جست‌وجوی استاد و پژوهشگر

Wenchang Luمشاهده پروفایل

استاد پژوهشی

• Materials Science
• Ferroelectric Polymers
• Electronic Structure Calculations
• +۵ مورد دیگر

Wenchang Lu is a Research Professor in the Department of Physics at NC State University, affiliated with the College of Sciences. His research focuses on materials science, computational physics, and nanomaterials engineering. He specializes in ferroelectric polymers, electronic structure calculations, and advanced material design using methods like Density Functional Theory (DFT) and the Real-Space Multigrid (RMG) code. His work explores topics such as dielectric enhancement in polymer nanocomposites, strain-induced polar interfaces, and graphene nanoribbon synthesis. He has contributed to advancements in high-performance capacitor materials and nanoscale electronic devices, with a particular emphasis on energy storage and electromechanical systems. Recent publications highlight studies on ultrahigh electromechanical coupling in relaxor ferroelectrics and the development of scalable computational methods for exascale architectures. His research bridges theoretical modeling with experimental applications, aiming to engineer next-generation materials for energy, electronics, and sensor technologies.

Thomas Huckleمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Scientific Computing
• Parallel Computing
• +۷ مورد دیگر

Prof. Thomas Huckle is a Professor of Scientific Computing at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. His research focuses on numerical linear algebra, parallel computing, and their applications in physics and computer science. Key interests include solving linear problems on parallel architectures, image processing, multigrid methods, preconditioning, and tensor-based high-dimensional problem approximation. Education: Studied mathematics and physics at the University of Würzburg (diploma in mathematics, 1985 PhD, 1991 habilitation). Professional History: DFG-funded research at Stanford University (1993–1994), appointed to TUM in 1995, and member of the Mathematics Department since 1997. Research Interests: Prof. Huckle’s work spans numerical methods for large-scale systems, including structured matrices, regularization techniques, and quantum computing applications. He develops algorithms for parallel computing environments and contributes to software tools like ELPA for eigenvalue problems. Grants and Labs: Engaged in projects such as the ELPA-AEO eigensolver and ESSEX-II initiatives. Active in the SCCS (Scientific Computing and Computational Science) group at TUM, focusing on high-performance computing and numerical methods.

Eddie Wadbroمشاهده پروفایل

استاد

• Topology Optimization
• Inverse Problems
• Large-Scale Optimization
• +۷ مورد دیگر

Eddie Wadbro is a Professor of Mathematics at Karlstad University , Sweden. His research focuses on design optimization , inverse problems , and large-scale computational methods , with applications in acoustic device design , fluid dynamics , and mechanical engineering . He leads projects in topology optimization of antennas, loudspeakers, and acoustic systems, often involving multi-physics simulations. Key contributions include: Development of minimum-size control techniques for topology optimization Investigation of waveguide acoustic black holes for enhanced wave focusing Optimization of superhydrophobic surfaces to delay turbulent transition in boundary layers Integration of deep reinforcement learning for real-world control systems His work bridges applied mathematics and engineering , with publications in high-impact journals and collaborations across disciplines like computational fluid dynamics and machine learning.

• Reversible Programming
• Compiler Construction
• Functional Programming
• +۳ مورد دیگر

Prof. Dr. Uwe Meyer is a faculty member at Technische Hochschule Mittelhessen (THM), where he serves as the Head of the Computer Science BSc program and Deputy Managing Director of the Institute for Programming Languages and their Application. He has held leadership roles in international conferences such as Program Chair of the 15th International Conference on Reversible Computation (RC 2023) and member of program committees for RC2024 and RC2025. Research Focus: Reversible programming, compiler construction, and automata theory. Key Contributions: Development of the RC3 compiler and reversible syntax analysis methods. Recent Publications center on deterministic automata, hybrid computing models, and language design. His work spans conferences like RC, DLT, and IFL, as well as journals including Acta Informatica and Theoretical Computer Science. Notable projects include the Janus programming language and RSSA virtual machine . Scientific Awards : Program Chair for RC2023 Program Committee Member for RC2024 and RC2025 Advising topics include compiler development, functional programming, and reversible computing projects. His teaching includes courses like Compiler Construction and Functional Programming .

• Numerical Analysis
• Numerical Linear Algebra
• Optimization
• +۳ مورد دیگر

Dr. Jan Van Lent is a Senior Lecturer in Applied Mathematics at the University of the West of England , affiliated with the Faculty of Engineering and Technology and its Engineering, Design and Mathematics department. His academic focus spans numerical methods, computational mathematics, and interdisciplinary applications in biological modeling. His research interests include Numerical Analysis , Numerical Linear Algebra , Optimization , Scientific Computing , Machine Learning , and Optimal Transport . He has contributed extensively to numerical methods for PDEs and multigrid techniques, alongside interdisciplinary work in ant colony behavior modeling. His publications reflect a strong emphasis on multigrid methods for partial differential equations (e.g., energy-minimizing coarse spaces , implicit Runge–Kutta discretizations ), with applications in both traditional numerical analysis and biological systems. His 2020 work on Python in teaching highlights his pedagogical contributions to computational education.

Martin Siebenbornمشاهده پروفایل

استاد

• Shape Optimization
• Computational Mathematics
• Fluid Dynamics
• +۳ مورد دیگر

Martin Siebenborn is a Junior Professor (W1) for Optimization and Approximation at the Department of Mathematics, University of Hamburg. His research focuses on shape optimization with applications in fluid dynamics, structural mechanics, and partial differential equations (PDEs), emphasizing scalable high-performance algorithms and multigrid methods. Education: PhD in Mathematics (2014, Universität Trier), Diploma in Mathematics (2010, Universität Trier). His research integrates algorithmic scalability for PDE-constrained optimization, leveraging multigrid preconditioners and nonlinear extension operators. Recent projects include simulation-based design optimization under uncertainties and scalable shape optimization for fluid dynamics. Onyshkevych, Pinzon Escobar, and Wyschka are part of his research team. He has developed open-source software tools like MinFEM, MGSS, and MultigridShapeOpt for teaching and high-dimensional data analysis.

• Experimental Fluid Mechanics
• Computational Fluid Mechanics
• Dynamic Flow Control
• +۴ مورد دیگر

Daniel M Nosenchuck is an Associate Professor at Princeton University's School of Engineering, Department of Mechanical and Aerospace Engineering. His work bridges experimental fluid mechanics and supercomputer architecture , focusing on flow control and simulation technologies. PhD in Aeronautics (Caltech, 1982) MS in Aeronautics (Caltech, 1977) BS in Aerospace and Mechanical Engineering (Syracuse, 1976) Research Highlights : Active control of turbulence and boundary layers using electromagnetic and thermal methods Invention of the Navier-Stokes Computer for advanced hydrodynamic simulations Development of laser-sheet scanning techniques for 3D flow visualization Patents in electromagnetic boundary layer control and dynamic reconfiguration systems Prominent Research Themes from 1982-1994 include: Experimental fluid mechanics with applications in aerospace and marine systems Parallel computing architectures for turbulence simulation Interdisciplinary studies in bioluminescence and hydrodynamics Active control of wake vortices and transitional flows Development of optimizing compilers for supercomputers Innovative visualization techniques for complex flows Scientific Awards : NSF Presidential Young Investigator Award (1984-1989) IBM Faculty Development Award (1984-1985) Princeton Rheinstein Award (1986) National EMMY for Special Visual Effects (1984) GTE Emerging Scholar (1987) William F Ballhaus Prize (1982) Undergraduate Engineering Council Teaching Award (1993) Advising & Grants : Advisor to multiple co-authors in fluid mechanics and computer science NSF funding through Presidential Young Investigator program Industrial and DoD consulting projects Labs & Facilities : Moody Hydrodynamics Laboratory (Princeton) Low-Speed Water Channel High-Speed Water Tunnel

• 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.

Matthias Mayrمشاهده پروفایل

مدرس

• Computational Mechanics
• Fluid-Structure Interaction
• High Performance Computing
• +۶ مورد دیگر

Dr. Matthias Mayr is a Senior Researcher & Lecturer at the University of the Bundeswehr Munich, where he heads the Data Science & Computing Lab. He holds a permanent position at the Institute for Mathematics & Computer-Based Simulation and maintains affiliations with the RISK Research Center. Previously, he was a Postdoctoral Researcher at Sandia National Laboratories and TU München. His research focuses on coupled multiphysics problems including fluid-structure interaction, computational contact mechanics, and high-performance numerical methods. He develops monolithic coupling schemes, parallel solvers, and adaptive algorithms for large-scale simulations. Key application areas include biomedical engineering, cardiovascular mechanics, and materials science. His publications demonstrate consistent focus on computational mechanics innovations, particularly in developing efficient algorithms for multiphysics problems. Recent work emphasizes mortar methods, multigrid preconditioners, and HPC implementations for complex coupled systems. Scientific Awards: Robert J. Melosh Medal (Duke University, 2017) Multiple scholarships including Max-Weber-Programm (Bavaria, 2008-2010) Rudolf Diesel Award (TU München, 2009) He advises students in computational mechanics and HPC topics, supervising projects on multigrid methods, contact algorithms, and parallel FSI solvers. He contributes to major scientific software including Trilinos and 4C Multiphysics. He leads the Data Science & Computing Lab and participates in international research collaborations. He maintains memberships in GAMM, SIAM, IACM, and EUROMECH.

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.

• Nonsmooth Optimization
• Inverse Problems
• Image Reconstruction
• +۵ مورد دیگر

Tuomo Valkonen is a Professor and Principal Investigator at the University of Helsinki , with significant contributions to mathematical optimization and inverse problems. He serves as the Managing Editor of the Journal of Nonsmooth Analysis and Optimization , advancing methodologies for nonsmooth and nonconvex optimization in imaging and PDE-constrained problems. His research spans Nonsmooth Optimization : Developing linearized block coordinate descent methods and forward-backward splitting in non-Hilbertian spaces. Inverse Problems : Applying these techniques to Electrical Impedance Tomography , Diffusion Tensor Imaging , and Magnetic Resonance Imaging . Optimal Transport : Innovating in unbalanced transport for point source localization and dynamic regularization via infimal convolution. Recent publications highlight his work on Online Optimization for real-time imaging. Multigrid Methods for total variation problems. Differential Estimates in bilevel PDE-constrained optimization. He has received the Magna Cum Laude Award at ISMRM 2013 for dynamic imaging work and authored a textbook on nonsmooth analysis (2024). His methods integrate stochastic and deterministic solvers, emphasizing efficiency in high-dimensional medical and geophysical imaging.

Kanat Tangwongsanمشاهده پروفایل

پژوهشگر

• Algorithms
• Parallel Computing
• Streaming Algorithms
• +۶ مورد دیگر

Kanat Tangwongsan is a researcher and educator in computer science, with a PhD from Carnegie Mellon University and prior roles at IBM T.J. Watson Research Center. He has extensive experience in algorithm design, parallel computing, and self-adjusting computation, with publications in top-tier venues like DEBS, ICDE, SPAA, and TOCS. Education: PhD in Computer Science (Carnegie Mellon University), B.S. in Computer Science and Mathematics (Carnegie Mellon University) Research Interests: His work spans algorithms , parallel computing , streaming data structures , and self-adjusting programs , focusing on theoretical improvements and practical implementations for graph problems, convex hulls, and network design. Article Trends: His publications emphasize low-latency streaming , parallel SDD solvers , k-means clustering , and triangle enumeration , with applications in large-scale graph analysis, numerical systems, and cache-oblivious methods. Advising and Collaborations: He has collaborated with prominent researchers like Guy E. Blelloch, Martin Hirzel, and Anupam Gupta, contributing to open-source algorithm frameworks and educational materials.

Ioannis Koutisمشاهده پروفایل

دانشیار

• Algorithms
• Numerical Linear Algebra
• Combinatorial Optimization
• +۳ مورد دیگر

Ioannis Koutis serves as Associate Professor in the Computer Science Department at New Jersey Institute of Technology and holds an Adjunct Faculty position in the Computer Science Department at Carnegie Mellon University. His research expertise spans algorithm design with specialization in numerical linear algebra , combinatorial optimization , and spectral methods . He leads development of significant computational tools including CMG (Combinatorial Multigrid), SpA (Spectral Algorithms), and SpClu (Spectral Constrained Clustering), which address complex problems in large-scale system solving. Analysis of his publication record reveals focus on efficient solvers for symmetric diagonally dominant systems, with applications spanning computer vision and image processing. His work demonstrates strong integration of theoretical foundations with practical implementations. NSF grant #1018463 as co-PI NSF grant CCF-#1149048 supporting CMG development Dr. Koutis maintains active research collaborations and has established software projects that have gained recognition in the computational research community, with his GitHub repositories showing significant engagement from other researchers.

Mario Botschمشاهده پروفایل

استاد

• Computer Graphics
• Geometric Modeling
• Virtual Reality
• +۷ مورد دیگر

Mario Botsch is a Professor in the Department of Computer Science at the Faculty of Technology, University of Bielefeld. His research focuses on computer graphics and geometry processing, with major contributions to polygonal mesh modeling, virtual reality, and GPU-accelerated rendering techniques. Research Interests: Immersive data visualization in 3D knowledge spaces Virtual reality for historical relationship discovery Point-based rendering and surface splatting GPU-accelerated geometric modeling Mesh processing and multiresolution modeling Surface reconstruction and anti-aliasing Scientific Contributions: Developed Phong splatting techniques for high-quality rendering Created multiresolution frameworks using displacement volumes Innovated feature-sensitive sampling for mesh optimization Pioneered GPU-based tolerance volume analysis for error control Established OpenMesh data structure for polygonal mesh handling

Armando Cocoمشاهده پروفایل

دانشیار

• Numerical Analysis
• Computational Fluid Dynamics
• Multigrid Methods
• +۸ مورد دیگر

Armando Coco is an Associate Professor in Numerical Analysis (MAT/08) at the University of Catania since 2021. Previously, he served as Senior Lecturer (2017-2021) and Lecturer (2015-2017) in Mathematical Modelling at Oxford Brookes University. Earlier roles included postdoctoral research at the University of Bristol (2014-2015), University of Bari Aldo Moro (2012-2013), and University of Bordeaux I - INRIA (2012). Education: Master in Mathematics, University of Catania (2008), summa cum laude PhD in Mathematics for Technology, University of Catania (2012), thesis: "Finite-Difference Ghost Cell Multigrid Methods for Elliptic Problems with Mixed Boundary Conditions and Discontinuous Coefficients" Post Graduate Certificate in Higher Education, Oxford Brookes University (2015-2016) Research focuses on high-order numerical methods for PDEs in geophysics, engineering, and plasma simulations. Key contributions include multigrid methods for complex geometries, ghost-point methods for PDE systems, and level-set techniques for interface evolution. Applications span geophysical deformation modeling, monument conservation, and semiconductor electron transport. Active in refereeing for journals like SISC, JCOMP, and Applied Mathematics and Computation. Co-supervised 2 PhD and 4 Master's students. Scientific Awards: London Mathematical Society Research in Pairs Grant (2018) Santander Research Scholarship Scheme Grant (2017) GNCS-INDAM Young Researcher Fellowship (2012) HPC-Europe2 Fellowship (2011-2012)