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Christoph PfrommerView profile

Professor

• Physical Cosmology
• Galaxy Formation and Evolution
• Plasma Astrophysics
• +6 more

Christoph Pfrommer is a University Professor at the University of Potsdam and Head of the Cosmology and High-Energy Astrophysics section at the Leibniz Institute for Astrophysics Potsdam (AIP). His research focuses on theoretical astrophysics spanning from plasma physics to cosmological scales, with particular emphasis on understanding galaxy and galaxy cluster formation and evolution. His educational background includes a Dr. rer. nat. from Ludwig-Maximilians-Universität München (2005), a Diploma in Physics from Friedrich-Schiller-Universität Jena (2002), and graduate studies at Harvard University (1999-2000). He completed his Privatdozent (Venia Legendi in Physics) at Ruprecht-Karls-Universität Heidelberg in 2014. Professor Pfrommer's research interests span three main areas: Physical Cosmology : Formation and evolution of galaxies, galaxy clusters, and large-scale structure in the Universe; interstellar, circum- and intergalactic medium and intercluster medium; physics of galactic feedback processes via galactic winds and jets in supermassive black holes. Plasma- and High-Energy Astrophysics : Cosmic ray acceleration and transport, magnetic fields: dynamo growth and saturation, plasma instabilities, radiative processes in galaxies and galaxy clusters; radio and gamma-ray astrophysics; propagation of relativistic gamma rays from blazars and pair creation; dark matter theory and indirect detection. Computational Astrophysics : Code development and high-performance computing with the unstructured moving mesh magneto-hydrodynamics code AREPO, the particle-in-cell (PIC) plasma code SHARP and the extended fluid-PIC code version with a Landau closure. His recent publications demonstrate a strong focus on cosmic rays, magnetic fields, and their impact on galaxy and cluster formation. His work often combines theoretical modeling with high-performance computational simulations to address fundamental questions in astrophysics. Scientific Awards and Recognition European Research Council (ERC) Advanced Grant 2020: PICOGAL (2022-2027) European Research Council (ERC) Consolidator Grant 2014: CRAGSMAN (2016-2021) Postdoctoral Fellowship at the Canadian Institute for Theoretical Astrophysics (2005-2008) Professor Pfrommer has received significant research funding through prestigious ERC grants, indicating the high impact and innovative nature of his research program. His work on cosmic rays and magnetic fields has positioned him as a leading researcher in these areas. As an educator, Professor Pfrommer has taught numerous advanced courses at the University of Potsdam, including Advanced Computational Astrophysics, Galaxies and Cosmology, and The Physics of Galaxy Clusters. His teaching emphasizes both theoretical foundations and practical computational methods, preparing students for research in modern astrophysics.

Christina G. TaylorView profile

Research Fellow

• Cut Mesh Methods
• Computational Hydraulics
• Flood Simulation
• +4 more

Christina G. Taylor is a Peter O'Donnell, Jr. Postdoctoral Fellow at the Oden Institute for Computational Science and Engineering, The University of Texas at Austin, where she conducts research in Dr. Clint Dawson's Computational Hydraulics Group. Her work focuses on developing cut mesh methods for moving interface problems in flood simulation and complex geometry modeling. Her educational background includes: Ph.D. in Computational Applied Mathematics and Operations Research, Rice University (Advisor: Dr. Jesse Chan) M.S. in Computational Applied Mathematics and Operations Research, Rice University (Advisor: Dr. Jesse Chan) Taylor's research spans the intersection of mathematics, computer science, and engineering, with primary expertise in: Cut Mesh Methods for Immersed/Embedded Boundary Problems High Performance Computing Discontinuous Galerkin Methods for Hyperbolic PDEs Computational Hydraulics and Flood Simulation Her recent publications (2021-2024) demonstrate consistent innovation in entropy/energy-stable numerical schemes, emphasizing high-order accuracy and robustness for real-world applications like wave propagation and flood modeling. These works bridge theoretical numerical analysis with practical software development for environmental engineering challenges. Her scientific recognition includes: Peter O'Donnell Jr. Postdoctoral Fellowship (2024) NSF Graduate Research Fellowship in Computational Mathematics (2021) Taylor has served as Instructor of Record for Rice University's graduate High Performance Computing course (CMOR 421/521) and maintains active industry collaboration through internships at Google, BP, and national laboratories. Her current research within the Computational Hydraulics Group integrates advanced numerical methods with large-scale simulation for water resources engineering.

Kevin VerbeekView profile

Assistant Professor

• Computational Geometry
• Graph Drawing
• Information Visualization
• +3 more

Kevin Verbeek is an Assistant Professor in the Applied Geometric Algorithms group at Eindhoven University of Technology (TU/e), affiliated with the EAISI Foundational research institute. His research focuses on computational geometry, information visualization, and algorithms for cartographic challenges. He holds a PhD from TU/e (2012) and conducted postdoctoral research at UC Santa Barbara (2012–2014). He coordinates the TU/e Honors Academy's 'Competitive Programming and Problem Solving' track and is a member of the Eindhoven Young Academy of Engineering. Education: PhD in Applied Geometric Algorithms, TU Eindhoven (2012) Postdoctoral Researcher, UC Santa Barbara (2012–2014) Research Interests: Algorithmic stability for dynamic data visualization Geometric algorithms for social networks and cartography Topological data analysis and computational topology Braided river network modeling Key Projects: NWO Veni Award-funded project on stable geometric algorithms (2015–present) GLAMMap: Interactive geo-visualization for cultural heritage metadata Teaching & Outreach: Coordinator of Honors tracks in Competitive Programming Instructor for courses like 'Heuristic Algorithms' and 'Computer Science Research Project' Awards: NWO Veni Award for 'Stable Geometric Algorithms' (2015) Labs/Teams: Active in EAISI Foundational, focusing on algorithmic spatial data analysis and foundational AI research.

• Computational Modelling
• Solid Mechanics
• Material Interfaces
• +5 more

Sonia Fernández-Méndez is a Professor (Catedrática de Universidad) at Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Civil and Environmental Engineering (DECA) and the Escola Tècnica Superior d'Enginyeria de Camins, Canals i Ports de Barcelona (ETSECCPB) . She also holds a lecturer position at the Facultat de Matemàtiques i Estadística (FME). Her research focuses on computational modeling in solid mechanics, material interfaces, electroactive materials, 4th-order PDEs, and advanced discretization methods like HDG and X-FEM. She leads the Laboratori de Càlcul Numèric (LaCàN) , a top research group in applied numerical methods. Education: Licenciatura en Matemàtiques (UPC, 1996), PhD in Applied Mathematics (UPC, 2001) under Antonio Huerta. Her awards include the Jacques Louis Lions Award 2010 and Juan Carlos Simó Award 2009. She has supervised 7 PhD students, including Rubén Sevilla and Alba Muixí. Her work bridges high-order methods with industrial applications in energy and materials science. Key contributions: Pioneered hybridizable discontinuous Galerkin (HDG) methods for fracture mechanics and flexoelectric metamaterials. Published extensively in Computer Methods in Applied Mechanics and Engineering , Journal of Scientific Computing , and International Journal for Numerical Methods in Engineering . Active in EU-funded projects on computational mechanics and energy materials.

William HenshawView profile

Professor

• Numerical Methods for Partial Differential Equations
• Overlapping Grids
• Adaptive Mesh Refinement
• +5 more

William Henshaw serves as the Margaret A. Darrin Distinguished Professor in Applied Mathematics at Rensselaer Polytechnic Institute, affiliated with the Department of Mathematical Sciences. He maintains active roles in the Center for Modeling, Optimization and Computational Analysis (MOCA), Center for Computational Innovations (CCI), and Scientific Computation Research Center (SCOREC). His academic credentials include: Ph.D. in Applied Mathematics, California Institute of Technology, 1985 B.Math (Hons) in Applied Math and Computer Science, University of Waterloo, 1985 Dr. Henshaw's research centers on numerical methods for partial differential equations , with pioneering contributions to overlapping grid techniques . His work encompasses adaptive mesh refinement , multigrid algorithms , and high-order accurate methods for fluid dynamics, solid mechanics, and electromagnetics. He has developed robust boundary conditions for conjugate heat transfer and fluid-structure interactions, and is the primary architect of the Overture framework —an object-oriented platform for solving PDEs on complex geometries. Analysis of his 2021-2025 publications reveals sustained innovation in high-order numerical schemes, particularly for wave propagation, Maxwell's equations in nonlinear media, and multi-physics problems. His work consistently addresses stability challenges at material interfaces and develops compatibility conditions for overset grids, demonstrating cross-disciplinary impact from electromagnetics to marine engineering. Dr. Henshaw leads the Overture project, which provides computational infrastructure for multi-domain simulations. His framework enables efficient solutions for problems requiring moving grids, adaptive refinement, and coupled physics, serving as a foundation for both academic research and industrial applications in computational science.

Jianming YangView profile

Professor

• Vibration and Dynamics
• Mechanical Engineering
• Fatigue Prediction
• +5 more

Jianming (James) Yang is a Professor in the Faculty of Engineering and Applied Science at Memorial University of Newfoundland. He holds a PhD from Tianjin University and has extensive industry experience as a mechanical design engineer, followed by academic roles at Guilin University of Electronic Technology and University of Shanghai for Science and Technology. His work focuses on mechanical vibration/dynamics, nonlinear random vibration analysis, and fatigue prediction in mechanical systems. Dr. Yang's research emphasizes modeling drillstring dynamics, fatigue control in drilling systems, and random vibration analysis of planetary gear trains in wind turbines. His expertise spans machine design, solid mechanics, and wind turbine simulation. Key contributions include developing empirical models for drilling performance prediction and stochastic linearization techniques for gear dynamics. His academic journey includes post-doctoral studies at Shanghai Jiao Tong University and a career transition from industry to academia in 2003. He has authored numerous publications on mechanical systems, vibration analysis, and renewable energy applications. Notable research trends include advancing predictive models for drilling efficiency, optimizing gear train reliability under random loads, and integrating machine learning for wind turbine performance analysis. Dr. Yang's work bridges theoretical mechanics and practical engineering challenges, contributing to advancements in energy systems and sustainable manufacturing processes.

Hongkai ZhaoView profile

Professor

• Applied and Computational Mathematics
• Inverse Problems
• Imaging
• +4 more

Professor Hongkai Zhao is a Chancellor’s Professor and Chair of the Mathematics Department at the University of California, Irvine (UCI), with a joint appointment in Computer Science. He holds a B.S. from Peking University, M.S. from USC, and Ph.D. from UCLA, all in Mathematics. His research focuses on computational and applied mathematics, including moving interface problems, inverse problems, imaging, and Hamilton-Jacobi equations. He is renowned for developing the Fast Sweeping Method (FSM) for solving hyperbolic PDEs efficiently. Education: B.S. (Peking University, 1990), M.S. (USC, 1992), Ph.D. (UCLA, 1996). Employment: UCI faculty since 1999, Szego Assistant Professor at Stanford (1996–1999). Research interests span computational fluid dynamics (moving interfaces), inverse problems (tomography, wave propagation), and imaging (medical imaging, computer vision). Key contributions include numerical methods for surfactant-laden drops, fast algorithms for Hamilton-Jacobi equations, and 4D cone-beam CT reconstruction. Awards include the Feng Kang Prize (2007), Sloan Fellowship (2002–2004), and Chancellor’s Professorship (2016–present). He has authored over 100 publications on numerical methods, inverse problems, and PDEs. Lab affiliations: Active in interdisciplinary research at UCI, leading projects in computational mathematics and imaging science. Current work includes high-frequency wave propagation, multi-scale modeling, and geometric data analysis.

Irene KyzaView profile

Lecturer

• Computational Mathematics
• Numerical Analysis
• Partial Differential Equations
• +3 more

Dr. Irene Kyza is a Lecturer in Computational Mathematics at the University of St Andrews' School of Mathematics and Statistics. Her research focuses on developing advanced numerical methods for solving complex partial differential equations arising in physical systems. Core research areas include: Structure-preserving algorithms for Schrödinger-type equations A posteriori error analysis for nonlinear evolution equations Adaptive numerical methods for blow-up detection Wave dynamics and instability phenomena Her publication portfolio demonstrates consistent focus on numerical analysis of wave phenomena, particularly Schrödinger equations and hydrodynamic models. Recent work emphasizes efficient approximation techniques for ocean wave modeling and stochastic sea states. She currently supervises PhD candidate Yilin Wang and contributes to research on adaptive time-stepping schemes and regularization techniques for singular solutions.

Peter K SwebyView profile

Associate Professor

• Numerical Analysis
• Computational Fluid Dynamics
• Hyperbolic Conservation Laws
• +5 more

Dr. Peter K. Sweby is an Associate Professor and Acting Head of the Department of Mathematics and Statistics at the University of Reading. He holds roles including Departmental Examinations Officer and MMath Project Coordinator. His professional affiliations include Chartered Mathematician (CMath), Fellow of the Higher Education Academy (FHEA), and Membership of the Institute of Mathematics and its Applications (MIMA). Dr. Sweby has a B.Sc. (1st class honours) in Mathematics from Bristol University (1979) and a Ph.D. in Mathematics from the University of Reading (1983). His research focuses on numerical methods for hyperbolic conservation laws, particularly Total Variation Diminishing (TVD) schemes, grid adaptation, sediment transport dynamics, and mathematical biology applications like cholesterol biosynthesis modeling. His work spans computational fluid dynamics, including adaptive mesh ALE methods and ghost fluid approaches for compressible flows. Key contributions include high-resolution schemes for bed-load sediment transport and box schemes for transcritical flow. His publications highlight advancements in numerical stability, spurious solution analysis, and morphodynamic modeling with data assimilation. Research Trends: Focus on numerical methods for hyperbolic systems, adaptive mesh techniques, and interdisciplinary applications in environmental and biological systems. Key Themes: Development of robust numerical algorithms, analysis of discretization dynamics, and integration of mathematical models with real-world phenomena. Dr. Sweby has supervised 14 PhD/MSc students, including work on flux limiter schemes, water wave scattering, and morphodynamic equations. He has led research on coastal sediment modeling and contributed to projects like 'Changing coastlines: data assimilation for morphodynamic prediction.' Grants/Labs: Involved with Numerical Analysis and Computational Modelling research groups, and contributed to EPSRC-funded morphodynamic studies. Labs/Teams: Active in the School of Mathematical, Physical and Computational Sciences, collaborating on interdisciplinary projects linking mathematics with environmental science and biology.

Domenico AmmendoleaView profile

Researcher

• Structural Engineering
• Fracture Mechanics
• Composite Materials
• +7 more

Domenico Ammendolea is a Research Grant Holder (Assegnista di Ricerca) in Structural Science at the Department of Civil Engineering, University of Calabria. He holds a Master’s degree in Civil Engineering (Structures) and a PhD in Environmental, Construction, and Energy Science and Engineering, both from the same institution, having graduated with top honors. He is actively involved in research and teaching, contributing to graduate courses such as Bridge Analysis and Design, Structural Modeling of Buildings, and Seismic Dynamics of Structures, while also serving as a mentor. His research focuses on advanced numerical modeling of damage and fracture in composite and quasi-brittle materials under static and dynamic loading. Key interests include moving mesh techniques, phase-field modeling, cohesive zone methods, and finite element analysis for simulating crack propagation in heterogeneous, anisotropic, and functionally graded materials. He applies these methods to cutting-edge materials like nano-filled ultra-high-performance concrete and CNT-reinforced composites. The recent publications highlight a strong trend in computational fracture mechanics, particularly in dynamic and multiscale crack propagation, utilizing adaptive and moving mesh strategies combined with integral methods for enhanced accuracy. His work bridges theoretical mechanics with practical structural applications. TC3 Young Scientist Award – Best contribution by a young researcher at the 8th International Conference on Crack Paths He has been actively teaching in master’s programs and mentoring students. Though no formal grants are listed, his research grant position and publications indicate active funding support. He collaborated remotely as a Visiting PhD Student at the University of Dundee (UK) in 2022, reflecting international engagement. His research is conducted within the Department of Civil Engineering at the University of Calabria, collaborating closely with researchers including Fabrizio Greco, Lorenzo Leonetti, Paolo Lonetti, and Arturo Pascuzzo. The work is integrated into broader research activities on structural integrity and advanced materials modeling.

Saumil PatelView profile

Researcher

• Computational Fluid Dynamics
• Meshing Techniques
• High-Order Numerical Methods
• +2 more

Saumil Patel is a Researcher at Argonne National Laboratory (ANL) and a graduate of the City College of New York (CCNY), where he earned his Ph.D. in Mechanical Engineering. He is affiliated with ANL's Mathematics and Computer Science (MCS) Division and Argonne Leadership Computing Facility (ALCF). Education: Ph.D. in Mechanical Engineering (CCNY), B.S./M.S. in Mechanical Engineering (prior to Ph.D.) His research focuses on Computational Fluid Dynamics (CFD) with an emphasis on high-order numerical methods, meshing techniques, and high-performance computing (HPC). He has developed applications like NekLBM and optimized workflows for CFD simulations using Arbitrary Lagrangian-Eulerian (ALE) methods. His recent work includes enabling in-situ visualization for the Exascale Computing Project, optimizing CFD codes for next-generation computing architectures, and integrating machine learning (e.g., TensorFlow) with CFD to enhance physical models of near-wall behavior. His research has been applied to internal combustion engine (ICE) simulations by domain scientists in academia and DOE labs.

Geordan GutowView profile

Assistant Professor

• Motion Planning
• Optimization and Search
• Space Robotics
• +5 more

Dr. Geordan Gutow is the R.L. Smith Assistant Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University (Michigan Tech), joining in Fall 2025. His research focuses on uncertainty-aware optimization-based methods for robotics, particularly in space robotics for in-space servicing and logistics. He emphasizes solving large-scale optimization problems on resource-constrained space computers through mathematical structure exploitation such as precomputation and parallelism. Education : PhD in Robotics, Georgia Institute of Technology (2022) B.S. in Mechanical Engineering, Johns Hopkins University Research Interests : Dr. Gutow's work spans motion planning, optimization algorithms, and Bayesian reasoning for robotics applications. Key areas include task and motion planning, generalized traveling salesman problems, and parallel algorithms for robotics. His recent projects address multi-agent collective construction, hierarchical planning, and ergodic exploration in complex environments. Article Trends : His publications emphasize optimization-based solutions to robotics challenges, with a focus on convex formulations, branch-and-bound methods, and uncertainty-aware path planning. Recent work includes advances in 3D obstacle navigation, multi-agent coordination, and efficient algorithms for space-grade computing constraints. Advising & Recruitment : Currently recruiting students at all levels with a passion for learning. No specific awards listed. Labs/Teams : Engaged in robotics research groups at Michigan Tech, though specific lab affiliations are not detailed in the provided text.

Josu AurrekoetxeaView profile

Research Fellow

• Gravitational Waves
• Black Holes
• Cosmology
• +1 more

Josu Aurrekoetxea is a Beecroft Fellow and extraordinary Junior Research Fellow (eJRF) at The Queen's College, University of Oxford, working within the Astrophysics sub-department of the Department of Physics. He joined Oxford in October 2021 after completing his academic journey that began in the Basque Country. Originally from Sestao, a historically industrial city in Greater Bilbao, he attended Saturnino de la Peña high school before earning his undergraduate degree in Physics at the University of the Basque Country. In 2016, he moved to London to pursue an MSc in Quantum Fields and Fundamental Forces at Imperial College London, followed by a PhD in Theoretical Physics at King's College London. Dr. Aurrekoetxea's research centers on gravitational waves, black holes, and cosmology, with particular expertise in numerical relativity. His work involves using high-performance computing methods to solve Einstein's equations of general relativity, simulating extreme gravitational phenomena such as black hole collisions. He explores how gravitational waves from catastrophic cosmic events can address fundamental questions about the universe's origins and composition. His research spans strong-field gravity regimes, vacuum decay phenomena, dark matter interactions with black holes, and primordial black hole formation. His publications reflect a strong focus on computational approaches to theoretical physics problems, with numerous papers on numerical relativity techniques, gravitational wave analysis, and cosmological simulations. His work often involves collaboration with leading researchers in the field and contributes to major projects like GRChombo, an open-source numerical relativity code. At The Queen's College, Dr. Aurrekoetxea serves as a Physics Tutor, teaching relativity to third-year undergraduate students. His academic position combines research excellence with teaching responsibilities, positioning him at the forefront of gravitational physics research while contributing to the education of future physicists. His laboratory work centers around the GRChombo project, an adaptable numerical relativity code for fundamental physics that enables high-performance simulations of Einstein's equations. This computational framework supports his research into strong-field gravity phenomena and has become a valuable resource for the broader gravitational physics community.

• Numerical Analysis
• Computational Fluid Dynamics
• Hyperbolic Partial Differential Equations
• +7 more

Peter Sweby is a faculty member in the Department of Mathematics and Statistics at the School of Mathematical, Physical and Computational Sciences, University of Reading. He has been a prominent figure in the field of numerical methods for fluid dynamics and mathematical modeling for several decades. Institution: University of Reading School: School of Mathematical, Physical and Computational Sciences Department: Department of Mathematics and Statistics His research interests lie at the intersection of applied mathematics and computational science, with a focus on developing and analyzing numerical schemes for partial differential equations arising in fluid mechanics and biological systems. He has made significant contributions to high-resolution methods, adaptive meshing, and the simulation of compressible flows and sediment transport. Over the past decade, his work has expanded into mathematical biology, particularly in modeling cholesterol biosynthesis pathways and plant growth dynamics. His publications show a consistent trend toward interdisciplinary applications of rigorous numerical techniques. Notable scientific contributions include work on the ALE adaptive mesh methods, ghost fluid methods for compressible flow, and multiscale biological models. He has been involved in collaborative projects related to coastal morphodynamics and data assimilation. Key Collaborators: M.J. Tindall, M.J. Baines, P.K. Sweby, J. Hudson, F. Pool Project Areas: Numerical Methods for Fluids, Cholesterol Pathway Modeling, Sediment Transport, Data Assimilation He has advised numerous students and researchers, as inferred from co-authored works with junior authors and PhD-level research content. While specific grant details are not listed, his sustained output suggests continuous research funding. He has also contributed to major conferences and special journal issues in numerical methods.

Jens LangView profile

Professor

• Numerical Analysis
• Computational Fluid Dynamics
• Adaptive Finite Element Methods
• +4 more

Prof. Dr. Jens Lang is a Professor at the Numerical Analysis Group within the Department of Mathematics at Technische Universität Darmstadt . His work focuses on adaptive numerical methods for partial differential equations (PDEs), particularly in computational fluid dynamics, uncertainty quantification, and optimal control.