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Yiqiu DongView profile

Associate Professor

• Mathematical Imaging
• Inverse Problems
• Variational Methods
• +4 more

Yiqiu Dong is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where they have been employed since 2010. Currently, they are working on the ERC-project "High-Definition Tomography" and teach courses including Optimization and Data Fitting, Computational Science in Imaging, and Discrete Inverse Problems. Dr. Dong received their Ph.D. in Computational Mathematics from Peking University in 2007 through a joint program with The Chinese University of Hong Kong. Their academic journey includes postdoctoral research at Karl-Franzens University of Graz in Austria and a scientific researcher position at Helmholtz Zentrum Muenchen in Germany. Dr. Dong's research focuses on mathematical imaging, inverse problems, and variational methods. Their work spans multiple areas including image restoration, tomographic reconstruction, and optimization methods for solving complex imaging problems. They have developed innovative approaches for handling various types of image noise including multiplicative noise, impulse noise, and Cauchy noise. Analysis of Dr. Dong's publication record reveals a strong focus on image restoration techniques using variational methods and total variation regularization. Their work demonstrates progression from basic noise removal techniques to more sophisticated multi-scale and vectorial approaches for color image processing. Recent publications show increasing focus on tomographic reconstruction and medical imaging applications. Dr. Dong has collaborated extensively with researchers across Europe and Asia, including institutions such as Helmholtz Zentrum Muenchen, Karl-Franzens University of Graz, The Chinese University of Hong Kong, and Peking University. Their work on the ERC-project "High-Definition Tomography" represents a significant research initiative in their field. Teaching responsibilities include advanced courses in optimization, computational imaging, and inverse problems, reflecting their expertise in mathematical methods for image processing and analysis.

Rakhim AitbayevView profile

Associate Professor

• Numerical Methods for Partial Differential Equations (PDEs)
• Interface Problems
• Orthogonal Spline Collocation Method
• +5 more

Rakhim Aitbayev is an Associate Professor of Mathematics at New Mexico Institute of Mining and Technology. His research focuses on numerical methods for solving partial differential equations (PDEs), particularly in interface problems, orthogonal spline collocation, iterative methods, and domain decomposition techniques. Research Interests: Numerical methods for interface boundary value problems Orthogonal spline collocation and multigrid methods Iterative solvers for large linear/nonlinear systems Applications to nonlinear elliptic and non-self-adjoint PDEs Finite element and Galerkin schemes Teaching: He has taught courses ranging from Calculus and Linear Algebra to advanced numerical methods, including Computational Fluid Dynamics, PDEs, and COMSOL modeling.

Wojciech L. GolikView profile

Professor

• Numerical Analysis
• Computational Mathematics
• Computational Electromagnetics
• +3 more

Dr. Wojciech L. Golik serves as Professor of Mathematics and Assistant Dean for Mathematical and Natural Sciences at Lindenwood University's College of Science, Technology, and Health. With a distinguished academic career spanning over three decades, he holds leadership responsibilities while maintaining active research and teaching commitments. His educational background includes a Master of Science in Mechanical Engineering from Poznan Technological University (1982), followed by an M.S. (1985) and Ph.D. (1988) in Mathematics from New Mexico State University. His doctoral dissertation focused on Convergence of the Boundary Integral Methods in Numerical Solutions of Fourier Problems . Dr. Golik's research centers on advanced computational methodologies, with particular expertise in numerical analysis, computational electromagnetics, and data science applications. His work demonstrates sophisticated integration of wavelet theory with electromagnetic modeling, developing innovative algorithms for solving complex boundary value problems and integral equations. The publication timeline reveals sustained contributions to computational mathematics, with recent expansion into data science and interdisciplinary applications. His 2013 Polish-language publication Siedemdziesiąt Trzy indicates broader intellectual interests beyond pure mathematics. Teaching responsibilities encompass foundational and advanced mathematics courses including Calculus sequences, Differential Equations, Linear Algebra, Numerical Analysis, and specialized topics like Computational Electromagnetics and History of Mathematics. His extensive course portfolio reflects deep expertise across both theoretical and applied mathematics domains. Professional activities include significant administrative leadership as Assistant Dean, though specific grant funding details aren't documented in available materials. His departmental involvement supports multiple undergraduate and graduate programs including Mathematics (BA/BS), Data Science, and Actuarial Science tracks within the College of Science, Technology, and Health.

Daniel JodlbauerView profile

Researcher

• Computational Mathematics
• Numerical Analysis
• Biomedical Imaging
• +4 more

Dr. Daniel Jodlbauer is a Research Scientist at the Johann Radon Institute for Computational and Applied Mathematics (RICAM) , part of the Austrian Academy of Sciences (OEAW). He is affiliated with the Transfer Group , focusing on interdisciplinary computational methods and their applications in science and engineering. His research spans numerical analysis, computational mechanics, and biomedical imaging. Key projects include developing matrix-free multigrid solvers for fluid-structure interaction and phase-field fracture problems, and advancing adaptive optics for biomedical imaging (e.g., AO-OCT) and astronomical instrumentation (e.g., the MICADO imager for the Extremely Large Telescope). His work bridges theoretical computational methods with real-world applications in medicine, astronomy, and engineering. Recent contributions highlight advancements in PSF reconstruction for the MICADO project, improving imaging resolution for the ELT telescope, and optimizing wavefront sensors for retinal imaging. His computational methods address challenges in high-performance computing, parallel algorithms, and nonlinear solvers for complex physical systems. Dr. Jodlbauer collaborates across disciplines, contributing to RICAM’s research groups in Computational Methods for PDEs and Optimization. His publications reflect expertise in mathematical data science, inverse problems, and multivariate algorithms, though no academic awards or grants are explicitly noted in the provided text.

Scott MacLachlanView profile

Professor

• Computational Applied Mathematics
• Scientific Computation
• Multigrid Methods
• +3 more

Scott MacLachlan is a Professor of Mathematics at Memorial University of Newfoundland. He holds a B.Sc. (Hon.) from the University of British Columbia and a Ph.D. in Applied Mathematics from the University of Colorado at Boulder. Previously, he served as Associate Professor at Tufts University. His research focuses on computational applied mathematics and scientific computation, particularly multigrid methods, finite-element discretizations, and numerical linear algebra. He is an associate editor for the SIAM Journal on Scientific Computing and SIAM Journal on Matrix Analysis and Applications, and co-chairs the program committee for the Copper Mountain Conference on Multigrid Methods. Education: B.Sc. (Hon.) in Mathematics and Computer Science (UBC, 2000), Ph.D. in Applied Mathematics (CU Boulder, 2004). Postdoctoral work at University of Colorado, University of Minnesota, and Delft University of Technology. Research interests include multiscale numerical analysis, PDE-based models in fluid/solid mechanics, and algorithm development for scientific computing. His work integrates functional analysis, numerical linear algebra, and physical modeling. He develops open-source software such as XBraid for parallel time integration and adaptive AMG codes. Publications span multigrid methods, domain decomposition, and liquid crystal simulations. He collaborates on projects involving thermal slip, magnetohydrodynamics, and geophysical electromagnetics. Teaching includes courses on numerical methods for PDEs at both Memorial University and Tufts. Professional activities include editorial roles, conference organization, and contributions to international workshops on multiscale modeling. His software tools and algorithmic innovations address challenges in parallel computing and large-scale simulations.

Shuang LiuView profile

Assistant Professor

• Numerical Methods
• Free Boundary Problems
• Mathematics of Scientific Computing
• +4 more

Shuang Liu is an Assistant Professor in the Department of Mathematics at the University of North Texas. Previously, she held positions as a SEW Assistant Professor at UC San Diego and Postdoc Research Associate at Los Alamos National Laboratory. She earned her PhD in Mathematics from the University of South Carolina and holds MS/BSc degrees from Henan Normal University. Her research focuses on numerical methods for free boundary problems with applications in ecology, plasma physics, and computational biology. Key areas include population dynamics modeling, cell movement simulation, and plasma equilibrium computations. Her publications demonstrate consistent focus on developing robust numerical algorithms for complex biological and physical systems, particularly advancing techniques for moving boundary problems and high-performance computing implementations. Awards: AMS Simons Travel Grant (2021-2023) NSF Graduate Internship at LANL (2019) George W. Johnson Fellowship (2019) Multiple travel awards from AMS/SIAM and USC She has supervised graduate students including Zunding Huang and received research funding from UCSD and AMS. Her work involves collaborations with research groups in computational mathematics and plasma physics.

• Numerical Linear Algebra
• Scientific Computing
• Image Reconstruction
• +5 more

Misha Kilmer is the William Walker Professor of Mathematics at Tufts University, with a secondary appointment in Computer Science. She also serves as Deputy Director of ICERM at Brown University and co-PI of Tufts TRIPODS Institute. Her research focuses on numerical linear algebra, scientific computing, and image reconstruction/restoration. Kilmer has held leadership roles, including Chair of Tufts' Mathematics Department (2013-2019) and has received prestigious recognition, including SIAM Fellowship (2019) and the Tufts Undergraduate Teaching Award (2001). Education: PhD in Applied Mathematics, University of Maryland (1997) MA in Mathematics, Wake Forest University (1994) BS in Mathematics, Wake Forest University (1992) Research Interests: Kilmer's work emphasizes tensor decompositions, iterative methods, and model reduction. She develops algorithms for high-dimensional data analysis, imaging applications, and inverse problems. Her techniques bridge computational mathematics and engineering challenges, with applications in biomedical imaging, environmental monitoring, and machine learning. Publications Trends: Recent work highlights advancements in tensor-based neural networks, dynamic image reconstruction, and efficient algorithms for large-scale data. Key contributions include randomized algorithms for tensor decompositions and compressed sensing techniques for multi-modal data. Awards: SIAM Fellow (2019) Tufts Undergraduate Initiative in Teaching Award (2001) Advising & Grants: Kilmer has secured funding from NSF, NIH, IARPA, DARPA, and IBM. She co-chairs major SIAM conferences and serves on editorial boards for journals like SIAM Review and La Matematica . Labs/Teams: Active in Tufts' Data Intensive Studies Center (DISC) and co-leads the TRIPODS Institute. Collaborates internationally on projects involving tensor analysis and computational science.

Martin LanserView profile

Researcher

• Computational Science and Engineering
• Numerical Methods
• Domain Decomposition Methods
• +4 more

Martin Lanser is a Researcher at the University of Cologne's Department of Mathematics and Computer Science and a Core Scientist at the Center for Data and Simulation Science (CDS). His work focuses on developing efficient numerical methods for computational science and engineering problems, particularly targeting modern many-core architectures with million-way parallelism. His research spans Computational Science and Engineering, Numerical Methods, and Parallel Computing, with specialized expertise in Domain Decomposition Methods, Multigrid Approaches, and Computational Homogenization for heterogeneous solid mechanics. Lanser's theoretical work emphasizes nonlinear solvers for strongly heterogeneous materials, integrating algebraic multigrid techniques to enhance scalability in extreme-scale simulations. Analysis of his publication record reveals consistent innovation in scalable domain decomposition methods since 2014, with recent work targeting exascale computing through the FE2TI software framework. His research demonstrates strong interdisciplinary connections between computational mathematics, materials science, and high-performance computing, particularly in applications for dual-phase steel modeling. No scientific awards are documented in the provided information. Lanser's advising and grant activities are not specified in the source material, though his collaborative publications indicate extensive partnerships with researchers like Axel Klawonn and Oliver Rheinbach on projects including SCALEXA and High-Q club initiatives. As a core developer of the FE2TI software package—a computational homogenization implementation selected for the High-Q club—Lanser contributes to the CDS's research in quantitative modeling of complex physical systems. His work directly supports exascale computing projects focused on parallel domain decomposition methods and is integrated into the university's numerical analysis research infrastructure at numerik.uni-koeln.de.

• Numerical Analysis
• Differential Equations
• Optimization
• +5 more

Prof. Dr. Dietrich Braess is affiliated with the Ruhr University Bochum as a faculty member in the Faculty of Mathematics . His research focuses on numerical methods for elliptic differential equations, nonlinear approximation theory, and optimization, particularly the Braess paradox in traffic and network systems. Research Interests Multigrid methods for elliptic PDEs A posteriori error estimation via two-energies principle Finite elements in solid mechanics (locking phenomena) Stokes solvers and mixed elements Rational approximation and complex analysis methods Braess paradox in transportation and computer networks Co-Editorships Constructive Approximation (until 1999) SIAM Journal on Numerical Analysis (1991–2000) Numerical Linear Algebra European Transactions on Electrical Power (until 2010) Braess Paradox A counterintuitive phenomenon where adding roads can worsen traffic congestion. This has implications beyond transportation networks, extending to computer networks and electrical systems. Key papers include: Original 1968 German Paper 2005 English Translation

Natalie BeamsView profile

Academic

• High-Performance Computing
• Scientific Computing
• Numerical Methods for PDEs
• +3 more

Natalie Beams is a Research Assistant Professor at the Innovative Computing Laboratory (ICL) within the University of Tennessee's Department of Electrical Engineering and Computer Science (EECS). She holds a PhD in Theoretical and Applied Mechanics from the University of Illinois at Urbana-Champaign (UIUC), an MS from UIUC, and a BS in Mechanical Engineering (summa cum laude) from the University of Oklahoma. Her research focuses on numerical methods for PDEs, high-performance computing (HPC), and GPU-accelerated algorithms, with contributions to projects like the Exascale Computing Project's CEED and CLOVER initiatives. Key research areas include finite element methods , integral equation solvers , and mixed-precision algebraic multigrid techniques . She has developed software tools such as the libCEED library for high-order discretizations and contributed to the Ginkgo and MAGMA libraries. Her work emphasizes exascale computing, GPU optimization, and parallel algorithm design. Education: PhD in Theoretical & Applied Mechanics, UIUC (2017) MS in Theoretical & Applied Mechanics, UIUC (2014) BS in Mechanical Engineering, University of Oklahoma (2010) Awards & Honors: Best Workshops Paper Award, PPAM Conference (2022) 2011/2012 Computational Science & Engineering Fellow 2010 College of Engineering Carver Fellow List of Teachers Ranked as Excellent by Students (UIUC, 2014) Grants & Projects: Active contributor to the Exascale Computing Project (ECP), leading efforts in CEED (libCEED library) and CLOVER (MFEM-Ginkgo interoperability). Collaborates with Rice University and other institutions on HPC and numerical algorithms. Labs & Teams: Core member of the Innovative Computing Laboratory (ICL) at UTK, specializing in exascale software and GPU-accelerated computing.

Dianne P. O'LearyView profile

Professor

• Scientific Computing
• Numerical Analysis
• Computational Science
• +6 more

Dianne P. O'Leary is a Professor affiliated with the Department of Computer Science at the University of Maryland. Her work focuses on scientific computing, numerical analysis, and computational science, with a particular emphasis on matrix factorizations, differential equations, and Monte Carlo methods. She authored the textbook *Scientific Computing with Case Studies* (SIAM Press, 2009), which integrates theoretical concepts with practical case studies across physics, engineering, epidemiology, and biology. Her research explores topics such as image deblurring, blind deconvolution, elastoplastic torsion, and iterative methods for solving large linear systems. The book includes nineteen case studies that illustrate algorithm design, mathematical modeling, and software development principles. Notably, the text emphasizes stability, error analysis, and MATLAB implementation grounded in machine arithmetic and memory management. While no explicit scientific awards or student advisees are mentioned in the provided texts, her contributions to computational education and algorithm development are central to her work. Supplementary materials include solution manuals, MATLAB code repositories, and detailed notes on topics like sensitivity analysis, computer memory management, and finite element methods. Her case studies often bridge theory and application, such as modeling epidemics, robot control systems, and structural engineering problems. The book’s structure reflects a commitment to hands-on learning, with units dedicated to dense matrix computations, optimization, Monte Carlo simulations, and sparse matrix techniques for partial differential equations.

• High Performance Computing
• Parallel Computing
• Numerical Linear Algebra
• +4 more

Dr. Salvatore Filippone is a Lecturer in Software Engineering for Technical Computing at Cranfield University, specializing in high-performance computing, sparse matrix algorithms, and GPU acceleration. He holds an MSc in Computer Engineering and a PhD in Mathematics from the University of Rome "Tor Vergata" and previously worked as a lead developer at IBM for the ESSL scientific software suite. Education: MSc in Computer Engineering (University of Rome "Tor Vergata"), PhD in Mathematics (University of Rome "Tor Vergata") His research focuses on scalable numerical algorithms, parallel computing environments, and applications in fluid dynamics, electromagnetism, and optimization. Dr. Filippone leads the "Software Engineering for Technical Computing" option in Cranfield's MSc in Computational Software Techniques for Engineering. He contributes to software frameworks like PSCToolkit and BootCMatch for exascale and extreme-scale computing. Recent publications emphasize adaptive Algebraic Multigrid (AMG) methods, GPU-accelerated sparse matrix operations, and parallel preconditioners. Key collaborations include researchers at Stanford University, University of Rome "Tor Vergata", and various EU-funded projects. Dr. Filippone serves as an associate editor for ACM Transactions on Mathematical Software and actively reviews EU-funded project proposals. His work bridges theoretical advancements in numerical algorithms with practical engineering applications, particularly in computational fluid dynamics and GPU-based scientific computing.

• Applied Mathematics
• Applied Machine Learning
• Uncertainty Quantification
• +8 more

Quanling Deng is a Lecturer in the School of Computing at the Australian National University (ANU), where he focuses on applied mathematics, computational methods, and machine learning. Previously, he held positions as a Van Vleck Visiting Assistant Professor at the University of Wisconsin-Madison (2020–2022) and a Research Associate at Curtin University (2016–2020). He earned his Ph.D. in Mathematics from the University of Wyoming in 2016 and has conducted visiting research at institutions including INRIA (Paris), AGH University (Krakow), and École des Ponts ParisTech. Education: Ph.D. in Mathematics, University of Wyoming, 2016 Moved to the USA in 2011 to pursue studies in mathematics His research interests span Applied Mathematics (e.g., sea ice dynamics, ocean/atmosphere systems), Computational Mathematics (finite element methods, isogeometric analysis), and Machine Learning (feature interaction, deep neural networks). He also investigates Data Assimilation techniques, including stochastic models and Lagrangian-Eulerian frameworks. Research Trends: His recent work emphasizes multiscale modeling (e.g., sea ice floes), eigenvalue problem solutions using advanced finite element techniques, and explainable machine learning. He explores applications of physics-informed neural networks and parallel computing for high-performance simulations. Grants & Projects: Leading the project "Advancing Numerical Computation for Schrödinger Eigenvalue Problems" (2023) Affiliations: Previously affiliated with Curtin Institute for Computation and Curtin TIGeR. Collaborates internationally on computational mathematics and climate modeling.

Malte von SchevenView profile

Researcher

• Adaptive structures
• Fluid-structure interaction
• Software Engineering
• +3 more

Malte von Scheven is a Senior Researcher and Deputy Director at the Institute of Structural Analysis and Dynamics at the University of Stuttgart. He holds a Dr.-Ing. degree (2009) and specializes in adaptive structures, fluid-structure interaction, and computational mechanics. Research Focus: Redundancy matrices for structural assessment, high-performance computing, actuator placement optimization Teaching: Finite element methods, computational mechanics, nonlinear structural analysis Leadership: Deputy Director since 2006, conference organizer for ECCOMAS and SMART symposia His work bridges structural mechanics with bio-inspired design, including studies on sea urchin skeletons as models for segmented shells. He has supervised numerous theses on SFRP composites, topology optimization, and adaptive systems. Scientific Engagement: Published 15+ papers on redundancy matrices and FSI Organized mini-symposia at international conferences (ECCOMAS 2024, SMART 2023) Active in university governance through Faculty Council and TIK committee Recent research investigates mechanical modeling of adaptive structures, with applications in civil engineering and architectural geometry. His redundancy matrix framework provides novel performance indicators for robust design and assemblability assessment.

• Numerical Analysis
• Partial Differential Equations
• Mathematical Modeling
• +3 more

Dr. Paul Zegeling is an Associate Professor at the Mathematical Institute, Faculty of Science, Utrecht University. His office is located in the Hans Freudenthal Building (Room 513) at Budapestlaan 6, Utrecht. He specializes in mathematical modeling with a focus on numerical methods for partial differential equations. Research Interests: Zegeling's work spans adaptive mesh computation, boundary value problems, and numerical analysis of PDEs. Key domains include: Moving mesh methods for reaction-diffusion systems High-order discretization techniques Computational glaciology and porous media flows Nonlinear multigrid solvers His recent publications (2019–2024) demonstrate consistent focus on advanced numerical methods for complex systems. Dominant themes include adaptive discretization schemes, glacial ice modeling, and singular perturbation problems, primarily published in computational mathematics journals. Teaching: Courses include: Introduction to Scientific Computing Numerical Methods for Partial Differential Equations Mathematics 2 Academic Activities: Has delivered invited talks at international conferences including presentations on tensor-product adaptive grids and moving mesh methodologies.