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Stefan GuettelView profile

Professor

• Numerical Analysis
• Scientific Computing
• Matrix Computations
• +3 more

Professor Stefan Guettel holds the academic rank of Professor of Applied Mathematics at The University of Manchester. His research focuses on numerical analysis, scientific computing, and matrix computations, with particular expertise in rational approximation, nonlinear eigenvalue problems, and Krylov methods. He has contributed to impactful research in areas such as computational efficiency, clustering algorithms, and randomized sketching techniques. Guettel has received prestigious awards including the James H. Wilkinson Prize (2021) and the Taussky–Todd Prize (2023). His work emphasizes practical applications of numerical methods in solving complex scientific and engineering problems. He has been actively involved in professional organizations such as the International Linear Algebra Society and served as SIAM UKIE Secretary/Treasurer from 2016 to 2018. His recent publications (2024) highlight advancements in Arnoldi processes, clustering algorithms, and randomized sketching for eigenvalue problems, demonstrating a focus on computational efficiency and algorithmic innovation. Guettel has also contributed to industrial safety through alarm system modeling and advised on pandemic response strategies during the UK’s efforts against COVID-19.

• Numerical Analysis
• Numerical Linear Algebra
• Eigenvalue Problems
• +2 more

Professor Francoise Tisseur is a leading researcher in numerical analysis at the University of Manchester's Department of Applied Mathematics. Her work focuses on eigenvalue problems, structured matrices, and algorithm stability, with significant contributions to numerical linear algebra. Her research spans numerical methods for matrix polynomials, eigenvalue computation, and structured matrix factorizations, with applications in scientific computing and engineering. Recent trends show emphasis on precision arithmetic, robust algorithms, and polynomial eigenvalue problems. Awards: Adams Prize (2012) SIAM Fellow (2016) Royal Society Wolfson Merit Award (2014) Whitehead Prize (2010)

Marcus WebbView profile

Lecturer

• Numerical Linear Algebra
• Approximation Theory
• Orthogonal Polynomials
• +1 more

Dr. Marcus Webb is Lecturer in Applied Mathematics at the University of Manchester, specializing in numerical analysis and computational mathematics. His research develops algorithms for spectral methods, matrix computations, and scientific simulations. Recent work includes stability analysis of sketch-and-precondition solvers, modeling of inhomogeneous chemical kinetics, and approximation methods for wave packets using orthogonal systems. Collaborations span computational chemistry and physics applications. He obtained his DPhil from the University of Cambridge and MSc from Oxford University. Current projects focus on Riemannian optimization for inverse eigenvalue problems and mixed-precision Jacobi algorithms.

Rita Teixeira da CostaView profile

Assistant Professor

• Mathematical General Relativity
• Black Holes
• Hyperbolic Partial Differential Equations
• +3 more

Rita Teixeira da Costa is an Assistant Professor at the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, and Sheila Edmonds Lecturer at Newnham College. She holds a dual affiliation as an NSF and Princeton Gravity Initiative Fellow (2021–2024) and Junior Research Fellow at Trinity College. Her research focuses on Mathematical General Relativity, particularly black hole dynamics, hyperbolic PDEs, and geometric analysis. Her work explores foundational questions in gravitational physics, including stability properties of black holes, the behavior of gravitational waves, and the interplay between geometric structures and partial differential equations. Notable contributions include analyses of the Teukolsky equation on Kerr spacetimes and homogenization techniques applied to Einstein’s equations in symmetric configurations. Key awards include her NSF fellowship and Trinity College Junior Research Fellowship. While her profile does not explicitly list grants or advising roles, her publications reflect active engagement in collaborative research with institutions like Princeton University and international co-authors. Her personal homepage at DPMMS provides further details on her research outputs and academic activities.

Dmitry JakobsonView profile

Professor

• Analysis
• Spectral Geometry
• Quantum Chaos
• +2 more

Roles & Affiliations: Dmitry Jakobson is a Full Professor at McGill University's Department of Mathematics and Statistics, part of the Faculty of Science. He has held academic ranks including Associate Professor (2003–2008) and Assistant Professor (2000–2003). He has been affiliated with institutions like the Institute for Advanced Study (IAS) and Caltech earlier in his career. Education: B.S., Massachusetts Institute of Technology, 1991 Ph.D., Princeton University, 1995 Research Interests: Jakobson's work spans Analysis (eigenfunctions of Laplacians), Spectral Geometry, Quantum Chaos, Number Theory, and Graph Theory. He explores topics like random metrics, nodal sets, conformal invariants, and probabilistic methods in geometry and topology. Grants & Awards: Alfred P. Sloan Foundation Fellowship (2001) NSERC Grants (2000, 2004, 2009) Canadian Mathematical Society's G. de B. Robinson Award (2008) Teaching & Service: Jakobson has taught advanced courses in Analysis, PDEs, and Spectral Theory. He co-organized the 2016 CRM thematic semester on probabilistic methods. He served as Editor-in-Chief of Annales mathématiques du Québec and advised numerous graduate students. Labs & Collaborations: His research often intersects with probabilistic geometry, spectral analysis, and mathematical physics. Collaborators include experts in number theory (e.g., Henry Kim), geometric analysis (Iosif Polterovich), and quantum chaos (John Toth).

Henry Horng-Shing LuView profile

Professor

• Statistics
• Image Science
• Bioinformatics
• +5 more

Henry Horng-Shing Lu is a Distinguished Professor at the Institute of Statistics, National Yang Ming Chiao Tung University (NYCU), Taiwan. He holds adjunct roles at Cornell University and Taipei Veterans General Hospital. His research spans statistics, bioinformatics, medical imaging, and machine learning, with a focus on interdisciplinary applications in healthcare and engineering. Education: PhD in Statistics (Cornell University, 1994), M.S. Statistics (Cornell, 1990), B.S. Electrical Engineering (National Taiwan University, 1986). Research Interests: Statistical methods in medical imaging (e.g., MRI, PET), bioinformatics (gene networks, protein interactions), and machine learning for precision medicine. He develops algorithms for image segmentation, disease classification, and genomic data analysis. Awards: Elected ISI Member (2011), PFHEA (2020), Outstanding Research Award (2022), IEEE Senior Member (2022). He has served as Dean of Academic Affairs at NYCU and led initiatives in big data and AI. Labs: Director of the Lu Lab at NYCU, focusing on medical AI, statistical bioinformatics, and secure random number generation. Collaborates with Taipei Veterans Hospital on clinical imaging systems.

Jesse LaeuchliView profile

Senior Lecturer

• Cyber Security
• Quantum Computing
• Linear Algebra
• +5 more

Dr. Jesse Laeuchli is a Senior Lecturer in the Department of Computer Science and Engineering at UNSW, specializing in Cyber Security, Quantum Computing, and Numerical Linear Algebra. He holds a PhD from the College of William and Mary, where his dissertation focused on matrix function diagonal estimation. His research bridges theoretical foundations (e.g., graph analytics, differential privacy) with practical applications like quantum remote memory attestation and posthumous data management through tools like BeyondLife. He has conducted security reviews for the Australian Government and led projects funded by the Defense Innovation Network and EU's HERMES initiative. Education : PhD in Numerical Linear Algebra, College of William and Mary Research Interests : Quantum Cybersecurity: Developing protocols using quantum entanglement for remote hardware verification Cyber Defense: Specializing in penetration testing and securing IoT systems Data Privacy: Innovating differential privacy techniques for centrality measures and graph analysis Legacy Systems: Creating open-source solutions for posthumous data management Grants & Awards : $528k IoT Security Grant (CSCRC, 2023) $136k Quantum Security Grant (Defense Innovation Network) Cyber Security CRC Award for pandemic contact tracing efforts Advising & Labs : Supervises research in quantum security and data forensics. Collaborates on projects like BeyondLife, an open-source digital will platform.

• Applied Mathematics
• Dynamical Systems
• Partial Differential Equations
• +3 more

Robert Marangell is an Associate Professor in Applied Mathematics at the University of Sydney's School of Mathematics and Statistics. He has held academic positions since 2008, including Senior Lecturer (2017–2021), Lecturer (2013–2017), and postdoctoral roles at institutions like the University of Warwick and the University of North Carolina at Chapel Hill. His research focuses on dynamical systems and partial differential equations (PDEs), particularly applying topological methods to analyze temporal dynamics of solutions to PDEs. He also explores mathematical biology, nonlinear dynamics, and Hamiltonian systems. Marangell’s educational background includes a PhD in Mathematics from the University of North Carolina at Chapel Hill (2008) and a BA in Mathematics from Cornell University (2003). His recent work emphasizes stability analysis of traveling waves, shock dynamics in reaction-diffusion systems, and spectral theory for Hamiltonian PDEs. He has contributed to understanding shock selection rules, viscous relaxation effects, and the Maslov index in diverse models, including biological invasion and fluid dynamics. Grants: 2024: 'We got the beat: a fast-slow splitting of the Maslov index' (Re-entry Fellowship). 2021: 'Spectral Theory of Hamiltonian Dynamical Systems' (ARC Discovery Project). His research group is part of the Applied Mathematics Group at Sydney. Although no specific students are listed, his work involves collaborative projects with colleagues such as Holger Dullin and Martin Wechselberger. He maintains an active research program with a focus on interdisciplinary applications of PDEs and dynamical systems.

• Numerical Analysis
• Geometric Integration
• Quantum Dynamics
• +5 more

Prof. Christian Lubich is a Professor in the Mathematical Institute at the University of Tübingen, Germany. His research focuses on numerical analysis, geometric integration, quantum dynamics, and partial differential equations. He has authored influential books such as *Geometric Numerical Integration* (with E. Hairer and G. Wanner) and *From Quantum to Classical Molecular Dynamics*. His work emphasizes stability, accuracy, and computational efficiency in numerical methods for high-dimensional and oscillatory systems. Lubich's contributions span dynamical low-rank approximation, oscillatory Hamiltonian systems, and wave propagation in dispersive media. He has developed novel algorithms for tensor networks and matrix differential equations, addressing challenges in quantum many-body dynamics and stiff evolution problems. His research bridges numerical analysis with applications in physics, chemistry, and engineering. Key areas of exploration include: (1) structure-preserving integrators for long-time simulations, (2) error analysis of finite difference methods in semi-classical regimes, and (3) parallel algorithms for tensor-based computations. His work on pseudospectra and eigenvalue optimization has advanced stability analysis in linear dynamical systems. Lubich collaborates extensively on boundary element methods, finite element algorithms for evolving surfaces, and variational discretization techniques. His publications reflect a commitment to both theoretical rigor and practical computational solutions for complex physical systems.

Kersten SchmidtView profile

Researcher

• Computational Mathematics
• Partial Differential Equations
• Numerical Analysis
• +5 more

Kersten Schmidt is a Researcher at the Department of Mathematics, Technische Universität Darmstadt, specializing in Numerical Analysis and Scientific Computing. His work focuses on mathematical modeling of wave phenomena, including electromagnetism, acoustics, and coupled systems, with applications in photonics and engineering. He holds a PD (postdoctoral qualification) and has contributed to advancements in finite element methods, homogenization techniques, and boundary layer analysis. Research Interests Partial differential equations modeling wave propagation Numerical methods: Finite Element Methods (FEM), boundary element methods, high-order discretizations Asymptotic analysis and homogenization for multiscale problems Applications in acoustics, electromagnetism, and plasmonics Modelling of thin structures and interface conditions Publications Recent work includes studies on: Acoustic-structure interaction optimization Homogenization techniques for periodic structures Impedance boundary conditions for viscous acoustics Nonlinear eigenvalue problems in waveguide systems Efficient numerical methods for photonic crystals and plasmonic particles Affiliations Member of the Numerical Analysis and Scientific Computing group at TU Darmstadt Collaborations with institutions including École Polytechnique (France), University of Zagreb (Croatia), and TU Wien (Austria)

Marc Alexander SchweitzerView profile

Professor

• Numerical Methods
• Computational Mechanics
• Meshfree Methods
• +4 more

Marc Alexander Schweitzer is a Professor of Mathematics at the University of Bonn and affiliated with Fraunhofer SCAI. His work focuses on numerical methods and computational mechanics, particularly meshfree methods, peridynamics, and algebraic multigrid techniques. He contributes to advancing computational tools for engineering and materials science applications. Research interests include partition of unity methods, multiscale modeling, fracture mechanics, and efficient linear solvers. His work integrates mathematical theory with practical computational challenges, such as adaptive algorithms and parallel computing. Publications highlight contributions to peridynamic fracture models, scalable solvers for material design, and visualization of fracture progression in particle-based systems. Collaborative efforts with Fraunhofer SCAI emphasize industrial applications and algorithmic innovation.

Josh SheltonView profile

Lecturer

• Fluid Dynamics
• Asymptotic Analysis
• Heat Transfer
• +5 more

Dr. Josh Shelton is a Lecturer in Fluid Dynamics at the University of St Andrews, affiliated with the School of Mathematics and Statistics. His research focuses on asymptotic analysis, fluid dynamics, and numerical methods, with applications to heat transfer, wave phenomena, and geophysical flows. His work explores complex systems such as capillary waves, Rossby waves, and ice-crystal icing physics, employing advanced mathematical techniques like exponential asymptotics and singular perturbation theory. Collaborations with researchers like Prof. Trinh and Dr. Peters have yielded impactful contributions to computational fluid dynamics and boundary layer modeling. Recent publications (2022–2025) emphasize the interplay between theoretical analysis and numerical simulations, addressing topics such as enthalpy-based models for phase change, viscous dissipation effects in surface waves, and trapped equatorial Rossby waves. These studies highlight advancements in understanding nonlinear phenomena and free-surface dynamics. No scientific awards are listed, but his active research program includes collaborations with international teams and contributions to peer-reviewed journals like Journal of Fluid Mechanics and SIAM Journal on Applied Mathematics . Dr. Shelton advises no listed students but collaborates extensively on grant-funded projects involving asymptotic methods and fluid mechanics applications.

Alexander LitvakView profile

Professor

• Convex Geometry
• Random Matrix Theory
• Probability Theory
• +3 more

Alexander Litvak is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on convex geometry, random matrix theory, and probability theory with applications to high-dimensional geometry and stochastic processes. Education: He holds a Ph.D. from Tel Aviv University and an M.Sc. from Saint Petersburg State University. Research Interests: Litvak explores geometric probability, random structures, and their applications in optimization and functional analysis. His work often addresses questions related to convex bodies, matrix singularity, and the behavior of high-dimensional random systems. Key topics include minimal dispersion, random polytopes, and spectral properties of random graphs. Publications: His recent work (2020-2024) emphasizes probabilistic methods in convex geometry and random matrix theory, with notable contributions to linear bandits, sparse matrix analysis, and the circular law for digraphs. These studies reveal trends in understanding high-dimensional phenomena and probabilistic structures. Grants and Awards: No specific grants or awards listed in the provided materials. Advising and Labs: No student advisees or lab affiliations explicitly mentioned in the text.

Yannick SireView profile

Professor

• Partial Differential Equations
• Harmonic Analysis
• Geometric Analysis
• +4 more

Dr. Yannick Sire is a Professor of Mathematics and Director of Graduate Studies at the Department of Mathematics, Johns Hopkins University, affiliated with the Krieger School of Arts & Sciences. He holds a PhD from Institut National des Sciences Appliquees de Toulouse (France), with postdoctoral training at the University of Texas at Austin. His research focuses on partial differential equations, harmonic analysis, geometric analysis, and dynamical systems. Notable contributions include work on conformal geometry, fractional Laplacian operators, and nonlocal equations. Education: PhD, Institut National des Sciences Appliquees de Toulouse (France), 2005 Master's degree, Université Paul Sabatier (France), early 2000s Joint engineering degree (applied mathematics) and pure mathematics degree from Toulouse institutions Research Interests: Nonlinear elliptic and parabolic PDEs Geometric flows and conformal invariants Fractional operators and their applications Free boundary problems and variational methods Singular solutions and regularity theory Recent work emphasizes the interplay between geometric analysis and nonlocal equations, including studies on spinorial Yamabe problems, fractional Schrödinger operators, and critical phenomena in nonlinear heat equations. His research has implications for mathematical physics, geometric measure theory, and materials science. Grants & Academic Roles: Director of Graduate Studies at JHU Former faculty at Aix-Marseille University (2007–2020) Recipient of grants supporting research in geometric analysis and PDEs Labs/Teams: Active in JHU's Mathematics Department research groups focused on analysis and geometry, collaborating internationally on projects involving geometric PDEs and nonlocal operators.

Tim KuniskyView profile

Assistant Professor

• Computational Complexity of Statistical Problems
• Spectral Algorithms
• Random Matrix Theory
• +4 more

Tim Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with affiliations to the Department of Mathematics, Data Science and AI Institute, and Algorithms and Complexity Group. Previously, he was a postdoctoral associate at Yale University (2021–2024) and earned his PhD in Mathematics from NYU’s Courant Institute (2021), advised by Afonso Bandeira and Gérard Ben Arous. His research focuses on the computational complexity of statistical problems, spectral algorithms, random matrix theory, and convex optimization. He explores topics like the theoretical limits of algorithms, phase transitions in statistical estimation, and applications to data science. Key interests include: Computational thresholds for hypothesis testing Eigenvalue distributions in random matrices Algorithmic lower bounds via sum-of-squares methods Applications to community detection and compressed sensing Recent work includes advancements in low-coordinate-degree algorithms, statistical inference in directed graphs, and spectral methods for tensor analysis. He teaches courses such as Random Matrix Theory in Data Science and Probability Theory II at JHU. Upcoming engagements include workshops on combinatorics and theoretical computer science (BIRS, June 2025) and the COLT conference in Lyon (July 2025). His work bridges mathematics and computer science, addressing foundational questions in high-dimensional statistics and algorithmic efficiency.