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Jos MaubachView profile

Assistant Professor

• Numerical Analysis
• Scientific Computing
• Computational Science and Engineering
• +3 more

Jos Maubach is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science. He is part of the Centre for Analysis, Scientific Computing, and Applications (CASA). His research focuses on numerical methods, scientific computing, and computational science and engineering, particularly in iterative methods for partial differential equations and model order reduction. Education: MSc in Mathematics (cum laude, Radboud University Nijmegen, 1986); PhD in Mathematics (Radboud University Nijmegen, 1991). Joined TU/e in 1998. Research Interests: Numerical analysis, nonlinear dynamical systems, computational methods for engineering applications, and model reduction techniques. Publications Highlight Trends: Recent work emphasizes model order reduction for bilinear systems, H2 optimization in electronics reliability, and mechanics of materials (e.g., moisture-induced buckling in paper sheets). No scientific awards explicitly mentioned. Teaches courses such as Programming and Modelling, Scientific Programming, Analysis 1, and Advanced Calculus II. No ancillary activities reported. Collaborations: Works with groups like the Computational Quantum & Molecular Dynamics team and the High Tech Systems Center.

Michal OutrataView profile

Assistant Professor

• Numerical Linear Algebra
• Krylov Methods
• Domain Decomposition Methods
• +5 more

Michal Outrata is an Assistant Professor in the Department of Numerical Mathematics at Charles University's Faculty of Mathematics and Physics in Prague, Czech Republic. He began his position in fall 2024 after completing a postdoctoral fellowship at Virginia Tech with Prof. Eric de Sturler and earning his PhD under Prof. Martin Gander at the University of Geneva, where his thesis was awarded the Henri Fehr Prize in 2023. His academic journey started with undergraduate studies in Prague under Prof. Zdeněk Strakoš (bachelor) and Prof. Miroslav Tůma (master). Dr. Outrata's research focuses on understanding why certain numerical methods work for specific problem classes, with emphasis on numerical linear algebra , Krylov subspace methods , and domain decomposition techniques . His work bridges theoretical analysis with practical algorithm development, particularly in preconditioning strategies for iterative solvers. Current projects include the Primus Research Programme (2025-2028) titled 'Divide, Conquer and Optimize: Domain Decomposition Methods in Scientific Computing' which explores hierarchical matrix formats and mixed precision computations for optimizing domain decomposition methods. His publication record demonstrates consistent contributions to top journals including SIAM Journal on Scientific Computing and Linear Algebra with Applications. His research shows progression from foundational work on GMRES convergence to sophisticated analyses of block Runge-Kutta preconditioners and optimized Schwarz methods with data-sparse transmission conditions. His recent work increasingly integrates hierarchical matrix formats and explores mixed precision computing approaches. Swiss Government Excellence Scholarship (3-year award) Henri Fehr Prize for best PhD thesis in mathematics (2023) Primus Research Programme grant (2025-2028) Dr. Outrata actively mentors students and postdocs, currently supervising Marouan Handa and Lenka Ptáčková (PostDocs) along with undergraduate researchers. He teaches courses including Numerical Analysis and Introduction to Numerical Mathematics at Charles University. His collaborative network spans international institutions including University of Geneva, Virginia Tech, and various European research centers. He is also involved in organizing major conferences including DD29 and GAMM95.

• Electronic Circuit Design
• RF Power Amplifiers
• Digital Signal Processing
• +5 more

Hans-Georg Brachtendorf is a Professor at the Research Center Hagenberg Embedded Systems within the School of Informatics, Communications and Media at the University of Applied Sciences Upper Austria - Hagenberg Campus. His work focuses on advanced circuit simulation techniques, RF engineering, and digital signal processing with significant contributions to harmonic balance methods and nonlinear circuit analysis. His research interests span electronic circuit design, RF power amplifiers, digital signal processing, and nonlinear dynamics. Specializing in harmonic balance methods and oscillator circuits, he has developed innovative techniques for steady-state circuit simulation, nonlinear adaptive filtering, and multiplier-less digital filter design. His work bridges theoretical mathematics with practical electronic engineering applications, particularly in THz-wave detection and wireless communication systems. Analysis of his recent publications (2022-2025) reveals a strong focus on nonlinear dynamics identification using symbolic regression, optimization of digital filters through multiplier-less architectures, and governing equation reconstruction from measurement data. His work demonstrates consistent advancement in computational efficiency for circuit simulation while addressing modern challenges in RF and THz technologies. While no specific scientific awards are listed in the available data, his research has generated significant academic impact with 43 publications since 1994 and an h-index reflecting substantial citation influence. Professor Brachtendorf has supervised at least two academic works according to institutional records, though specific student names are not provided. His research activities include collaborations evidenced by joint publications with researchers such as Steiger, Dalpiaz, and Kronberger across multiple institutions. The Research Center Hagenberg Embedded Systems serves as his primary laboratory environment, focusing on digital transformation within ICT strength areas with particular emphasis on circuit simulation and signal processing applications.

Erin Claire CarsonView profile

Assistant Professor

• Numerical Linear Algebra
• Parallel Computing
• High-Performance Computing
• +7 more

Erin Claire Carson is an Assistant Professor at the Department of Numerical Mathematics, Faculty of Mathematics and Physics, Charles University, Prague. A specialist in numerical linear algebra and high-performance computing, she leads the ERC Starting Grant project InEXASCALE focused on exascale algorithms. Her research explores mixed precision arithmetic, communication-avoiding Krylov subspace methods, and stability analysis in finite precision. Ph.D., University of California, Berkeley (2015) Courant Instructor, New York University (2015-2018) Postdoctoral Researcher and PRIMUS Fellow, Charles University (2018-2022) Dr. Carson's work bridges theoretical analysis with practical implementations for supercomputers. Her recent publications include advancements in low-synchronization orthogonalization, silent error detection, and multilevel sampling techniques. She received the 2025 Wilkinson Prize from SIAM for outstanding contributions to numerical analysis. Current trends in her research involve: Exploiting mixed precision arithmetic for algorithm acceleration Developing stable communication-avoiding Krylov methods Optimizing numerical stability in GPU-based solvers Understanding error propagation in multistage refinements Scientific honors: 2025 SIAM Wilkinson Prize in Numerical Analysis and Scientific Computing 2023 ERC Starting Grant recipient 2019-2022 PRIMUS Research Fellow She supervises PhD and Master’s students while teaching advanced courses in numerical linear algebra and high-performance computing. Her work has been featured in WIRED and Forbes Czech Republic for improving supercomputer algorithms.

Berkant SavasView profile

Associate Professor

• Scientific computing
• Numerical linear and multilinear algebra
• Large-scale graph/network computations
• +3 more

Berkant Savas is an Associate Professor at Linköping University's Department of Science and Technology (ITN) within the Physics, Electronics and Mathematics school. His research focuses on scientific computing, numerical linear/multilinear algebra, large-scale graph/network computations, and tensor analysis. He has developed influential algorithms like Clustered Matrix Approximation (CMAPP) and Grassmann manifold computational tools. His work bridges theoretical foundations with practical applications in data science and engineering. Key contributions include scalable methods for massive graphs, low-rank tensor approximations, and optimization on manifolds. Savas' MATLAB-based tools (e.g., Grassmann classes, tensor approximation packages) are widely used in academic and industrial research. He has published extensively in top journals and conferences such as SIAM Journal on Matrix Analysis and IEEE transactions. Current research explores applications in link prediction, recommendation systems, and high-dimensional data analysis. Savas collaborates internationally, with notable co-authors like Inderjit Dhillon and Lars Eldén. His work emphasizes computational efficiency and memory optimization for large-scale problems arising in networks and information retrieval. Though no explicit awards are mentioned, his impactful contributions highlight recognition within the computational mathematics community.

Laura GrigoriView profile

Professor

• High-Performance Scientific Computing
• Numerical Linear Algebra
• Communication-Avoiding Algorithms
• +6 more

Laura Grigori is a Professor of Applied and Computational Mathematics at EPFL and Head of the Laboratory for Simulation and Modeling at the Paul Scherrer Institute (PSI), Switzerland. She holds a Ph.D. in Computer Science from Université Henri Poincaré (2001) and has been a leading figure in high-performance numerical algorithms and communication-avoiding methods. Her research focuses on numerical linear algebra, randomized algorithms, and scalable scientific computing for applications in astrophysics, molecular simulations, and exascale systems. Affiliations: EPFL Chair of High-Performance Numerical Algorithms & Simulation; Head, PSI Laboratory for Simulation and Modeling. Education: Ph.D. in Computer Science, Université Henri Poincaré (2001); Postdoctoral Researcher, UC Berkeley/LBNL (2001–2003); Senior Researcher at INRIA (2004–2023). Research Interests: Communication-avoiding algorithms, randomized numerical linear algebra, parallel preconditioners, tensor computations, and exascale computing challenges. Awards: SIAM Fellow (2020), ERC Synergy Grant (2018), SIAM Supercomputing Career Prize (2024), and Best Paper Awards (2016, 2023). Grants & Leadership: PI of the ERC Synergy Project EMC² (Extreme-scale Computational Chemistry); Chair of PRACE Scientific Steering Committee (2021–2022); SIAM SIAG Supercomputing Chair (2016–2017). Labs/Teams: Alpines Group (INRIA/Sorbonne University), EMC² Collaboration (Cancès, Maday, Piquemal).

Gilles VillardView profile

Researcher

• Computer Algebra
• Linear Algebra
• Lattice Basis Reduction
• +3 more

Gilles Villard is a Researcher at the CNRS, affiliated with the Pascaline project-team and the Laboratoire LIP (a joint unit with CNRS, ENS Lyon, INRIA, Université Claude Bernard Lyon 1, and the University of Lyon). His research activities focus on exact linear algebra, lattice basis reduction, and computational complexity, with contributions to symbolic computation, polynomial algorithms, and high-performance algebraic computing. He teaches advanced courses in symbolic summation and integration at the Master’s level (M2) at École Normale Supérieure de Lyon. His work emphasizes structured matrix computations, including quasiseparable matrices and Sylvester matrices, and integrates theoretical advancements with practical software development, such as the mapml library interfacing Maple with the pml C++ library and the hplll library for lattice problems. Villard has organized and contributed to major international conferences, including ISSAC, STACS, and the Springer TMSC volume for the Recent Trends in Computer Algebra (RTCA) initiative. His articles frequently explore topics like Krylov subspace methods, resultant computation, and modular composition, emphasizing algorithmic efficiency and numerical stability. He has been involved in collaborative projects like the LinBox joint CNRS/NSF initiative and the FLINT library development, and maintains active participation in research networks such as the GdR CNRS Informatique Fondamentale et ses Mathématiques and the International Linear Algebra Society (ILAS).

• Numerical Analysis
• Linear Algebra
• Matrix Equations
• +4 more

Valeria Simoncini is a Full Professor in the Department of Mathematics at the University of Bologna. Her research focuses on numerical analysis, linear algebra, and computational mathematics, with particular emphasis on matrix equations, Krylov subspace methods, and optimization algorithms. She has contributed to advancements in iterative solvers, randomized sketching techniques, and preconditioning strategies for large-scale problems. Her work often intersects with applications in partial differential equations and scientific computing. Her research interests include the development of efficient numerical methods for solving matrix equations, such as Sylvester and Lyapunov equations, as well as optimization problems arising in control theory and data science. She explores topics like tensor decompositions, approximation techniques for matrix functions, and the analysis of numerical stability in iterative algorithms. Simoncini’s publications span over two decades, with recent contributions addressing the integration of randomized algorithms into numerical linear algebra, the acceleration of quadrature methods, and the application of low-rank approximations in high-dimensional problems. Her work frequently appears in top-tier journals such as SIAM Journal on Matrix Analysis and Applications and Numerical Linear Algebra with Applications . Despite the extensive list of publications, no specific scientific awards or grants are explicitly mentioned in the provided text. She advises students through her academic role but no specific advisee names are listed here.

• Inverse Problems
• Computational Algorithms
• Medical Imaging
• +3 more

Rosemary Renaut is a Professor and Associate Director for Faculty Development at the School of Mathematical and Statistical Sciences, Arizona State University. Her research focuses on computational algorithms for solving inverse problems in medical and geophysical applications, emphasizing regularization techniques and numerical linear algebra. She holds a Ph.D. in Applied Mathematics from the University of Cambridge (1985). Education: Ph.D. in Applied Mathematics, University of Cambridge, UK (1985) Research Interests: Dr. Renaut specializes in developing algorithms for solving ill-posed inverse problems, particularly in medical imaging (e.g., PET, MRI) and geophysics (e.g., gravity/magnetic data inversion). Her work addresses challenges like data noise, model ill-posedness, and parameter regularization. She has contributed to methods like total variation regularization, χ²-based parameter estimation, and iterative solvers (e.g., LSQR). Articles Trends: Recent publications (2020–2025) emphasize large-scale inversion techniques, sparsity-driven reconstruction, and efficient regularization parameter estimation. Key applications include geophysical exploration (e.g., iron oxide deposits, kimberlites) and medical imaging (e.g., EEG source localization). Her work often combines numerical methods with domain-specific challenges, such as joint inversion of multimodal geophysical data. Awards: SIAM Fellow (2022) Technische Universitaet Muenchen Ambassador (2013) J.T. Knight Prize in Mathematics (1982) Grants & Service: Dr. Renaut has led numerous grants (e.g., NSF, DOD) on inverse problems and computational methods. She serves on editorial boards (BIT, SIAM News) and has held roles like Program Director at the NSF. Her service also includes mentoring junior faculty and advancing computational biosciences education. Labs/Teams: Collaborates with interdisciplinary teams in geophysics (e.g., gravity/magnetic data inversion) and biomedical imaging (e.g., PET parametric imaging). Active in conferences like International Conference on Spectral and High Order Methods.

Melina FreitagView profile

Professor

• Numerical Linear Algebra
• Data Assimilation
• Inverse Problems
• +6 more

Melina Freitag is Professor for Data Assimilation at the Institute for Mathematics, University of Potsdam. Her research spans numerical linear algebra, inverse problems, and model order reduction, with applications in geophysics, image processing, and machine learning. She leads the Data Assimilation Group and contributes to the SFB 1294 Collaborative Research Center. Education : Prof. Freitag earned her Diplom in Mathematics at TU Chemnitz (2004) and PhD in Mathematical Sciences from University of Bath (2007). Research Themes : Her work focuses on Krylov subspace methods, low-rank approximations, and preconditioning for large-scale systems. She bridges classical numerical analysis with modern data assimilation, addressing challenges in: Bayesian inverse problems with unstable systems Optimized spectral sampling in X-ray imaging Physics-informed neural networks for Navier-Stokes inversion Parameter-dependent eigenvalue analysis Collaborations & Grants : She collaborates with institutions like KTH Stockholm and Arizona State University. Her group secures funding through SFB 1294 and participates in INI Cambridge networks. Leadership : Co-Chair of GAMM Activity Group on Applied Numerical Linear Algebra SIAM Activity Group on Linear Algebra Chair (2022) Teaching : Delivers courses on numerical optimization, matrix methods in data science, and inverse problems, integrating computational theory with real-world applications.

Giovanni FasanoView profile

Professor

• Operations Research
• Numerical Analysis
• Computational Mathematics
• +6 more

Giovanni Fasano is a Full Professor at the Department of Management, Venice School of Management, Ca' Foscari University of Venice. His academic journey includes a PhD in Operations Research from Sapienza University of Rome and extensive contributions to optimization theory, computational mathematics, and applications in finance and engineering. He holds dual qualifications in the Italian National Scientific Qualification (Sector 01/A5 and 01/A6). Education: PhD in Operations Research (2001), Sapienza University of Rome MSc in Electronic Engineering (1997), Sapienza University of Rome High School Diploma (1986), Liceo Scientifico B. Pascal, Rome Research Focus: His work spans optimization algorithms (e.g., conjugate gradient, Newton-Krylov), stochastic models for agent interactions, and applications in finance (cryptocurrency forecasting), ship design, and machine learning. He has pioneered methods for handling large-scale and nonconvex problems, including preconditioning techniques and hybrid metaheuristics (PSO). Grants & Projects: Key roles in projects like the €1M Interreg MIMOSA (maritime transport) and RITMARE (ship hydrodynamics). Funded by EU, MIUR, and industry partners (e.g., INSEAN, RINA). Awards: Charles Broyden Prize (2009), Best Publication Awards (2009, 2014), ANVUR FFABR Grant (2017). Over 100 peer-reviewed articles and 34 co-authors (9 international). Labs/Teams: Member of BLISS (Ca' Foscari), GNCS (INdAM), and Science of Complex Systems groups. Active in conferences (e.g., SIAM, EUROPT) and editorial roles (Mathematical Methods in Economics and Finance).

• Numerical Analysis
• Partial Differential Equations
• Computational Mathematics
• +3 more

Othmar Koch is a Professor at TU Wien's Institute of Analysis and Scientific Computing, part of the Faculty of Mathematics and Geoinformation. His research focuses on numerical analysis, computational methods for differential equations, and quantum mechanics simulations. He has contributed to adaptive integrators, exponential time propagation methods, and software development for boundary value problems (e.g., bvpsuite2.0). Education and affiliations: Primary affiliation is TU Wien's Institute of Analysis and Scientific Computing. Collaborations include projects on solar cell modeling, Bose-Einstein condensates, and nonlinear wave equations. Research interests emphasize numerical stability, adaptive algorithms, and computational techniques for stiff equations and quantum systems. His work bridges theoretical analysis with practical software tools for solving complex mathematical models. Key contributions include adaptive exponential integrators, Magnus-type methods for quantum systems, and continuation methods for parameter-dependent boundary value problems. He has advised students on topics like collocation methods (Merlin Fallahpour), singular BVPs (Amir Saboor Bagherzadeh), and semi-infinite interval eigenvalue problems (Christa Simon). The Network Lab may be associated with his research activities.

Hassane SadokView profile

Professor

• Numerical Analysis
• Iterative Methods
• Linear Algebra
• +5 more

Hassane Sadok is a Professor and President at the University of the Littoral Côte d'Opale (ULCO), France. He is affiliated with the Department of Mathematics within the College of Science, where he contributes to teaching and research in numerical analysis and scientific computing. Position: Professor Institution: University of the Littoral Côte d'Opale (ULCO) Administrative Role: President since 2016 Department: Mathematics School: College of Science His research focuses on numerical linear algebra, particularly iterative methods for solving large linear systems, Krylov subspace methods (such as GMRES, Arnoldi, and CMRH), and regularization techniques for ill-posed problems. He has made significant contributions to the convergence analysis of minimal residual and orthogonal projection methods. His work bridges theoretical numerical analysis with practical computational algorithms. The recent publications reflect a strong trend in developing and analyzing block and global iterative solvers for multiple linear systems, convergence theory for eigenvalue problems, and regularization strategies like the L-curve and greedy Tikhonov methods. These works are grounded in applied mathematics and computational science, with applications in engineering and data analysis. Although no formal awards are listed in the provided texts, his leadership as university president and sustained research output indicate recognition in both academic and administrative domains. He advises graduate students in numerical analysis and computational mathematics, though specific names are not listed. He has not disclosed information on research grants. His teaching includes Master’s level courses in interpolation, quadrature, and numerical resolution of nonlinear equations. While no specific research lab or team is named, his collaborations with researchers like K. Jbilou, Y. Saad, and L. Reichel suggest active participation in a research group focused on numerical methods, likely within the Laboratoire de Mathématiques Pures et Appliquées (L.M.P.A.) at ULCO.

Patrick KürschnerView profile

Associate Professor

• Numerical Linear Algebra
• Model Order Reduction
• Eigenvalue Problems
• +8 more

Patrick Kürschner is an Associate Professor for Numerical Mathematics and Linear Algebra at Leipzig University of Applied Sciences (HTWK Leipzig) . He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and KU Leuven's Kulak Kortrijk Campus. His work focuses on computational methods in systems and control theory. PhD in Mathematics (Otto-von-Guericke University, 2015) MSc in Mathematics (Chemnitz University of Technology, 2010) BSc in Financial Mathematics (Chemnitz University of Technology, 2008) His research interests span numerical linear algebra, matrix equations and functions, eigenvalue problems, preconditioning, model order reduction, tensor methods, and numerical algorithms for digital signal processing. He develops efficient computational techniques for large-scale systems in control theory and data science applications. Recent publications include advancements in low-rank ADI iteration, time-limited balanced truncation, inexact linear solvers, and tensor decompositions for polynomial systems. His work addresses challenges in computational neuroscience, mechanical simulations, and optimal control.

Dmitrij RappoportView profile

Researcher

• Chemistry
• Theoretical Chemistry
• Reaction Networks
• +3 more

Dmitrij Rappoport is a researcher affiliated with the University of California, Irvine . His work spans theoretical chemistry, reaction networks, and molecular properties, with a focus on quantum chemistry and computational methods. University of Karlsruhe, 2007 (Dr. rer. nat., Theoretical Chemistry) University of Karlsruhe, 2003 (Diplom, Chemistry) His research explores complexity in chemical systems, including reaction network modeling, nonlinear dimensionality reduction for reactive coordinates, and quantum chemical approaches to thermodynamics. He has developed open-source tools like Libkrylov and contributed to the TURBOMOLE software suite. Recent publications include studies on non-Kasha fluorescence, enzyme discovery via machine learning, and reaction mechanisms in dinitrogen reduction. His work bridges computational chemistry, materials design, and biochemical applications. Scientific Awards: Postdoctoral Research Award of the Physical Chemistry Division of the ACS (2010) Proctor & Gamble Graduation Award (2003) German National Academic Foundation Fellowship (1998-2001) He has contributed to software development and computational methodologies, with a particular emphasis on accurate modeling of excited states, charge transport, and reaction dynamics. Collaborative projects include bioluminescence mechanism analysis and machine learning applications in protein function prediction.