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Frédéric GibouView profile

Professor

• Computational Science and Engineering
• Solid Mechanics, Materials & Structures
• Fluid Dynamics, Thermal Sciences & Fluid Mechanics
• +10 more

Frédéric Gibou is a Professor in the Department of Mechanical Engineering, Department of Computer Science, and Department of Mathematics at the University of California, Santa Barbara. He is also a core faculty member in the Computational Science and Engineering program. His academic journey began with a PhD in Applied Mathematics from UCLA, followed by post-doctoral research in the Departments of Mathematics and Computer Science at Stanford University. PhD in Applied Mathematics, UCLA Post-doctoral research, Stanford University (Mathematics and Computer Science) Professor Gibou's research sits at the interface between Applied Mathematics, Computer Science and Engineering Sciences, focusing on the design of high resolution computational methods for large scale computations. His work spans Computational Materials Science, Computational Fluid Dynamics, and Computational Image Analysis. The common thread across these applications is that they involve complex/free boundaries and similar classes of nonlinear partial differential equations. His group develops computational strategies on spatially adaptive grids for massively parallel environments, increasingly incorporating Machine Learning algorithms to solve forward and inverse problems. His research output shows a clear trend toward integrating traditional numerical methods with machine learning approaches, particularly for solving partial differential equations with complex interfaces. The publications reveal a strong focus on developing sharp interface methods, adaptive grid techniques, and novel computational paradigms that can handle multiscale phenomena across various scientific domains. Alfred P. Sloan Fellowship in Mathematics Regent's Junior Faculty Fellowship NSF Mathematical Sciences Postdoctoral Fellowship Robert Sorgenfrey Distinguished Teaching award Professor Gibou leads a multidisciplinary research group called Computational Applied Science Laboratory (CASL), which has strong collaborations with experimentalists at UCSB and worldwide. His group has received substantial funding from various agencies, enabling them to tackle challenging problems in computational science. CASL focuses on designing computational methods on Quad-/Oc-trees grids in the level-set formalism for solving previously intractable problems in science and engineering. The group's work spans Computational Materials Science (including nanostructured polymeric materials and high temperature multicomponent alloys), Computational Fluid Dynamics (including flow over superhydrophobic surfaces, flow in reactive porous media, and multiphase flows), and Computational Image Analysis (including image guided surgery and image segmentation).

• High-performance Computing
• Parallel Algorithms
• Scientific Computing
• +3 more

Professor Tobias Weinzierl holds the position of Professor in the Department of Computer Science at Durham University. He is the Co-director of the Institute for Data Science (IDAS) and leads the Scientific Computing research group. His expertise includes high-performance computing, parallel algorithms, and scientific computing, with a focus on numerical methods and adaptive mesh refinement techniques. Research interests encompass high-performance computing architectures, parallel algorithm design, and the development of scalable numerical solvers for hyperbolic partial differential equations (PDEs). He has contributed to projects like ExaHyPE, an engine for exascale simulations of wave phenomena, and has authored influential books such as *Principles of Parallel Scientific Computing* and *A Framework for Parallel PDE Solvers on Multiscale Adaptive Cartesian Grids*. He has held roles including inaugural director of the Master in Scientific Computing and Data Analysis (MISCADA) and has reviewed for the European High Performance Computing Joint Undertaking (EuroHPC JU). His work emphasizes resilience in numerical software, compiler optimizations, and energy-efficient computing. Key contributions include the Peano software framework for adaptive grid traversals, and research on task-based parallelism, GPU offloading, and fault tolerance in HPC systems. Current research focuses on exascale computing, multiscale optimisation, and the application of parallel computing to astrophysics and fluid dynamics. Labs/Teams: Scientific Computing research group, ExaHyPE project team Grants/Projects: PI/Co-I on ExCALIBUR projects, H&ES installations

Peter D. MinevView profile

Professor

• Computational Fluid Dynamics
• Numerical Analysis
• Fluid-Structure Interaction
• +4 more

Peter D. Minev is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, Canada. His research specializes in computational fluid dynamics, numerical methods for partial differential equations (PDEs), and large-scale simulations for scientific and engineering problems. He develops advanced algorithms for incompressible Navier-Stokes equations, fluid-structure interaction, multiphase flows, and magnetohydrodynamics (MHD). Education: MSc and PhD from Sofia University, Bulgaria. Research Focus: Minev's work spans: High-order time-stepping schemes and splitting methods for complex PDEs Fictitious domain approaches for fluid-structure interaction Parallel algorithms for supercomputing applications Modeling of multiphysics phenomena (e.g., porous media flows, phase-field equations) He has created benchmark results for 3D lid-driven cavity flows and simulations of particle sedimentation/bubble dynamics. Publications: His recent articles (2014–2021) focus on: Efficient splitting schemes for stress/vorticity formulations High-order adaptive time integration Algorithms for spherical/heterogeneous geometries Applications in fuel cells, geophysics, and biomechanics A consistent theme is minimizing computational complexity while maintaining accuracy.

Yanzhi ZhangView profile

Professor

• Fractional Partial Differential Equations (PDEs)
• Scientific Computing
• Numerical Analysis
• +6 more

Dr. Yanzhi Zhang is a Professor of Mathematics at the Department of Mathematics and Statistics, Missouri University of Science and Technology (Missouri S&T), where she has held academic positions since 2010. She earned a Ph.D. in Mathematics from the National University of Singapore and a B.Sc. in Applied Mathematics (with a Computer Science minor) from Chongqing University. Her research spans computational and applied mathematics, focusing on fractional PDEs, nonlocal models, machine learning applications in materials science and business, Bose-Einstein superfluids, and seismic wave inversion. Dr. Zhang has been awarded the Faculty Excellence Award from Missouri S&T and other accolades. Her work is supported by grants from the National Science Foundation, including projects on fractional viscoacoustic wave equations, Bose-Einstein superfluids, and dispersive wave equations. She advises Ph.D. students in interdisciplinary research, such as Yumeng Wang (machine learning for eye-tracking data) and Yixuan Wu (seismic inversion). Her research integrates mathematical rigor with computational innovation, addressing real-world challenges in geophysics, materials science, and business analytics. Collaborations with industry and academia enhance her contributions to data-driven modeling and open-source strategies for digital transformation.

Johannes KrausView profile

Professor

• Numerical Solution of Partial Differential Equations
• Finite Element Methods
• Isogeometric Analysis
• +8 more

Prof. Dr. Johannes Kraus is a faculty member at the University of Duisburg-Essen , affiliated with the Faculty of Mathematics . His research focuses on advanced numerical methods for partial differential equations and their applications across multiple disciplines. University: University of Duisburg-Essen Faculty: Faculty of Mathematics Contact: Thea-Leymann-Str. 9, 45127 Essen, Germany Dr. Kraus specializes in numerical solution of partial differential equations , discretization techniques (including finite element and isogeometric analysis), numerical linear algebra , and subspace correction methods like domain decomposition and multigrid. His work extends to high-performance computing and machine learning applications in medicine, engineering, and sciences. Recent publications highlight collaborations on nonlinear poroelasticity , space-time finite element methods , and preconditioning techniques for complex systems. Articles span domains such as biomechanics , biomolecular electrostatics , and multiscale modeling , demonstrating interdisciplinary impact. Teaching responsibilities include Numerical Mathematics II (Summer 2025) and Numerical Mathematics I , with seminars on multiphysics finite element software . He leads the Numerical Mathematics group and has secured third-party funding from DFG and FWF grants.

• Fluid Dynamics
• Computational Physics
• Complex Systems
• +3 more

Sauro Succi is a Researcher and Research Director at the Istituto dei Sistemi Complessi (CNR, Rome, Italy), concurrently serving as a Research Associate at Harvard University's Physics Department. He holds a visiting fellowship at the Freiburg Institute for Advanced Studies (FRIAS) within the School of Soft Matter Research. His expertise spans computational modeling of complex systems, with emphasis on fluid dynamics across scales—from quantum flows to biopolymer translocation and nanoscale phenomena. Education: BSc in Nuclear Engineering from the University of Bologna, PhD in Plasma Physics from École Polytechnique Fédérale de Lausanne (1987). He has held visiting positions at institutions including Yale, University of Paris, and Queen Mary College London. Research focuses on lattice Boltzmann methods, multiscale simulations, and soft matter physics. Key projects include modeling biological nanopore translocation, carbon nanotube gas flows, and micro-emulsion rheology. His work bridges mesoscopic theory with practical applications in biophysics and nanotechnology. Scientific recognition includes Fellow of the American Physical Society, Alexander von Humboldt Award, and Killam Award. His 200+ publications include seminal works on lattice Boltzmann equations and multiscale fluid dynamics.

• Quantum Computing
• Hybrid Quantum-Classical Algorithms
• Workflow Scheduling
• +3 more

Katarzyna Rycerz is a Lecturer at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, Poland. She holds the academic rank of Lecturer as part of the assistant professors group, with her office located at D-17, ul. Kawiory 21, room II, 3.54. Her research spans quantum computing, hybrid quantum-classical algorithms, and distributed systems, with significant contributions to workflow scheduling and complex network optimization. Dr. Rycerz's primary research focuses on quantum optimization techniques, particularly quantum annealing and variational algorithms applied to combinatorial problems. Her work bridges theoretical quantum computing with practical high-performance computing challenges, including load rebalancing in HPC systems and community detection in complex networks. She has developed software tools like QHyper for hybrid quantum-classical optimization and explored quantum solutions for thermoelectric properties in graphene and quantum game theory paradoxes. Her 15 most recent publications (2019-2025) reveal a dominant trend toward quantum-classical hybrid methods, with 80% focused on quantum annealing applications for workflow scheduling, network analysis, and optimization problems. Key themes include D-Wave system implementations, quantum game theory extensions, and thermoelectric material simulations, demonstrating a consistent shift from earlier multiscale simulation work toward cutting-edge quantum computing applications. No scientific awards are documented in the available information. Details regarding student advising, research grants, laboratory affiliations, or collaborative teams are not provided in the source materials.

Eran TreisterView profile

Assistant Professor

• Computational Science
• Scientific Computing
• Numerical Methods
• +8 more

Eran Treister is an Assistant Professor at the Ben Gurion University of the Negev in the Department of Computer Science. He completed his postdoctoral fellowship at the University of British Columbia (2014-2016) and earned his PhD from the Technion in 2014 under Prof. Irad Yavneh. His research spans computational science, numerical methods, and machine learning, with a focus on: Scalable algorithms for inverse problems Graph Neural Networks (GNNs) optimization Seismic and optical imaging via PDE solvers Low-precision deep learning acceleration Multilevel preconditioning techniques Recent work explores: Graph neural networks for PDEs with adaptive meshes Deep learning approaches to Helmholtz equation modeling 3D shape reconstruction via parametric level sets He serves on editorial boards: SIAM Journal on Scientific Computing (2024-) Copper Mountain Conference on Multigrid Methods (2025) International Conference on Machine Learning (ICML) as Area Chair (2025) Current teaching: Optimization Methods for Data Science (Spring 2025) Deep Learning Mini-Project (Winter 2024/5) Advanced Numerical Optimization (Spring 2025)

Florian De VuystView profile

Professor

• Fluid-Structure Interaction
• Scientific Machine Learning
• Reduced-Order Modeling
• +6 more

Florian De Vuyst is a Full Professor at the Department of Computational Engineering, University of Technology of Compiègne (UTC), where he conducts research at the Biomechanical Bioengineering Laboratory (BMBI UMR 7338). Previously affiliated with the Applied Mathematics Laboratory (LMAC), his work spans Fluid-Structure Interaction (FSI) , Scientific Machine Learning , GPU Computing , and Reduced-Order Modeling (ROM) . His research focuses on multimaterial flows , automotive crash dynamics , and bioengineering applications like microcapsule deformation in Stokes flows. His recent publications highlight machine learning integration with FSI simulations and GPU-accelerated solvers for compressible flows. Collaborations include Renault , Michelin , and ENS Paris-Saclay on projects like tsunami coastal impact modeling (DIGISCOPE EquipEx) and vehicle drag estimation . He has supervised 15+ PhD students, including Azzedine Tiba (non-intrusive ROM) and Vincent Mahy (multimaterial methods). Scientific Awards: Recipient of the Best Applied Paper Award at EGC 2008 Grants & Collaborations: Involved in CNRS Editions' interdisciplinary volume on tsunamis, ERCOFTAC symposia, and industrial partnerships

• Scientific Computing
• Applied mathematics
• Numerical methods
• +2 more

Marie E. Rognes is a Chief Research Scientist at Simula Research Laboratory's Numerical Analysis and Scientific Computing department. She specializes in computational mathematics and biomedical modeling, particularly focusing on cerebral fluid dynamics, electrodiffusion, and poroelasticity. Her work bridges advanced numerical methods with clinical applications in neuroscience. Research Pillars: Brain waterscape modeling, finite element methods, and biophysical simulations Software Leadership: Key contributor to FEniCS and Dolfin-adjoint projects Application Domains: Neurodegenerative diseases, cardiac electrophysiology, and personalized medicine Recent publications reveal methodological innovations in perivascular flow modeling , ionic transport simulations , and multi-scale brain mechanics . Her work on glymphatic system dynamics and cardiac tissue modeling demonstrates cross-disciplinary impact. While no explicit awards are listed, her extensive publication record in top-tier computational journals (SIAM, PLOS, Nature Computational Science) and invited talks at premier conferences (SIAM, ECCOMAS, FEniCS workshops) establish her as a leading figure in biomedical computing.

Daniel PeterseimView profile

Professor

• Computational Mathematics
• Numerical Analysis
• Multiscale Methods
• +6 more

Prof. Dr. Daniel Peterseim is the Chair of Computational Mathematics at the University of Augsburg, with a focus on numerical methods for partial differential equations and multiscale problems. He leads a research team including collaborators like Robert Altmann, Moritz Hauck, and Hannah Mohr. 2017–present: Chair of Computational Mathematics, University of Augsburg 2013–2017: Professor for Numerical Simulation, University of Bonn 2009–2013: Head of Junior Research Group, DFG Research Center Matheon & Humboldt University Berlin His research centers on computational multiscale methods, eigenvalue problems, and wave phenomena, with applications in mechanics, physics, and medicine. Recent work includes numerical stochastic homogenization and quantum computing applications of finite element methods. He has received the ERC Consolidator Grant for his research. Scientific awards: ERC Consolidator Grant He has advised numerous PhD and Master’s students, including Moritz Hauck, Fabian Kröpfl, and Barbara Verfürth. His team collaborates on projects like metamaterial simulations and microstructure reconstruction using neural networks.

• Computational Biomechanics
• Fluid-Structure Interaction
• Cardiovascular Disease Modeling
• +5 more

Michael Gee is a Professor at Technische Universität München (TUM), specializing in Mechanics on High-Performance Computers. His research focuses on computational biomechanics, fluid-structure interaction, and patient-specific modeling of cardiovascular systems, particularly abdominal aortic aneurysms (AAAs). He employs advanced numerical methods like algebraic multigrid and mortar contact formulations to address complex biomechanical challenges. Recent work highlights include the development of digital twin technologies for endovascular repair, integration of artificial intelligence in vascular diagnostics, and multiscale modeling of atherosclerosis. His publications emphasize high-performance computing applications in cardiovascular disease modeling, medical device simulation, and rupture risk assessment. Institution: Technische Universität München Department: Mechanics on High-Performance Computers Contact: gee@tum.de

• Medical Imaging
• Ultrasound Tomography
• Image Registration
• +4 more

Torsten Hopp is a Research Associate at the Institute for Data Processing and Electronics (IPE) at Karlsruhe Institute of Technology (KIT). His work focuses on advanced medical imaging technologies, particularly 3D ultrasound computer tomography (USCT) and its integration with MRI and X-ray modalities for breast cancer diagnosis. Research Interests: Ultrasound tomography system development Image registration algorithms Machine learning applications in medical imaging Biomechanical and computational modeling GPU-accelerated image reconstruction Multimodal imaging for clinical workflows Publication Trends: Torsten Hopp has contributed extensively to the evolution of 3D USCT, with recent work emphasizing deep learning integration , refraction correction , and patient-specific modeling . His research bridges hardware innovation (e.g., PtQube device) with computational methods for clinical translation. Labs & Teams: Affiliated with KIT's Institute for Data Processing and Electronics , he collaborates on projects like the High Data Rate Processing and Analysis Initiative , advancing ultrasound tomography for breast cancer screening.

Peter OswaldView profile

Professor

• Approximation Theory
• Function Spaces
• Applied Harmonic Analysis
• +7 more

Peter Oswald is a Professor and holds a Bonn Research Chair at the Hausdorff Center for Mathematics, affiliated with the Institute for Numerical Simulation at the University of Bonn. His research lies at the intersection of approximation theory, function spaces, and numerical methods for partial differential equations, with a focus on wavelets, splines, and multiscale computational techniques. University: University of Bonn School: Hausdorff Center for Mathematics Department: Institute for Numerical Simulation Email: oswald@ins.uni-bonn.de His research interests include Approximation Theory, Function Spaces and Applied Harmonic Analysis, Multiscale Methods in Scientific Computing, Numerical Methods for PDEs, Finite Elements, Splines, Wavelets, and Mathematical Modelling. These areas reflect a deep commitment to both theoretical foundations and computational applications in modern applied mathematics. The analysis of his recent publications reveals a consistent focus on iterative and subspace correction methods, preconditioning in sparse grid contexts, function space theory (especially Besov and Hilbert spaces), and the mathematical underpinnings of finite element and wavelet-based discretizations. His work often bridges pure and computational mathematics, with increasing exploration of stochastic and randomized algorithms in numerical linear algebra and high-dimensional problems. Peter Oswald has not been mentioned as having formal advisees in the provided texts, and no scientific awards are listed. However, his extensive collaboration with Michael Griebel and publication in prestigious journals and book series (e.g., Springer Series in Computational Mathematics) underscores his active and influential role in the mathematical community. He is involved in advanced research on space splittings, iterative solvers, and high-dimensional function approximation, contributing to both theoretical developments and practical computational frameworks. His work supports broader efforts in scientific computing, uncertainty quantification, and the numerical solution of complex physical models.

• Quantum computation
• Quantum algorithms
• Quantum simulation
• +3 more

Dr. Yuval Rishu Sanders is a Senior Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He is affiliated with the Centre for Quantum Software and Information (QSI), where he conducts cutting-edge research in quantum computing and quantum information theory. Dr. Sanders holds a PhD from the University of Waterloo, Canada, and has established himself as a prominent researcher in the quantum computing field with numerous high-impact publications. Dr. Sanders' academic journey began with a Bachelor of Science (First Class Honours) from the University of Calgary in 2008, followed by a Master of Science from the same institution in 2011. He completed his Doctor of Philosophy at the University of Waterloo in 2016. His career progression includes positions as a Research Associate at Macquarie University (2016-2021) and at UTS (2021-2022), before becoming a Permanent Faculty member at UTS in September 2022. Dr. Sanders' research focuses on the theoretical foundations of quantum computing, with particular expertise in quantum algorithms, quantum simulation, and quantum error correction. His work addresses fundamental questions about the computational advantages of quantum computers over classical systems, with a special emphasis on developing practical quantum algorithms for real-world applications. He is particularly interested in improving the efficiency and accuracy of quantum simulations, developing better methods for quantum state preparation, and establishing rigorous computational cost models for quantum algorithms. Dr. Sanders has made significant contributions to the field of quantum linear systems solvers, quantum measurement theory, and quantum resource theories. His research statement emphasizes the need for reliable computational cost analysis to determine when quantum computers will outperform classical computers for useful tasks. Analysis of Dr. Sanders' publication record reveals a consistent focus on advancing the theoretical underpinnings of quantum computing. His most recent work (2024-2025) centers on improving quantum simulation techniques through better product formulae, while his earlier work (2018-2022) demonstrates expertise across multiple quantum computing subfields including quantum algorithms for fermionic systems, quantum error characterization, and quantum measurement theory. A notable trend in his research is the development of more efficient quantum algorithms that reduce resource requirements while maintaining accuracy, which is crucial for near-term quantum applications. Dr. Sanders serves as an Associate Editor for the IEEE Transactions on Quantum Engineering since February 2023, demonstrating his standing in the quantum computing research community. His publication record includes articles in prestigious journals such as PRX Quantum, Physical Review Letters, and New Journal of Physics, with significant citation counts indicating the impact of his research. Dr. Sanders is actively involved in funded research projects that advance quantum computing theory and applications. His current projects include 'Building the Theoretical Foundation of Refinement Techniques for Quantum Programming' (2025-2027), 'The QB-suite: a framework for quantum algorithm design and benchmarking' (2024-2027), and 'Quarkov Decision Processes' (2023-2027). He has also contributed to significant projects such as 'Tools for fault-tolerant resource estimation' (2022-2025) and 'Defence acquisition optimisation using quantum algorithms' (2021-2024). His research vision includes developing software tools that can automate the analysis of quantum computations, potentially enabling a 100,000-fold speedup in the design iteration process for quantum applications. As a member of the Centre for Quantum Software and Information at UTS, Dr. Sanders collaborates with a multidisciplinary team of researchers working on various aspects of quantum computing. His research group focuses on developing theoretical frameworks and practical tools for quantum algorithm design, with particular emphasis on making quantum computing more accessible and efficient. Dr. Sanders' work bridges theoretical quantum computing with practical applications, contributing to the broader goal of realizing useful quantum advantage.