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Lina von SydowView profile

Professor

• Computational Science
• Computational Finance
• Ice Sheet Modeling
• +2 more

Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.

Georgios ZourarisView profile

Professor

• Numerical Analysis
• Finite Element Methods
• Stochastic Partial Differential Equations
• +3 more

Georgios Zouraris is a Professor at the University of Crete, where he has maintained an active research profile since earning his Ph.D. from the same institution in 1995. His work is centered in the School of Science and Engineering, focusing on advanced computational mathematics with applications in physics and engineering. Education: Ph.D. in Mathematics, University of Crete, 1995 Professor Zouraris specializes in the development and rigorous analysis of numerical methods for partial differential equations. His research spans finite element and finite difference techniques for nonlinear Schrödinger equations, logarithmic heat equations, and stochastic PDEs with space-time white noise. Key contributions include error estimation frameworks for relaxation schemes, convergence analysis of Crank-Nicolson methods, and efficiency improvements for multilevel Monte Carlo simulations. His theoretical work consistently addresses singular nonlinearities and complex domain geometries, bridging mathematical rigor with computational practicality. Analysis of his 2020-2025 publications reveals a sustained focus on high-accuracy numerical schemes for challenging PDEs, particularly those involving logarithmic singularities and stochastic forcing. Recent work demonstrates increasing sophistication in handling noncylindrical domains and coupling strategies, with applications ranging from quantum systems to material science. The publications show consistent emphasis on provable convergence rates and computational efficiency. Information regarding student advising, research grants, and laboratory facilities is not documented in the available sources. His active publication record through 2025 indicates ongoing research leadership in computational mathematics.

Michele RuggeriView profile

Assistant Professor

• Numerical Analysis
• Partial Differential Equations
• Materials Science
• +6 more

Michele Ruggeri is an Assistant Professor of Numerical Analysis (RTD-B) at the Department of Mathematics, University of Bologna, and an Honorary Lecturer at the University of Strathclyde, UK. His research focuses on numerical analysis of partial differential equations in materials science and engineering, including liquid crystal theory, micromagnetism, nonlinear elasticity, and spintronics. He is also active in developing numerical methods for uncertainty quantification and model order reduction. Education: PhD in Technical Mathematics (TU Wien, Austria), Laurea Magistrale in Mathematics (University of Pavia, Italy), and a Diploma in Science and Technology from Scuola Universitaria Superiore IUSS, Italy. He holds a Fellowship (FHEA) from Advance HE and a PGCert in Learning & Teaching in Higher Education from the University of Strathclyde. His work emphasizes finite element methods and their applications, with contributions to computational micromagnetics and software development (e.g., Commics). He is affiliated with the (AM)² Research Center on Applied Mathematics and ARCES at the University of Bologna. Awards: Fellowship (FHEA), Advance HE, UK. Key Projects: Development of numerical methods for micromagnetic simulations, uncertainty quantification in stochastic models. Contact: m.ruggeri@unibo.it

Sebastiano BoscarinoView profile

Associate Professor

• Numerical methods for conservation laws
• Numerical methods for stiff problems
• Numerical methods for hyperbolic systems with relaxation terms
• +2 more

Sebastiano Boscarino is an Associate Professor of Numerical Analysis (MAT/08) at the Department of Mathematics and Computer Science, University of Catania, Italy. His research focuses on numerical methods for conservation laws , stiff problems , hyperbolic systems with relaxation terms , and kinetic problems , with a special emphasis on semi-Lagrangian methods. Education: Ph.D. in Applied Mathematics (2006, University of Catania), Laurea in Mathematics (2001, University of Catania) Research Projects: Coordinated national and international projects including PRIN 2017/2022 and European MODCLIM/MODCOMPSHOK. From 2016 to 2023, his publications highlight high-order semi-implicit and IMEX schemes for evolutionary PDEs , particularly in gas dynamics , Boltzmann equations , and shallow water models . Collaborations include institutions in the USA, South Korea, and Germany. He has organized international workshops and minisymposia at conferences like ICIAM, SCICADE, and ODS2018, and serves as a referee for journals such as SIAM Journal on Scientific Computing and Journal of Computational Physics. His teaching includes courses in Numerical Analysis and participation in PhD programs since 2017.

Giovanni RussoView profile

Professor

• Computational Fluid Dynamics
• Numerical Methods for Conservation Laws
• Numerical Methods for Kinetic Equations
• +5 more

Giovanni Russo is a Full Professor of Numerical Analysis at the Department of Mathematics and Computer Science, University of Catania, Italy . He coordinates the PhD program in Pure and Applied Mathematics and has been a visiting scholar at institutions including Courant Institute, University of California, Los Angeles, University of Michigan, and University of Bordeaux. His career spans over four decades across academia and research institutions. Education: PhD in Physics (1986, University of Catania), Laurea in Nuclear Engineering (1982, Politecnico di Milano). Research Interests: Russo specializes in Computational Fluid Dynamics , Numerical Methods for Conservation Laws , and Kinetic Equations . His work includes asymptotic preserving schemes , IMEX methods , semi-Lagrangian schemes , and high-order numerical techniques for PDEs with applications to fluid dynamics, plasma physics, and multiscale modeling. Scientific Trends: Recent publications focus on modeling epidemic dynamics , kinetic equations for inert mixtures , semi-Lagrangian methods , and uncertainty quantification in quantum systems. These works reflect his expertise in high-order numerical schemes , multiscale analysis , and applied mathematical modeling . Scientific Awards: CNR-NATO Fellowship (1987) Advising and Grants: Russo has supervised nine PhD students and served as Principal Investigator (PI) for major projects including MOSCOVID (modeling COVID-19) and ModCompShock (Horizon 2020 Marie Curie project). He has also organized international conferences like the 18th European Conference on Mathematics for Industry with 370 participants. Labs and Teams: Russo collaborates with research groups at the University of Catania and has been a visiting researcher at Courant Institute, University of Michigan, and GSSI L’Aquila. He contributes to journals as an editor and reviewer, including SIAM Journal of Numerical Analysis and Journal of Computational Physics .

Tommaso BuvoliView profile

Assistant Professor

• Scientific computing
• High performance computing
• Numerical analysis
• +3 more

Tommaso Buvoli is an Assistant Professor of Mathematics at Tulane University, specializing in numerical analysis, scientific computing, and high-performance computing. He holds a Ph.D. in Applied Mathematics from the University of Washington (2018) and dual B.S. degrees in Applied Mathematics and Computer Science from the University of Colorado Boulder (2013). His research focuses on developing novel numerical methods for solving complex differential equations arising in multiscale systems, including weather, combustion, and plasma simulations. Key projects include the Polynomial Time Integration Framework and Parallel-in-Time Methods for non-diffusive equations. Buvoli has secured significant grants, including NSF awards for constructing new time integrators. He actively contributes to academic service, co-organizing Tulane’s Applied and Computational Mathematics Seminar and the Mathematics Colloquium. His teaching spans courses like Scientific Computing and Ordinary Differential Equations. He has advised Master’s and undergraduate students, including Ben Stager, Garrett Gilliom, and Catherine Brooks. Notable software contributions include the Polynomial Integrator Package (PIPack) and Container Job Runner (CJR).

• Mathematical Physics
• Partial Differential Equations
• Computational Mathematics
• +7 more

Norbert Mauser is a full Professor in the Department of Mathematics at the University of Vienna, where he has been affiliated since 1999. His research bridges mathematical analysis, computational physics, and applied mathematics with a focus on developing and analyzing numerical methods for complex physical systems. Mauser's research interests center on mathematical physics, particularly partial differential equations arising in quantum mechanics and magnetism. His work spans Schrödinger-type equations, many-body quantum systems, micromagnetics, and more recently, the integration of machine learning techniques with physics-based modeling. He has made significant contributions to the mathematical analysis of quantum systems, numerical methods for micromagnetics, and computational approaches to Bose-Einstein condensates. His recent publications (2023-2025) reveal a growing emphasis on machine learning applications in micromagnetics, with multiple papers on physics-informed machine learning for magnetic energy minimization and spin wave dynamics. This represents an evolution from his earlier foundational work on Schrödinger equations and quantum systems toward more applied computational approaches that integrate AI with physical modeling. His research consistently demonstrates strong mathematical rigor combined with practical computational implementations. Mauser leads or participates in multiple significant research projects including 'Adaptive Splitting for Magneto-Hydrodynamics in Astrophysics' (2022-2026), 'Taming Complexity in Partial Differential Systems' (2017-2026), and 'Numerical simulation of A-type and white dwarf stars' (2021-2023). He has an extensive collaboration network across Europe, frequently working with researchers in computational physics and applied mathematics. His academic activities include organizing conferences such as 'Inverse-Design Magnonics' (2024) and presenting invited talks on absorbing boundary conditions for quantum wave equations. With over 80 publications spanning more than two decades, Mauser maintains an active research program that continues to evolve with contemporary challenges in computational mathematical physics.

• Extreme Value Theory
• Environmental Statistics
• Climate Modeling
• +3 more

Dr. Emma Eastoe is a Senior Lecturer in Statistics at the School of Mathematical Sciences, Lancaster University. Her research focuses on Extreme Value Theory and Environmental Statistics, with applications in climate modeling, oceanography, and environmental risk assessment. Current research projects include: Improved Models for Multivariate Metocean Extremes (IMEX, 2020–2021) STORi: Multivariate Oceanographic Extremes in Time and Space (2019–2023) Her work spans disciplines such as: Extreme value analysis of bivariate and multivariate processes Statistical downscaling of climate models Modeling non-stationary extremes in environmental data Applications to oceanographic, atmospheric, and glaciological systems She supervises PhD students including Kajal Dodhia and Aiden Farrell, and collaborates with research groups like the Data Science Institute (DSI) - Environment, Environmental and Ecological Statistics, and the STOR-i Centre for Doctoral Training.

Carl-Martin PfeilerView profile

Researcher

• Computational Micromagnetics
• Numerical Analysis
• Magnetic Materials
• +3 more

Carl-Martin Pfeiler is affiliated with TU Wien's Research Group Numerics of PDEs (E101-02-2). He holds academic qualifications including Dipl.-Ing. (Master of Engineering) and Dr.techn. (PhD in Technical Sciences). His research focuses on computational micromagnetics, numerical methods for partial differential equations (PDEs), and magnetic skyrmion dynamics. Key contributions include developing the mass-lumped midpoint scheme for skyrmion dynamics simulations and advancing IMEX-type integrators for the Landau-Lifshitz-Gilbert equation. Education: Dipl.-Ing. (Engineering) from TU Wien Dr.techn. (Technical Sciences PhD) from TU Wien Research interests emphasize computational approaches to magnetic phenomena , with a focus on: Numerical analysis of micromagnetic models Algorithm development for efficient simulations (e.g., Commics software) Study of topological spin textures like magnetic skyrmions Stability and convergence of numerical schemes Publications highlight advancements in: Nonlinear dynamics of skyrmions Efficient finite element methods Preconditioning strategies for iterative solvers Chiral skyrmion simulations Collaborations include work with Dirk Praetorius, Michele Ruggeri, and the Commics development team. His work bridges applied mathematics and materials science, addressing challenges in spintronics and nanomagnetism.

Jingwei HuView profile

Professor

• Kinetic Theory
• Multiscale Modeling
• Numerical Analysis
• +3 more

Jingwei Hu is a Professor in the Department of Applied Mathematics at the University of Washington, with an adjunct appointment in the William E. Boeing Department of Aeronautics & Astronautics. Her research focuses on numerical analysis, scientific computing, and kinetic theory, addressing challenges in multiscale modeling and computational methods for partial differential equations. She holds a B.S. in Computational Mathematics from Peking University (2006) and a Ph.D. in Applied Mathematics from the University of Wisconsin-Madison (2011), advised by Shi Jin. Her work emphasizes the development of efficient and accurate numerical schemes for kinetic equations, such as the Boltzmann and Fokker-Planck models, with applications in plasma physics, fluid dynamics, and image processing. She has pioneered dynamical low-rank methods, energy-conserving schemes, and structure-preserving algorithms for complex systems. Current research includes organizing a 2025 Summer School on Structure-Preserving Scientific Computing and co-editing topical collections in La Matematica and Kinetic & Related Models . Notable achievements include the NSF CAREER Award (2017), the Isaac Newton Institute Organiser Fellowship (2022), and editorial roles at SIAM Journal on Applied Mathematics and Networks and Heterogeneous Media . Her team includes PhD students and postdocs working on projects ranging from plasma simulation to multispecies collision models. She advises on grants related to computational methods and collaborates with institutions worldwide, contributing to both theoretical advancements and applied computational tools.

Juntao HuangView profile

Assistant Professor

• Computational Mathematics
• Applied Mathematics
• Numerical Analysis
• +6 more

Juntao Huang is an Assistant Professor at the University of Delaware , specializing in computational mathematics and applied mathematical modeling. His research bridges numerical analysis, machine learning, and physical systems, focusing on hyperbolic partial differential equations, kinetic theory, and high-order numerical methods. Dr. Huang's work emphasizes machine learning-based moment closures for equations in fluid dynamics, radiative transfer, and semiconductor physics. He develops implicit-explicit (IMEX) schemes for stiff hyperbolic systems and investigates bound-preserving discontinuous Galerkin methods in reactive flows. His publications highlight adaptive sparse grid algorithms and stability-preserving time integrators . His recent article trends include machine learning integration into kinetic models (BGK equations, Boltzmann transport), multi-scale hyperbolic systems , and thermodynamically stable PDE formulations . This aligns with his interest in non-equilibrium flow modeling and numerical methods for Vlasov-Maxwell equations in plasma physics. Dr. Huang's advising and grant information isn't present in the provided texts, but his technical contributions span from lattice Boltzmann methods for convection-diffusion equations to curved interface treatments in kinetic simulations. His work also explores information theory through generalized Kullback-Leibler divergences in Tsallis statistics.

• Numerical Analysis
• Partial Differential Equations
• Fluid Mechanics
• +8 more

Anna Martinez-Gavara is an Associate Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, Universitat de València. She is a member of the OPTIMATH research group, focusing on Optimization and Mathematical Modelling. Her research spans two major domains: applied numerical analysis for partial differential equations—particularly in developing high-resolution, well-balanced, and adaptive schemes for hyperbolic conservation laws and shallow water flows—and combinatorial optimization , where she applies metaheuristic methods such as tabu search, GRASP, and scatter search to problems like graph drawing, clustering, and diversity maximization. Her work integrates theoretical stability analysis with practical algorithmic design. The 15 most recent publications reflect a dual trajectory: early works (2008–2012) emphasize numerical methods for fluid dynamics and conservation laws, while more recent papers (2015–2023) pivot toward discrete optimization and metaheuristics. This evolution highlights her interdisciplinary approach, bridging numerical PDEs and operational research. Her collaborations include prominent researchers such as Rafael Martí, Rosa Donat, and Manuel Laguna, resulting in publications in high-impact journals including Journal of Computational Physics , Applied Numerical Mathematics , and European Journal of Operational Research . While no scientific awards are mentioned in the provided texts, her sustained publication record and active research up to 2023 indicate a strong and ongoing academic contribution. She has advised no publicly listed students in the data, and there is no mention of external grants or leadership of a lab or research team.

• Scientific Computing
• Element-Based Galerkin Methods
• Atmospheric and Oceanic Modeling
• +6 more

Welcome to my website. I am a Professor and founding member of the Scientific Computing group at the Naval Postgraduate School , with an Adjunct Professor appointment in Applied Mathematics at the University of California at Santa Cruz . My research focuses on Scientific Computing , particularly the development of continuous and discontinuous Galerkin methods for solving nonlinear partial differential equations in fluid dynamics. My group constructs numerical models for atmospheric and oceanic systems, emphasizing applications like hurricane simulations , multi-scale modeling , and GPU acceleration . We also work on projects such as NUMA (Navier-Stokes solver), xNUMA (multi-scale framework), and NUMO (ocean model). Recent trends in my publications include adaptive mesh refinement for tropical cyclones, entropy-stable formulations , GPU-accelerated atmospheric models , and dynamic LES stabilization . These works span nonhydrostatic modeling , microphysics implementation , and high-order spectral element methods applied to geophysical fluid dynamics. Emails: fxgirald@nps.edu , fgiraldo@ucsc.edu , frank.giraldo@gmail.com Students advised include Soonpil Kang, Yassine Tissaoui, Michal Kopera, Sohail Reddy, Felipe A. V. B. Alves, and others working on hurricane simulations, mesh refinement, and GPU acceleration.

Emanuele MACCAView profile

Researcher

• Numerical Analysis
• Numerical Modeling
• Numerical Simulation
• +5 more

Emanuele MACCA is a permanent researcher at the Department of Mathematics and Computer Science, University of Catania, specializing in Numerical Analysis. He holds a fixed-term RTD-a contract and focuses on developing efficient numerical schemes for simulating physical phenomena. Education Graduated with honors from University of Catania (2018) PhD in Mathematics and Computational Sciences from University of Palermo (2022) Research Interests His research centers on: Modeling hyperbolic systems with conservation laws Designing finite-difference, IMEX, and finite-volume schemes Simulating environmental/geophysical phenomena like shallow water systems Multiscale simulation techniques for complex systems He investigates benchmark problems (e.g., Sod shock problem) to validate numerical methods while addressing coastal evolution, sedimentation, and wave dynamics in estuaries. Grants & Awards Marie Curie pre-doctoral fellowship at University of Malaga (2019-2020) Research grant at University of Catania (2022-2023) Contact Office: 236 bis Phone: 095 7383201 Office hours: Mondays 10:00-12:00 (via email or Teams)

Hilary WellerView profile

Associate Professor

• Numerical Methods
• Atmospheric Modelling
• Adaptive Meshes
• +3 more

Dr. Hilary Weller is an Associate Professor in Meteorology and Computational Atmospheric Dynamics at the University of Reading's Department of Meteorology. Their research focuses on numerical methods for atmospheric modelling, including adaptive meshes, finite volume methods, and multi-fluid approaches for convection. Dr. Weller leads projects such as the Moving Meshes for Global Atmospheric Modelling initiative and contributes to the Gung Ho project for the UK's next-generation atmospheric model development. Research Interests: Numerical methods for atmospheric flows Adaptive mesh techniques Multi-fluid convection modelling Implicit time stepping for long time steps Mesh adaptation on spherical geometries Key Projects: Moving Meshes for Global Atmospheric Modelling (NERC-funded) Gung Ho: UK's dynamical core development Development of the AtmosFOAM framework for atmospheric simulations Advising: Current and past students include James Shaw (PhD on mountain representation), Amber te Winkel (implicit advection), and James Woodfield (conservative monotone schemes). Labs/Tools: Creator of AtmosFOAM, an OpenFOAM-based toolset for atmospheric modelling with capabilities in mesh redistribution and optimal transport.