Find Academic Supervisors

• 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.

Peter HillView profile

Researcher

• Computational Physics
• Plasma Physics
• Software Sustainability
• +3 more

Dr. Peter Hill is a Research Software Engineer at the University of York , affiliated with the York Plasma Institute under the School of Physics, Engineering and Technology. His 11-year career spans roles at CCFE, IRFM (CEA, France), and the University of York, with a focus on advancing software sustainability in plasma physics research. Research Interests Dr. Hill specialises in plasma physics and computational modeling , particularly in tokamak and stellarator plasma simulations . He develops and maintains critical software packages like BOUT++ and GS2 , contributes to pyrokinetics , and explores drift kinetic theory for neoclassical tearing modes. His work addresses fusion reactor design , exascale computing , and FAIR data principles in plasma science. Scientific Contributions Lead developer for BOUT++ and GS2 plasma simulation frameworks EPSRC RSE Fellowship (2021) for promoting open research in UK plasma science Multi-institutional collaborations with University of Warwick, University of Strathclyde, CCFE, and Leibniz Association

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.

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.

Andersen AngView profile

Lecturer

• Mathematical programming/optimisation
• Nonnegative Matrix Factorisation/Tensor factorisation
• Machine learning
• +3 more

Dr Andersen Ang is a Lecturer at the University of Southampton, specializing in mathematical optimization, nonnegative matrix/tensor factorization, and machine learning applications. His research focuses on developing advanced algorithms for signal processing, convex optimization, and data analysis. He is actively supervising two PhD students in Computer Science and has published extensively in top-tier journals/conferences like IEEE Transactions on Signal Processing and SIAM Journal on Optimization. Dr Ang's work spans theoretical algorithm development (e.g., MGProx multigrid optimization, sparse NMF) and practical applications in source separation, biomedical interfaces, and educational technology. His recent publications emphasize graph signal processing, convex relaxation techniques, and scalable optimization methods for high-dimensional data. He maintains an active Google Scholar profile and a personal research website at https://angms.science/. While no specific awards were listed, his prolific publication record (over 25 articles since 2014) and focus on impactful optimization techniques demonstrate significant contributions to computational mathematics and machine learning communities. Current research includes inhomogeneous graph trend filtering and Riemannian optimization approaches for constrained problems.

• 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.

• Numerical Analysis
• Computational Mechanics
• Phase-field Modeling
• +3 more

Prof. Dr. Carsten Gräser is a Professor in the Department of Applied Mathematics at Friedrich-Alexander University Erlangen-Nürnberg (FAU). His research focuses on numerical analysis, computational mechanics, and phase-field modeling, with an emphasis on nonsmooth optimization and multigrid methods for complex systems. 2025: Well-posedness of evaporation models for droplets. 2023: Multiscale fault systems and phase-field brittle fracture simulations. 2021: Membrane mechanics and DUNE framework developments. His work spans partial differential equations, computational geosciences, and biological membrane modeling, often involving finite element methods and nonlinear systems. Recent publications highlight applications in fluid dynamics, fracture mechanics, and geophysical fault simulation. His articles demonstrate a strong focus on numerical methods for phase-field models and nonsmooth systems, with recurring sub-fields like multigrid algorithms, membrane-mediated interactions, and finite element discretization. He collaborates widely on topics such as marine ice sheets and solder alloy coarsening. At FAU, Gräser teaches courses including Numerics of Incompressible Flows and Modeling, Simulation, and Optimization . His research integrates software development (e.g., the DUNE framework) with applications in physics, materials science, and geosciences.

Claudia Frohn-SchaufView profile

Professor

• Numerical Mathematics
• Image Processing
• Partial Differential Equations
• +3 more

Claudia Frohn-Schauf is a Professor of Engineering Mathematics and Numerics at the Department of Mechatronics and Mechanical Engineering at Bochum University of Applied Sciences. Since March 2022, she has served as Vice President for Studies, Teaching, and Continuing Education. Previously, she was Dean of Studies (2016–2022) for the same department. Her educational background includes a PhD in Mathematics from Heinrich Heine University Düsseldorf (1992) and a Diploma in Mathematics with a minor in Computer Science (1988). Her research spans: Core Numerical Methods : Multigrid techniques, PDE-based image processing, and total variation applications Engineering Education : Curriculum design, digital tool integration (MATLAB), and learning analytics Applied Mathematics : Image registration/denoising and fluid dynamics modeling She contributes to international academic partnerships in India and Mexico, focusing on student exchange programs and joint research initiatives. Her administrative leadership involves optimizing digital learning strategies through the DigiTeach Institute and improving academic success via the Institute for Academic Success and Didactics (ISD).

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +5 more

Ying Cai is a prolific researcher with significant contributions across diverse domains of computer science, mathematics, and biomedical applications. Their work spans artificial intelligence, medical imaging, cybersecurity, and computational methods, as evidenced by recent publications in journals like Engineering Applications of Artificial Intelligence and IEEE Transactions on Medical Imaging . Key research areas include lung cancer detection algorithms, distributed filtering under cyber-attacks, and cryptographic protocols. 2025: 9 publications 2024: 18 publications 2023: 8 publications Notable collaborations include work with Yang Zhao, Zeyu Zhang, and Daji Ergu on medical AI applications and computational techniques. Their recent articles demonstrate expertise in: Medical imaging and diagnostic automation Deep learning optimization Secure communication protocols Computational mathematical models Ying Cai's research bridges theoretical rigor with practical implementation across domains like health informatics, network security, and educational technology.

Christos Papadopoulos FilelisView profile

Assistant Professor

• Computational Mathematics
• Mathematical and Computational Physics
• Numerical Methods
• +6 more

Christos Papadopoulos Filelis is an Assistant Professor at the Department of Electrical and Computer Engineering, Democritus University of Thrace, where he conducts research and teaches courses in computational mathematics and physics. His academic journey began with a Diploma in Electrical and Computer Engineering from Democritus University of Thrace in 2010, followed by a PhD in Numerical Analysis and High Performance Computing from the same institution in 2014. His research interests span Computational Mathematics, Mathematical and Computational Physics, Numerical Methods, Scientific Calculations, High Performance Computing, and Machine Learning applications in Mathematics and Physics. He has made significant contributions to pre-coordinated iterative methods, multi-grid and multi-level methods, parallel computing, cloud computing, and big data analytics. His work bridges theoretical mathematics with practical computational applications across multiple domains. Analysis of his recent publications reveals a strong focus on developing advanced computational techniques for solving complex mathematical problems, with particular emphasis on matrix computations, preconditioning methods, and time series analysis. His research demonstrates a consistent trajectory toward more efficient algorithms for high-performance computing environments, with increasing applications in machine learning and environmental sustainability. Dr. Papadopoulos Filelis has taught undergraduate courses including Physics, Numerical Analysis, Mathematical Software, Scientific Calculations, and Calculus of Changes. He has participated in European (H2020) and National funded projects such as the SFI FinTechNext project (UCC, Ireland), demonstrating his ability to secure competitive research funding. His work with the Physics Laboratory at Democritus University of Thrace involves developing computational frameworks that integrate theoretical physics with advanced numerical methods. He has collaborated extensively with researchers across Europe, particularly in cloud computing and high-performance computing projects that address real-world computational challenges.

Matthias MayrView profile

Lecturer

• Computational Mechanics
• Fluid-Structure Interaction
• High Performance Computing
• +6 more

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.