جست‌وجوی استاد و پژوهشگر

Tucker Carrington Jr.مشاهده پروفایل

استاد

• Theoretical Chemistry
• Computational Physics
• Quantum Mechanics
• +۹ مورد دیگر

Tucker Carrington Jr. is a Full Professor at Queen's University, cross-appointed between the Department of Chemistry and Physics, Engineering Physics & Astronomy. His research focuses on theoretical and computational approaches in molecular quantum physics. PhD from University of California, Berkeley Affiliated with the Condensed Matter Physics & Optics (CMPO) group Office: CHE409 (Chernoff Hall), Lab: CHE314 Research Interests: Developing new computational methods for studying quantum molecular dynamics, including: Computation of ro-vibrational spectra and reaction rate constants Machine learning for fitting potential energy surfaces High-dimensional quantum dynamics and sparse grids Spectroscopy of floppy molecular systems Contact: tucker.carrington@queensu.ca | 613-533-2552 | Lab Tel: 613-533-6000 ext. 74863

Mostafa Kavehمشاهده پروفایل

استاد

• Statistical Signal Processing
• Wireless Communications
• Image Processing
• +۳ مورد دیگر

Mostafa Kaveh is a Professor in the Department of Electrical and Computer Engineering at the University of Minnesota, College of Science and Engineering. His research spans statistical signal processing, wireless communications, image processing, and biomedical ultrasound imaging. He has held leadership roles including Department Head (1990-2005), Associate Dean for Research and Planning (2005-2018), and Dean of the College of Science and Engineering (2018-2022). His academic credentials include a B.S. and Ph.D. from Purdue University (1969, 1974) and an M.S. from UC Berkeley (1970). Kaveh’s research focuses on: Statistical signal processing for antenna arrays and mobile localization Wireless communication systems with multiple antennas and collaborative transmission Image restoration algorithms based on partial differential equations Medical image processing for tomography and reconstruction Signal processing applications in genomics and biotechnology Google Scholar publications highlight trends in array signal processing, direction-of-arrival estimation, wireless channel modeling, and medical imaging techniques. His work has influenced GRAPPA algorithms for MRI, space-time interference-canceling receivers, and genomic data analysis.

• Numerics of Partial Differential Equations
• Stochastic Perturbations
• Mathematics of Machine Learning
• +۳ مورد دیگر

Michael Feischl is a Professor for Computational PDEs at the Institut für Analysis und Scientific Computing, TU Wien. His research focuses on numerical methods for partial differential equations, stochastic perturbations, machine learning mathematics, computational micromagnetism, and optimal mesh refinement techniques. He leads the ERC Consolidator Grant project "New Frontiers in Optimal Adaptivity" (2024–2029), which aims to develop mathematically guaranteed optimal adaptive algorithms for time-dependent PDEs. Feischl's research interests include the numerical analysis of PDEs with stochastic components, computational micromagnetism (particularly the Landau-Lifshitz-Gilbert equation), and the intersection of machine learning with computational mathematics. His work emphasizes adaptive algorithms that maximize accuracy while minimizing computational costs. His publications demonstrate a strong emphasis on adaptive finite element methods, neural network theory, and computational micromagnetism. Recent articles explore optimal time-stepping, neural operator networks, and convergence analyses for complex physical systems. Trends include machine learning-enhanced numerical methods and rigorous mathematical foundations for computational physics. Awards: ERC Consolidator Grant: "New Frontiers in Optimal Adaptivity" (2024–2029) Feischl has held prior positions as Associate Professor at TU Wien (2019–2022), Professor at the University of Bonn (2018–2019), and Junior Research Group Leader at KIT (2017–2018). He obtained his doctorate from TU Wien in 2015.

Jan Hamaekersمشاهده پروفایل

پژوهشگر

• Numerical Algorithms
• High-Dimensional Problems
• Sparse Grids
• +۷ مورد دیگر

Dr. Jan Hamaekers is a researcher at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) in Bonn, Germany. He is affiliated with the development of advanced numerical methods for high-dimensional problems in computational science, particularly in quantum mechanics and molecular dynamics. His work is centered on efficient algorithms such as HCFFT, ESPACK, and TREMOLO, and he collaborates on major research projects funded by DFG and EU programs. Research Interests: Development of hierarchical methods for high-dimensional problems Sparse grid and fast Fourier transform techniques Quantum mechanical and molecular mechanical (QM/MM) methods Numerical algorithms for the electronic Schrödinger equation Parallel software for nanoscale dynamics simulations His recent publications reflect a strong trend in computational materials science, multiscale modeling, and machine learning applications in chemistry. He has contributed to software packages like TREMOLO and ATK-ForceField, and his work bridges applied mathematics, physics, and engineering. He teaches practical labs in numerical simulation, indicating an active academic engagement. Scientific Projects: Molecular Dynamics Simulations for Material Science Applications CODICE - Computationally Driven design of Innovative Cement-based materials (EU FP7) Development of efficient algorithms for nanoscale dynamics simulations Multiscale QM/MM simulations of nanotubes and composites (DFG) Dr. Hamaekers has advised or collaborated with multiple researchers, as seen in his extensive co-authorship. He has not received any explicitly mentioned awards in the provided text. He is involved in software development and algorithmic innovation, with a focus on scalability and efficiency in scientific computing.

Paresh Dateمشاهده پروفایل

استاد

• Mathematical Finance
• Nonlinear Filtering
• Power Systems Optimization
• +۳ مورد دیگر

Professor Paresh Date is a faculty member in the Department of Mathematics at Brunel University London, College of Engineering, Design and Physical Sciences. He holds a PhD from the University of Cambridge and an MTech from the Indian Institute of Technology Mumbai. His research focuses on Mathematical Finance , Nonlinear Filtering , and Power Systems Optimization , with applications in financial portfolio modeling, energy market forecasting, and stochastic control systems. He authored the book 'Nonlinear Estimation: Methods and Applications with Deterministic Sample Points' (Taylor & Francis, 2019). Recent publications analyze exchange rate modeling via Kalman filters, basket option pricing, wind power risk hedging, and sparse-grid filtering techniques. His work combines financial engineering with mathematical control theory, often addressing measurement delay and uncertainty. Scientific awards include Fellow of the Institute of Mathematics and its Applications Teaching includes Year 1 Calculus (2014-2021), Year 2 Analysis (2017-2018), and Financial Mathematics MSc courses on interest rate theory and financial markets (2018-2022). He has supervised 11 PhD and 3 MPhil students to completion.

• Computational Chemistry
• Heterogeneous Catalysis
• Chemical Kinetics
• +۶ مورد دیگر

G Richard Olds is an Adjunct Professor affiliated with the Department of Pathology and Laboratory Medicine at Brown University. His academic background includes an MD from Case Western Reserve University (1976) and a BS from Willamette University (1972). Education: 1976 MD, Case Western Reserve University 1972 BS, Willamette University Dr. Olds specializes in computational chemistry and heterogeneous catalysis, with a focus on microkinetic modeling, automated reaction mechanism generation, and thermodynamic accuracy in adsorbate systems. His work bridges chemical engineering, surface science, and environmental chemistry, particularly in PFAS degradation and CO2 conversion processes. His research includes 15 recent publications (2024–2025) on topics such as: Transition state theory for surface reactions Coverage-dependent catalytic barriers Automated microkinetic model generation Thermochemistry of adsorbates Environmental treatment of perfluorinated acids CO2 desorption and surface diffusion These studies often employ advanced computational tools like Reaction Mechanism Generator (RMG), Cantera, and phase-space integration methods.

• Fractional Calculus
• Numerical Analysis
• Partial Differential Equations
• +۴ مورد دیگر

Professor Suzan Cival Buranay is a faculty member at the Faculty of Arts and Sciences, Eastern Mediterranean University, serving as Department Head in Mathematics. She earned her PhD in Mathematics (2007), MS in Applied Mathematics (1995), and BS in Applied Mathematics (1993) from the same university. Research Interests: Her work focuses on Fractional calculus and its applications to differential equations Numerical analysis of finite difference schemes and hexagonal grids Approximation theory with modified Bernstein-Kantorovich operators Preconditioning techniques for linear systems and ill-posed problems Computational methods for heat equations and Volterra integral equations Recent Contributions: She has developed high-accuracy numerical methods for fractional boundary value problems, Caputo derivatives, and singular integral equations. Her work on hexagonal grid approximations for heat equation derivatives has advanced computational efficiency. Thesis Supervision: She has advised 5 PhD students and 6 MS students, with ongoing supervision of two PhD/MS theses. Her supervised works include collocation methods for fractional equations and implicit schemes for heat equations. Projects: Currently leading a project on collocation methods for generalized Bagley-Torvik equations (2023-2024), and previously worked on block-grid methods for Laplace equations with singularities (2010-2011).

• Numerical Analysis
• Computational Mathematics
• Sparse Signal Processing
• +۴ مورد دیگر

Dr. Toni Volkmer is a researcher affiliated with the Faculty of Mathematics at Chemnitz University of Technology. His work focuses on computational mathematics and signal processing, particularly in high-dimensional data analysis and sparse Fourier transforms. His research intersects numerical analysis, approximation theory, and algorithm design for efficient data processing. Key research areas include Sparse spectral estimation Rank-1 lattice sampling Multivariate function approximation High-dimensional data analysis Fast matrix-vector operations Graph Laplacian computations His publications demonstrate expertise in developing sublinear-time algorithms for harmonic analysis and creating efficient numerical methods applicable to modern data science challenges. While no formal awards are listed, his contributions to scientific computing have been documented in multiple peer-reviewed publications since 2012.

• Approximation Theory
• Sampling Theory
• Function Spaces
• +۳ مورد دیگر

Dr. Yurii Kolomoitsev serves as a Research Fellow at the Institute of Numerical and Applied Mathematics, University of Göttingen. His research focuses on approximation theory and sampling processes, with active involvement in multiple DFG-funded projects including "Spezielle Glattheitsmaße und Funktionenräume für Sampling und Interpolation" (2025-2027, project KO 5804/3-1) and the Research Training Group 2088 "Discovering structure in complex data" (2015-2024). His primary research interests span approximation theory, sampling theory, function spaces, Fourier analysis, compressed sensing, and inverse problems. Kolomoitsev investigates fundamental properties of quasi-interpolation operators, moduli of smoothness, and Lebesgue constants, with significant contributions to multivariate approximation and high-dimensional computational methods. His work bridges theoretical analysis with practical applications in data science and signal processing. Analysis of his 15 most recent publications (2020-2023) reveals consistent focus on asymptotic behavior in approximation processes, particularly examining error estimates in Lp spaces, sparse grid methods, and the interplay between function smoothness and approximation quality. Key trends include the extension of classical results to non-standard spaces (0 Dr. Kolomoitsev holds substantial research funding through the German Research Foundation (DFG), currently leading project KO 5804/3-1 and participating in RTG 2088 until 2024. He actively contributes to the academic community through workshop organization including the annual RTG Workshop series and specialized events on inverse problems and clinical MRI data analysis. His teaching portfolio includes graduate-level courses such as "An Introduction to Compressed Sensing" (2023/24) and "Fourier Analysis and Approximation" (2022).

• Numerical Analysis
• Sparse Grids
• Machine Learning
• +۳ مورد دیگر

Prof. Dr. Michael Griebel is a leading academic at the Institute for Numerical Simulation (INS) at the University of Bonn , Germany. His work focuses on numerical methods for partial differential equations (PDEs), sparse grid techniques, and applications in computational science, machine learning, and materials modeling. Research Interests : Numerical analysis, sparse grids, high-dimensional approximation, tensor product methods, stochastic processes, and multiscale simulations. Collaborations : Co-author of over 50 publications with researchers in computational mathematics, fluid dynamics, and quantum chemistry. Prominent research trends include advancements in sparse grid methods for uncertainty quantification, kernel-based regression, and efficient solvers for ill-posed problems. His work on space-filling curves and domain decomposition enables scalable parallel algorithms for complex simulations. Scientific Contributions : Editor of 10+ volumes in Lecture Notes in Computational Science and Engineering . Co-developer of the EXAHD exascale sparse grid framework for plasma physics simulations. Foundational work on tensor product spaces and their applications to electronic structure theory. Innovations in adaptive wavelet solvers and parallel multigrid methods. Software and Projects : Key developer of sparse grid solvers and adaptive algorithms. His INS Preprint Series archives over 200 technical reports, reflecting his group’s impact on computational methods.

Andrea Scaglioniمشاهده پروفایل

پژوهشگر

• Uncertainty Quantification
• Partial Differential Equations
• Computational Mathematics
• +۳ مورد دیگر

Andrea Scaglioni is a Postdoctoral Researcher in computational mathematics at the University of Vienna since October 2024, working under Professor Ilaria Perugia. He received his Ph.D. from the Technical University of Vienna (TU Wien) in October 2024, focusing on stochastic dynamics in micromagnetic systems, and holds a Master's degree in Computational Science and Engineering from EPFL (Switzerland, 2019). His academic background includes: Ph.D. in Computational Mathematics, Technical University of Vienna (TU Wien), 2024 Master's in Computational Science and Engineering, EPFL, Switzerland, 2019 Scaglioni specializes in Uncertainty Quantification for Partial Differential Equations using high-dimensional interpolation methods like Sparse Grid Interpolation to construct surrogate models. His research targets stochastic micromagnetics, particularly the Landau-Lifshitz-Gilbert equation, with applications in modeling magnetic hysteresis phenomena. He actively contributes to Austria's numerical analysis community through conferences and collaborations within Prof. Perugia's research group. His recent presentations include talks at the Annual ÖMG-DMV Meeting (Linz, September 2025), the International Symposium on Hysteresis Modeling and Micromagnetics (Torino, May 2025), and workshops on stochastic PDEs at TU Wien (September 2024). These highlight his focus on surrogate modeling, reduced-order methods, and sparse grid techniques for stochastic dynamics in magnetic systems.

Jérémie Gaidamourمشاهده پروفایل

پژوهشگر

• Quantum Mechanics
• Numerical Methods
• High Performance Computing
• +۵ مورد دیگر

Jérémie Gaidamour is a CNRS Research Engineer at the Institut Élie Cartan de Lorraine (University of Lorraine), specializing in high-order numerical methods and high-performance computing (HPC) for quantum mechanics simulations. His work focuses on parallel solvers for large sparse linear systems, including hybrid direct-iterative methods and algebraic multigrid (AMG). PhD Thesis: "Conception d'un solveur linéaire creux parallèle hybride direct-itératif" (2009, Université de Bordeaux I) His research interests span Quantum Mechanics (Bose-Einstein condensates via Gross-Pitaevskii equations), Numerical Methods (pseudo-spectral techniques, domain decomposition), and High-Performance Computing (MPI, parallel algorithms). Recent publications highlight advancements in fractional gradient flows and energy-minimizing AMG preconditioners for nonlinear Schrödinger equations. Software development projects include BEC2HPC (parallel spectral methods for BECs), MueLu (AMG solver in Trilinos), and HIPS (hybrid direct-iterative sparse solver). He has contributed tutorials on HPC tools (MPI, OpenMP, GPU/Xeon Phi) and participated in Grid'5000/IDRIS support teams.

• Meteorology
• Climatology
• Atmospheric Physics
• +۶ مورد دیگر

Víctor Homar Santaner is a University Professor in the Physics Department at the University of the Balearic Islands (UIB), specializing in meteorology and atmospheric physics. His research focuses on high-impact weather phenomena in the Mediterranean region, particularly severe storms, flash floods, and climate change implications. Member of European Severe Storms Laboratory (2009-2012) Associate Editor of Atmospheric Research Participated in HyMeX international program His work integrates numerical weather prediction (NWP) models, ensemble forecasting systems, and data assimilation techniques (e.g., Ensemble Kalman Filter) to analyze predictability limits and improve early warning systems. He has contributed to understanding climate change impacts on tourism, ecosystems, and human health, emphasizing the need for adaptation strategies. Recent publications reveal expertise in: Ensemble forecasting for Mediterranean weather Hydrological modeling of flash floods Climate projection bias correction Storm-scale data assimilation Maritime convection dynamics He supervises two doctoral students and has coordinated multiple research projects with over €750k in funding. His scientific output includes 65+ peer-reviewed articles and 20+ book chapters.

Robert Barthorpeمشاهده پروفایل

مدرس ارشد

• Dynamics
• Structural Health Monitoring
• Energy Applications
• +۳ مورد دیگر

Dr. Robert Barthorpe is a Senior Lecturer in the Department of Mechanical Engineering at the University of Sheffield, affiliated with the School of Mechanical, Aerospace and Civil Engineering. His research bridges structural dynamics and energy systems, focusing on developing advanced methodologies for data integration in engineering challenges. Education: MEng in Mechanical Engineering with German (University of Sheffield, 2005), PhD (University of Sheffield, 2010) Rob's research centers on two primary domains: Structural Health Monitoring (SHM): Pioneering techniques like model validation, uncertainty quantification, and machine learning to address data-scarcity challenges in damage identification. Energy Applications: Investigating distributed thermal/electrical storage systems and hierarchical control methodologies (incorporating model predictive control) for low-carbon energy grid optimization. Key publication trends reveal expertise in: Digital twins and real-time updating strategies Probabilistic risk-based SHM frameworks Hybrid physics-data modeling approaches Uncertainty quantification in composite structures Scientific contributions include: EPSRC/Wellcome Trust Doctoral Prize Fellowship Collaboration on £556k Horizon 2020 DyVirt project £4.15M EPSRC grant for structural dynamics lab verification £3.2M ERDF funding for Laboratory for Verification & Validation Teaching activities include Control Engineering (MEC321) for mechanical engineering undergraduates. His work has been published in premier journals like Mechanical Systems and Signal Processing and presented at major conferences including ISMA and EWSHM.

Leopold Haimbergerمشاهده پروفایل

استاد

• Atmospheric General Circulation Diagnostics
• Radiosonde Data Homogenization
• Numerical Weather Prediction
• +۴ مورد دیگر

Leopold Haimberger is a Professor at the University of Vienna , affiliated with the Institute of Meteorology and Geophysics under the Faculty of Earth Sciences, Geography and Astronomy . He has been actively involved in teaching courses such as Climate Dynamics and Diagnostics , Data Assimilation and Ensemble Methods , and Scientific Communication . His research focuses on atmospheric general circulation diagnostics, radiosonde data homogenization, and numerical weather prediction, with significant projects including Arctic energy budget analysis and European climate reanalysis initiatives. Research Themes Atmospheric transport mechanisms Climate model validation Ocean-atmosphere interactions Historical climate data recovery Recent Publications 2024: StraitFlux for ocean modeling 2024: Radiosonde position correction 2023: North Atlantic heat flux trends 2023: Indonesian Throughflow validation Scientific Collaborations Working with international teams on CMIP6 models Leading Arctic climate analysis projects Contributing to global radiosonde homogenization