جست‌وجوی استاد و پژوهشگر

Jon Wilkeningمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Methods
• Partial Differential Equations
• +۸ مورد دیگر

Jon Wilkening is a Professor in the Department of Mathematics at the University of California, Berkeley, with affiliations to Lawrence Berkeley National Laboratory (LBNL). He specializes in applied mathematics and numerical analysis, focusing on partial differential equations, water waves, and computational methods. Education: Not explicitly stated in provided texts Appointed in 2005 Collaborations: LBNL Research interests include: Water waves (3D standing waves, vortex sheets, mode-locked lasers) Boundary value problems with adjoint methods and shape optimization Dirichlet-to-Neumann operators in solid/fluid mechanics Numerical optimization, optimal transportation, and spectral deferred correction Least squares finite elements and interface problems in elasticity Perturbation methods and lubrication theory Publication trends show strong focus on computational fluid dynamics (water waves, vortex sheets), numerical methods (spectral, multigrid), and mathematical modeling of physical systems. His work spans partial differential equations, fluid mechanics, and applied optimization. Awards : NSF CAREER Award (2010-2015) Frederick A. Howes Scholar (2003) DOE Computational Science Fellowship (1997-2001) Flinn Foundation Scholarship (1992-1996) Teaching history includes courses like Math 156 (Numerical Analysis), Math 228B (Differential Equations), Math 118 (Fourier Analysis), and others from 2005-2025.

Siwei Liaoمشاهده پروفایل

پژوهشگر

• Computational Mathematics
• Complementarity Problems
• Numerical Methods

Siwei Liao is a Researcher at the University of Geneva , joining in December 2023 under the supervision of Martin Jakob Gander. She previously earned a Master of Science in Computational Mathematics from Lanzhou University between 2019 and 2022. Research Focus: Her work investigates complementarity problems , including horizontal linear complementarity problems, vertical complementarity problems, and tensor complementarity problems. She specializes in numerical methods like modulus-based methods, Levenberg-Marquardt methods, and multigrid methods, analyzing their convergence behavior and numerical performance.

Gonzalo Rubio Calzadoمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics
• High-Order Numerical Methods
• Discontinuous Galerkin Methods
• +۴ مورد دیگر

Dr. Gonzalo Rubio Calzado is a Associate Professor at the Department of Applied Mathematics to Aerospace Engineering , part of the Universidad Politécnica de Madrid (UPM) . He is affiliated with the Research Group: Numerical Methods and Applications to Aerospace Technology and the Center for Research in Computational Simulation (CCS) at UPM. Bachelor's in Aeronautic Engineering (UPM, 2009) Master's in Aerospace Engineering (UPM, 2011) PhD in Aerospace Engineering (UPM, 2015) His research spans fluid dynamics, high-order numerical methods, and machine learning applications in CFD, with over 50 publications and an h-index of 15 (last 5 years). He focuses on: Discontinuous Galerkin (DG) methods Error estimation and hp-adaptation Turbulence modeling and LES Machine learning for flow simulations Multiphase flow analysis Industrial applications (aeronautics, energy systems) Recent publications emphasize machine learning integration with high-order DG solvers, turbulence modeling, and optimization techniques. His work includes collaborations with companies like REPSOL and AIRBUS, as well as national and European projects (SIMOPAIR, DeepCFD, HERFUSE, ROSAS). Scientific awards include the Extraordinary PhD Award (2015). He is the lead developer of the open-source HORSES3D high-order CFD project and contributes to energy measurement patents for building efficiency.

Kent-Andre Mardalمشاهده پروفایل

استاد

• Computational Mechanics
• Biomechanical Applications in Medicine
• Brain Clearance During Sleep
• +۵ مورد دیگر

Kent-Andre Mardal is a Professor at the Department of Mathematics , University of Oslo . He specializes in computational mechanics with a strong focus on biomechanical applications in medicine , particularly in modeling brain clearance mechanisms during sleep. His work integrates multi-physics modeling , fluid-structure interaction , and poroelastic couplings to advance understanding of the glymphatic system and cerebrospinal fluid dynamics. Education: PhD (2002) – Simula Research Laboratory Research Interests: Mardal's research spans a wide range of disciplines including: Computational Mechanics – developing robust numerical algorithms for complex physical systems Biomechanical Applications – particularly in neuroscience and medical imaging Brain Clearance During Sleep – modeling the glymphatic system and CSF flow dynamics Multi-Physics Modeling – integrating fluid dynamics, elasticity, and neural networks Finite Element Methods – for accurate and efficient simulations Neural Networks in Scientific Computing – exploring physics-informed neural networks Research Trends from Publications: Mardal's recent publications (2022–2025) demonstrate a clear focus on brain fluid dynamics , particularly the glymphatic system , CSF circulation , and neurodegenerative disease modeling . He employs advanced numerical techniques such as isogeometric analysis , physics-informed neural networks , and parameter-robust preconditioning to solve complex multi-physics problems. His work bridges medical imaging (MRI) with computational modeling to provide insights into brain clearance mechanisms and their impairment in diseases like Alzheimer's. Scientific Awards: No specific awards are mentioned in the provided text. Grants and Projects: Currently, Mardal is the Principal Investigator (PI) of three active research projects: Alzheimer's Physics – exploring the role of fluid dynamics in neurodegeneration Scientific Machine Learning – advancing numerical methods with AI Computational Hydrology – modeling subsurface fluid flow Affiliations and Teams: Mardal was previously a group leader at the Centre of Excellence “Biomedical Computing” at the Simula Research Laboratory. He has authored over 100 papers and several books, and his research homepage is available at https://kent-and.github.io/ .

Philippe Delachartreمشاهده پروفایل

استاد

• Medical Imaging
• Signal Processing
• Image Processing
• +۷ مورد دیگر

Philippe Delachartre is a Professor at INSA Lyon (University of Lyon) in the Department of Electrical Engineering and researcher at CREATIS (Center for Research and Applications in Image and Signal Processing). He obtained his MS (1990) and PhD (1994) in Signal and Image Processing from INSA Lyon, joining the faculty in 1995 as Associate Professor before being promoted to Professor. His research focuses on medical image processing including: Advanced signal processing for ultrasound and MRI Motion estimation and segmentation algorithms Hypercomplex signal theory applications Deep learning for medical image analysis Real-time data acquisition systems With 30+ years of experience, he's contributed to over 100 publications. Recent publications (2018-2025) show strong focus on: Deep learning applications in medical signal classification Advanced segmentation methods (phase-field, CNN) Mathematical frameworks using hyperquaternions 3D ultrasound analysis for neurology and dermatology Cardiac motion estimation algorithms Research grants include: Regional project on emboli classification with deep learning (€170k) ANR LabCom project on Doppler ultrasound (€300k) Dermis characterization contract with Institut Pierre Fabre (€45k) Prostate segmentation project (€21k) Image denoising research (€100k) He has supervised 9 PhD students and leads research activities at CREATIS laboratory focusing on innovative medical imaging solutions.

• Nonsmooth elliptic and parabolic problems involving multiple scales
• Variational problems and geometric partial differential equations
• Subspace correction methods
• +۸ مورد دیگر

Prof. Dr. Ralf Kornhuber is a faculty member at the Freie Universität Berlin , affiliated with the Department of Mathematics and Computer Science and the Numerical Analysis of Partial Differential Equations institute. His research focuses on advanced numerical methods for multiscale problems and applications in geosciences, biomechanics, and materials science. Education: Dr. rer. nat. in Mathematics (1986) and Diploma in Mathematics (1979), both from TU Berlin. Academic Career: Senior Professor at Freie Universität Berlin (since 2023), Full Professor (C4) at the same institution (1998-2022), and prior roles at University of Stuttgart, WIAS Berlin, and TU Berlin. Research Interests: He specializes in adaptive finite element methods, subspace correction techniques, and multiscale modeling of partial differential equations (PDEs). His work addresses nonsmooth elliptic/parabolic problems, geometric PDEs, and domain decomposition strategies, with applications spanning geoscience simulations, biomechanics, and material science. Scientific Boards & Committees: Chair of CRC 1114 Scaling Cascades in Complex Systems (2021-2022), leadership roles in MATH+, Berlin Mathematical School, and Helmholtz Research School GeoSim, alongside editorial positions at journals like SIAM Journal on Multiscale Modeling and Simulation . Recognition: Awarded the International Multigrid Prize in 2022 for collaborative contributions to multigrid methods.

Jannis Teunissenمشاهده پروفایل

پژوهشگر

• Computational Plasma Physics
• Electric Discharge Simulation
• Scientific Computing
• +۲ مورد دیگر

Jannis Teunissen is a researcher in the Multiscale Dynamics group at Centrum Wiskunde & Informatica (CWI), the Dutch national center for mathematics and computer science. He also serves as a visiting lecturer at the Centre for mathematical Plasma Astrophysics at KU Leuven. Education: BSc in Physics & Astronomy and Master in Computational Science from University of Amsterdam PhD in computational plasma physics at CWI (obtained "cum laude") Postdoctoral research at KU Leuven's Centre for mathematical Plasma Astrophysics Dr. Teunissen's research focuses on computational plasma physics, particularly on simulating electric discharges. His work bridges theoretical modeling, computational methods, and experimental validation. He develops advanced computational techniques for studying streamer discharges, which are fast-moving ionized channels that form the first stage of sparks. These phenomena have important applications in environmental technology, high-voltage engineering, and atmospheric science. His research employs a range of computational methods including adaptive mesh refinement (AMR), plasma fluid modeling, particle-in-cell simulations, geometric multigrid solvers, and high-performance computing techniques. More recently, he has been applying machine learning methods to space weather research. Analysis of his publication history shows a strong focus on streamer discharge phenomena across different gas mixtures, with emphasis on macroscopic parameterization, electric field measurements, and radio emission calculations. Hershkowitz Early Career Award and Review (2024) from Plasma Sources Science and Technology Early Career Scientist Prize on Plasma Physics (2023) from IUPAP Student Award of Excellence of the Gaseous Electronics Conference (2015) PhD obtained "cum laude" (2015) Dr. Teunissen has been actively involved in several research projects including "Reliable nExt GENERation Actuation sysTEms (REGENERATE)" and "Plasma for Plants: Towards controlled and efficient plasma-activated water generation for a cleaner environment." His work has resulted in numerous publications focusing on streamer discharges in various gas mixtures, their radio emissions, electric field measurements, and computational modeling approaches. His research has significant implications for understanding natural phenomena like lightning and developing more environmentally friendly alternatives to traditional insulating gases used in high-voltage technology.

• Medical Imaging
• Tomography
• Radiation Physics
• +۵ مورد دیگر

Massimiliano Colarieti Tosti is an Associate Professor at KTH Royal Institute of Technology, working within the Division of Biomedical Imaging. His academic responsibilities include teaching multiple courses in medical imaging, radiation physics, and biomedical engineering. His research focuses on the intersection of medical physics, imaging technology, and computational methods for healthcare applications. Dr. Colarieti Tosti's research interests span several critical areas in biomedical imaging and medical physics. His work primarily focuses on medical imaging systems , particularly in tomographic reconstruction techniques for modalities like PET and SPECT. He has made significant contributions to radiation physics and radiation therapy , developing methods for proton range monitoring and gamma imaging. His research also extends to computational hemodynamics , where he applies patient-specific flow simulations to cardiovascular applications. Additional areas of expertise include radiation detection , scintillator development , and contrast agent technology for multimodal imaging. An analysis of his recent publications reveals a strong focus on advancing medical imaging technologies through innovative computational approaches. His work shows a clear trajectory from fundamental research in radiation detection and crystal physics toward increasingly clinical applications. Key trends include the development of advanced reconstruction algorithms for PET imaging, the application of computational fluid dynamics to cardiovascular diagnostics, and the creation of novel imaging hardware for radiation therapy monitoring. His research consistently bridges the gap between theoretical physics and practical medical applications, with increasing emphasis on patient-specific modeling and clinical validation. Dr. Colarieti Tosti has been actively involved in academic teaching and curriculum development. He serves as course responsible and examiner for several key courses including Ionising Radiation Imaging, Radiation Physics and Biology, and Radiation Therapy. His teaching portfolio demonstrates a commitment to training the next generation of medical engineers and physicists through courses covering 3D Image Reconstruction, Medical Imaging Systems, and Simulation Methods in Medical Engineering.