals Supervisor of numerous research projects in functional analysis and mathematical physics Research Interests: Pilipović’s work emphasizes microlocal analysis, fractional calculus in viscoelasticity, and stochastic PDEs. Key contributions include monographs on generalized functions and Fourier analysis, with over 400 publications. His research bridges pure mathematics (e.g., ultradistribution theory) and applications in mechanics and signal processing. Grants and Collaborations: Active in international collaborations, including projects on fractional Zener models and stochastic dynamics. His work frequently intersects with engineering and physics, addressing real-world problems in material science and wave propagation.

• Algebra
• Commutative Algebra
• Algebraic Geometry
• +3 more

Linquan Ma is an Associate Professor in the Department of Mathematics at Purdue University, specializing in Commutative Algebra and Algebraic Geometry. His research focuses on singularities in mixed characteristic, perfectoid spaces, and homological conjectures. He earned his Ph.D. from the University of Michigan under Melvin Hochster. His work bridges algebraic and geometric perspectives, with applications to F-singularities, local cohomology, and multiplier ideals. Education: Ph.D. in Mathematics, University of Michigan (2014), advised by Melvin Hochster Research Interests: Commutative Algebra: F-singularities, tight closure, local cohomology, multiplier ideals, Hilbert-Kunz multiplicity Algebraic Geometry: Singularities in mixed characteristic, minimal model program, perfectoid methods Homological Algebra: Ulrich modules, Boij-Söderberg theory, homological conjectures Recent Work Trends: Recent papers explore perfectoid spaces' role in resolving homological conjectures, behavior of test ideals in mixed characteristic, and structural properties of Cohen-Macaulay modules. His work often unifies characteristic p and mixed characteristic techniques. Awards: None explicitly listed, though his contributions to F-singularity theory and mixed characteristic algebra highlight his field-leading impact. Teaching: Purdue: Courses include Algebra Honors, Linear Algebra, and graduate topics in commutative algebra (e.g., 2024: Positive characteristic methods) Prior institutions: University of Utah (Calculus, Differential Equations), University of Michigan (undergraduate calculus) Collaborations: Active collaborations with Bhargav Bhatt, Karl Schwede, Ilya Smirnov, and others on foundational problems in commutative algebra and algebraic geometry.

Anil N. HiraniView profile

Professor

• Differential Equations
• Numerical Analysis
• Computational Topology
• +5 more

Anil N. Hirani is a Professor in the Department of Mathematics at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the College of Liberal Arts & Sciences. He holds a PhD from the California Institute of Technology (2003) in Computer Science with minors in Mathematics and Control and Dynamical Systems. His academic journey includes roles as Assistant Professor (Computer Science, UIUC, 2005–2013) and Associate Professor (Mathematics, UIUC, 2013–2022) before becoming a full Professor in 2022. His research focuses on the interplay between geometry/topology and algorithms, with emphasis on structure-preserving discretizations of exterior calculus and differential geometry. Key areas include Discrete Exterior Calculus (DEC), numerical methods for PDEs, computational topology, and machine learning applications. He has organized workshops, such as the 2025 Discrete Exterior Calculus workshop at IMSI, and contributed to software like PyDEC. Education: PhD, Caltech (2003); MS in Computer Science (Stanford); Undergraduate degree in Computer Science (BITS Pilani, India). Awards include the NSF CAREER Award (2007–2012). Teaching includes courses on Differential Geometry (MATH 423), Vector and Tensor Analysis (MATH 481), and Computational Mathematics (MATH 490). He has advised numerous PhD students, notable among them Kaushik Kalyanaraman and Vaibhav Karve. Articles span DEC applications in fluid dynamics, cohomology computations, and machine learning. His work bridges theoretical foundations with practical applications in engineering and computer science.

• Applied Geometry
• Discrete Differential Geometry
• Numerical Analysis
• +3 more

Max Wardetzky is a Professor at the Institute for Numerical and Applied Mathematics within the Faculty of Mathematics and Computer Science at the University of Göttingen, Germany. His office is located at Lotzestraße 16-18, 37083 Göttingen, and he can be reached via email at wardetzky@math.uni-goettingen.de or by phone at +49 551 39 26778. Professor Wardetzky leads the Discrete Differential Geometry Lab at the University of Göttingen, where he conducts research at the intersection of mathematics, computer science, and geometry processing. His work bridges theoretical foundations with practical applications in computer graphics and scientific computing. His primary research interests include: Applied Geometry Discrete Differential Geometry Numerical Analysis Geometry Processing Physical Simulation Computer Graphics Professor Wardetzky's extensive publication record demonstrates significant contributions to the field of discrete differential geometry and its applications. His work shows a consistent focus on developing mathematically rigorous yet computationally efficient methods for geometric problems. Key trends in his research include the development of discrete analogues of smooth geometric objects, the study of convergence properties between discrete and continuous models, and the application of these methods to problems in computer graphics and physical simulation. Professor Wardetzky has made substantial contributions to the theoretical foundations of discrete differential geometry while maintaining strong connections to practical applications. His work on discrete Laplacians, curvature approximations, and geometric flows has influenced both theoretical mathematics and practical geometry processing algorithms.

• Compiler Optimization for High Performance Computing
• Optimization of Sparse/Dense Matrix/Tensor Computations
• Scalable Machine Learning
• +1 more

P. (Saday) Sadayappan is a Professor in the School of Computing at the University of Utah. He serves as a lead researcher in high-performance computing, with a focus on compiler optimization and algorithm-architecture co-design. His current projects include NIH SBIR Phase 2 funding for large-scale image analysis and NSF grants for tensor applications and cyber-infrastructure for AI. Research Interests : Compiler Optimization for High Performance Computing Optimization of Sparse/Dense Matrix/Tensor Computations Scalable Machine Learning Algorithm-Architecture Co-Design Optimization Research Trends in Publications : His work emphasizes optimizing computational workflows for emerging hardware architectures, with a focus on accelerating machine learning and scientific computing through compiler-level innovations. Recent trends include co-design for CNNs, sparse matrix optimizations, and distributed algorithms. Scientific Awards : ACM SIGPLAN Most Influential PLDI Paper Award (2018) Grants & Projects : NSF (2022–2027): Comprehensive Framework for Tensor Applications NSF AI Institute ICICLE (2021–2026): Cyber-infrastructure for environmental AI NIH SBIR (2023–2025): Next-gen machine learning for image analysis Labs & Teams : Collaborates with institutions like Ohio State University and RNET Technologies on projects involving parallel computing, sparse algorithms, and compiler design.

Rasmus BjørkView profile

Professor

• Energy Conversion
• Materials Science
• Magnetocaloric and Elastocaloric Cooling
• +4 more

Rasmus Bjørk is a Professor at the Technical University of Denmark (DTU) in the Department of Energy Conversion and Storage. His research focuses on advanced materials for energy systems, particularly in magnetocaloric and elastocaloric cooling, magnetic materials, and additive manufacturing for functional devices. His work contributes to the UN Sustainable Development Goals, especially in affordable and clean energy. PhD Supervision: Active projects include energy storage using topological spin textures, magnetothermal waste heat harvesting, and bio-magnetometers. Key Research Areas: Magnetic refrigeration, energy harvesting, and freeze-casting of functional materials. Recent advancements include 3D-printed elastocaloric coolers and studies on magnetoresistive devices. His team develops novel techniques for optimizing magnetic systems and energy conversion processes. He has published over 160 articles and led projects on regenerator design, magnetic bearings, and sensor technologies. Collaborations span multiple countries and disciplines. Notable contributions include pioneering work on freeze-casting for biomaterials and the MagTense micromagnetic framework. His research bridges theoretical modeling and practical applications in sustainable energy solutions.

• Optimization
• Convex Analysis
• Nonlinear Analysis
• +3 more

Radu Ioan Bot is a Professor and Dean of the Faculty of Mathematics at the University of Vienna, where he also serves as Head of the Department of Mathematics. His primary affiliations include the Department of Mathematics (Oskar-Morgenstern-Platz 1, 1090 Wien) and the Research Network Data Science (Währinger Straße 29, 1090 Wien). Bot's research centers on optimization theory with emphasis on convex/nonconvex optimization, monotone operators, and dynamical systems. He develops fast algorithms for variational inequalities and monotone inclusions by bridging continuous-time dynamics with discrete optimization methods. His work frequently addresses bilevel optimization, Tikhonov regularization, and second-order dynamics, yielding accelerated convergence rates for complex problems. Analysis of his 15 most recent publications (2023-2025) reveals dominant trends in time-scaling techniques, vanishing damping dynamics, and structured splitting methods. Key contributions include unifying Nesterov acceleration with Heavy Ball dynamics, developing reflected forward-backward algorithms for constrained optimization, and establishing strong convergence guarantees for monotone operator flows. These advances demonstrate consistent innovation in accelerating optimization while maintaining theoretical rigor. No scientific awards were mentioned in the provided source material. Details regarding student advising and research grants were not specified in the available information, though his leadership roles as Dean and Department Head indicate significant administrative responsibilities alongside active research. Bot participates in the University of Vienna's Research Network Data Science, suggesting interdisciplinary engagement in data-driven methodologies with potential applications in machine learning and computational mathematics.

• Mathematical Logic
• Model Theory
• O-Minimal Structures
• +5 more

Patrick Speissegger is a Professor in the Department of Mathematics and Statistics at McMaster University. He holds a Ph.D. in Mathematics from the University of Illinois at Urbana-Champaign and a Diploma in Mathematics from the Swiss Federal Institute of Technology Lausanne. His research focuses on O-Minimal Structures, Real Analytic Geometry, and their applications to Differential Equations and Dynamical Systems. Education: Ph.D., University of Illinois at Urbana-Champaign; Diploma, Swiss Federal Institute of Technology Lausanne His recent work explores multisummable series, transseries, and Hilbert's 16th problem. Publications span foundational topics in Model Theory, Pfaffian systems, and geometric definability. He has served as an instructor for various undergraduate and graduate courses, including Advanced Calculus and Quantum Computing. His scholarly activity includes collaborations and presentations on O-Minimal Structures and their implications in Number Theory and Analysis. Despite no explicit mention of scientific awards, his contributions to mathematical logic and analytic geometry remain significant.

• Geometric Analysis
• Differential Geometry
• Partial Differential Equations
• +3 more

Haotian Wu is a Senior Lecturer in the School of Mathematics and Statistics at The University of Sydney, where he maintains an active research program in geometric analysis. His academic appointments include membership in the Geometry, Topology and Analysis Research Group, and he has been involved in organizing significant mathematical events including the AMSI Summer School 2025 and the Symposium on Geometric Analysis and Non-linear PDEs. Dr. Wu's educational background includes a PhD in Mathematics from The University of Texas at Austin (2013) and dual Bachelor's degrees in Mathematics and Physics from Lafayette College (2007). His research aligns with the University's Faculty of Science Research Strengths in Understanding the Universe, Fundamental Laws of Nature, and Complex Systems. Wu's research focuses on geometric evolution equations, particularly Ricci flow and mean curvature flow. His work investigates singularity formation, stability properties, and asymptotic behavior in these flows, with significant contributions to understanding Type-II singularities. His research bridges pure mathematics and mathematical physics, with applications to general relativity and geometric analysis. He has developed novel analytical techniques for studying nonlinear partial differential equations arising in geometric contexts. Analysis of Wu's publication record shows a consistent trajectory of high-impact research in geometric analysis, with increasing focus on numerical methods for studying geometric flows in recent years. His work demonstrates strong international collaboration, particularly with researchers in the United States, including notable collaborations with Garfinkle, Isenberg, Knopf, and Zhang. The publications reveal a progression from foundational work on Ricci flow to increasingly sophisticated analyses of mean curvature flow and related geometric evolution equations. Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) 2018 for 'Singularity Analysis for Ricci Flow and Mean Curvature Flow' Faculty of Science Faculty Startup Scheme 2023 for 'Elliptic and parabolic problems in geometric analysis' Dr. Wu currently supervises research students including Alexander BEDNAREK (working on General Kahler Ricci Flow) and Tiernan CARTWRIGHT (working on Hölder regularity of solutions to degenerate complex Monge–Ampère equations). He has received multiple research grants supporting his work in geometric analysis. Wu has been actively involved in teaching undergraduate mathematics courses including MATH1021 Calculus of One Variable, MATH2061 Linear Mathematics and Vector Calculus, and specialized courses like MATH3968 Differential Geometry. As an active member of the mathematical community, Wu co-organizes significant research events including the AMSI Summer School 2025 and the International Conference on Nonlinear Partial Differential Equations honoring Professor Neil Trudinger's 80th birthday. His research group focuses on geometric analysis problems with connections to mathematical physics and differential geometry.