Fast Fourier-based Algorithms Professors and Supervisors in Germany

Sriramkrishnan Muralikrishnanمشاهده پروفایل

پژوهشگر

Jülich Research CenterGermany

Sriramkrishnan Muralikrishnan is a Research Staff member at the Department of Mathematics and Education within the Jülich Supercomputing Center (JSC) at Forschungszentrum Jülich, Germany. His work focuses on developing advanced computational methods for high-performance scientific computing, particularly in plasma physics and related multi-physics applications. Dr. Muralikrishnan's research spans several key computational domains: Numerical Analysis and High-Order Methods High Performance Scientific Computing for Exascale Architectures Plasma Physics Simulations Fast Solvers and Preconditioners Parallel-in-Time Integration Techniques Performance Portable Programming His recent publications demonstrate a strong focus on particle-based computational methods, particularly Particle-in-Cell and Particle-in-Fourier techniques. His work consistently addresses challenges in energy conservation, scalability across architectures, and noise reduction in plasma simulations. A significant portion of his research involves developing performance-portable frameworks that can efficiently leverage modern supercomputing hardware from different vendors without code rewrites. Dr. Muralikrishnan is actively involved in open-source scientific software development: Lead developer of IPPL (a performance portable library for grids and particles) Developer of OPAL (an open-source particle accelerator library) His research has direct applications in plasma physics, fusion energy research, and advanced accelerator design, with a strong emphasis on making computational methods accessible through open-source development and advocating for diversity in scientific computing.

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Franziska Nestlerمشاهده پروفایل

پژوهشگر

Chemnitz University of TechnologyGermany

Franziska Nestler is a Researcher at the Department of Mathematics , Chemnitz University of Technology , with a focus on applied functional analysis and computational methods. She leads the BMBF junior research group SALE (Fast Algorithms for explainable recommendation systems) and contributes to E-learning initiatives at the Faculty of Mathematics. Education : Diploma in Mathematics (2012), PhD in Applied Functional Analysis (2018) Research interests center on fast Fourier-based algorithms for electrostatic interactions in particle systems, machine learning with interpretable kernel methods, and E-learning tools for mathematics education. Her work bridges numerical analysis, computational physics, and pedagogical innovation. Key scientific awards include the Best Poster Award at the Strobl18 Workshop. She has collaborated on projects like DFG's Fast Fourier-based Coulomb solvers and BMBF's Qualitätspakt Lehre for teaching digitalization. Advising : Supervised Master’s and Bachelor’s theses in fast summation methods and inverse NFFT techniques

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Gerlind Plonkaمشاهده پروفایل

استاد

University of GöttingenGermany

Gerlind Plonka is a Professor of Applied Mathematics at the University of Göttingen, specifically within the Institute for Numerical and Applied Mathematics (NAM). Her research focuses on Numerical Fourier Analysis Wavelet Theory Regularization and Nonlinear Diffusion Methods Fast Algorithms and Numerical Stability Signal and Image Processing Applications Her recent publications emphasize structured subsampling in Fourier domains, Prony-type methods for exponential sum recovery, and deep learning integration in medical imaging. She supervises active PhD candidates including Benjamin Kocurov, Anahita Riahi, Yannick Nicola Riebe, and Janina Schmidt, with a legacy of advising over 50 graduates across diverse topics like Sparse FFT Algorithms Phase Retrieval Constraints Wavelet-Based Image Compression Nonlinear Diffusion Filters High-Dimensional Data Approximation

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Karl Bringmannمشاهده پروفایل

استاد

Max Planck Institute for InformaticsGermany

Karl Bringmann is a Professor at Saarland University since November 2019 and is affiliated with the Max Planck Institute for Informatics, where he works in the Department of Algorithms and Complexity. He has established himself as a leading researcher in theoretical computer science, particularly in fine-grained complexity and algorithm design. His work bridges theoretical insights with practical applications in optimization problems. Bringmann's research focuses on conditional lower bounds (often based on the Strong Exponential Time Hypothesis) and algorithm design, with particular emphasis on optimization problems, string algorithms, and computational geometry. His work has significant implications for fundamental problems like Subset Sum, Knapsack, and Integer Programming, with applications ranging from scheduling to post-quantum cryptography. He develops innovative approaches combining modern algorithmic techniques, mathematical structure theory, and fine-grained complexity to design faster algorithms and establish optimality. His publication record shows a consistent trend toward developing near-optimal algorithms for fundamental problems, with significant contributions to fine-grained complexity theory. His work often establishes tight conditional lower bounds while simultaneously providing matching upper bounds, creating a comprehensive understanding of problem complexity. He has made notable advances in string algorithms (particularly edit distance), geometric problems, and optimization. ERC Starting Grant 2019: Technology Transfer between Integer Programming and Efficient Algorithms (TIPEA) EATCS Presburger Award for Young Scientists 2019 Heinz Maier-Leibnitz-Prize 2019 EATCS Distinguished Dissertation Award 2015 Google European Doctoral Fellowship 2012-2014 Bringmann leads the ERC-funded TIPEA project (2019-2024), which investigates fundamental optimization problems with the goal of developing next-generation industrial solvers. He advises several PhD students including Nick Fischer, Alejandro Cassis, and Vasileios Nakos, and has served on numerous program committees for top theoretical computer science conferences including STOC, FOCS, SODA, and ICALP. His teaching includes advanced courses on Fine-Grained Complexity Theory and Competitive Programming.

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Dirk Pflügerمشاهده پروفایل

استاد

University of StuttgartGermany

Dirk Pflüger is a Professor at the University of Stuttgart's Institute of Parallel and Distributed Systems, within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on high-performance computing (HPC), parallel and distributed systems, and sparse grids. He has led projects in astrophysical simulations, machine learning applications, and uncertainty quantification. Notable contributions include developing scalable algorithms for exascale computing using HPX, Kokkos, and SYCL frameworks. His expertise spans distributed computing architectures, task-based parallel programming, and interdisciplinary applications in astrophysics and medical AI. Recent work includes optimizing hyperparameter tuning, simulating stellar mergers, and enhancing blood glucose prediction models using deep reinforcement learning. Pflüger's research emphasizes performance portability, fault tolerance, and cross-platform collaboration. He has contributed to open-source tools like PLSSVM and hws, which address hardware monitoring and GPU acceleration challenges. His work bridges theoretical advancements with practical implementations for real-world computational problems.

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Gerlind Plonka-Hochمشاهده پروفایل

استاد

University of GöttingenGermany

Gerlind Plonka-Hoch is a Professor of Applied Mathematics and Deputy Director of the Institute for Numerical and Applied Mathematics (NAM) at the University of Göttingen. She leads research in numerical analysis and signal processing, with a focus on mathematical methods for image reconstruction and analysis. Her work bridges theoretical mathematics with practical applications in medical imaging and data science. Professor Plonka-Hoch's research spans several key areas in applied mathematics: Numerical Fourier analysis and fast algorithms Wavelet theory and sparse signal representation Regularization methods and nonlinear diffusion Applications in signal and image processing, particularly medical imaging Phase retrieval and parameter estimation problems Her recent publications demonstrate a strong focus on medical imaging applications, particularly using Optical Coherence Tomography (OCT) data. She has developed innovative approaches combining wavelet analysis, deep learning, and sparse representation techniques for image reconstruction and classification. Her work on ESPIRA (Estimation of Signal Parameters by Iterative Rational Approximation) has provided new methods for reconstructing exponential sums from limited data, with applications across multiple scientific domains. Professor Plonka-Hoch leads an active research group at the University of Göttingen, mentoring several doctoral students including Dr. Yurii Kolomoitsev, M.Sc. Benjamin Kocurov, M.Sc. Anahita Riahi, M.Sc. Yannick Nicola Riebe, and M.Sc. Janina Schmidt. Her working group has produced numerous publications on numerical methods and their applications, with a strong emphasis on both theoretical foundations and practical implementation.

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Yoann Le Hénaffمشاهده پروفایل

پژوهشگر

University of TübingenGermany

Yoann Le Hénaff is a postdoctoral researcher at the Numerical Analysis Department of the University of Tübingen, Germany. He works within the CRC TRR 352's B4 project and collaborates with C. Lubich's group. His research focuses on computational mathematics and physical modeling through numerical analysis. Education: PhD in Mathematics (2024), University of Rennes, France Master's degree in Mathematics, University of Rennes, France Engineering degree in applied mathematics, INSA Rennes, France Research Interests: Specializing in partial differential equations and numerical simulations, Le Hénaff develops grid-free particle methods for the Vlasov-Poisson system and studies modulated soliton solutions for nonlinear Schrödinger equations. Key subfields include spectral concentration problems, Dirac-Frenkel variational principles, and computational complexity optimization. Publication Trends: Recent work demonstrates expertise in hybrid numerical methods combining classical PDE approaches with modern computational techniques. 2023-2025 publications show progression from particle method convergence proofs to advanced soliton-based algorithms and spectral concentration generalizations across multiple dimensions. Collaborations: Actively involved in the CRC TRR 352 consortium, participating in annual retreats and contributing to the B4 project's objectives. Regularly presents at international seminars including the NAG seminar in Tübingen and IRMAR doctoral meetings in Rennes.

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Dániel Marxمشاهده پروفایل

استاد

Max Planck Institute for Software SystemsGermany

Dániel Marx is a tenured Professor at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. He leads research in the area of Algorithmic Foundations and Cryptography, with a focus on parameterized algorithms and computational complexity. He has held previous positions at the Max Planck Institute for Informatics and the Institute for Computer Science and Control of the Hungarian Academy of Sciences (MTA SZTAKI). His work is highly theoretical, aiming to understand the precise complexity of algorithmic problems, especially on bounded-treewidth graphs and in parameterized settings. PhD: Budapest University of Technology and Economics, 2005 Postdoc: Tel Aviv University, Humboldt University Berlin Senior Research Fellow: MTA SZTAKI, 2012–2019 Senior Researcher: Max Planck Institute for Informatics, 2019–2020 Faculty: CISPA Helmholtz Center, 2020–present His research interests lie at the intersection of theoretical computer science and discrete mathematics. He is particularly known for his work on parameterized algorithms , fine-grained complexity , and lower bounds . His group investigates algorithmic graph theory problems, including domination, independence, homomorphism, and clustering, especially under structural constraints like bounded treewidth. He has pioneered techniques in dynamic programming, kernelization, and hardness proofs based on the Exponential Time Hypothesis. The recent publications of Dániel Marx reveal a consistent focus on establishing tight complexity bounds for fundamental algorithmic problems. His work spans from exact and parameterized algorithms to approximation and counting problems. A recurring theme is the analysis of problems on bounded-treewidth graphs, where he explores the boundary between tractable and intractable cases. He also contributes to network design (e.g., Steiner problems), clustering, and subgraph counting, often providing complete classifications of complexity based on parameters. European Research Council Starting Grant European Research Council Consolidator Grant Humboldt Research Fellowship for Experienced Researchers Dániel Marx has advised numerous researchers and collaborated widely across institutions. His research is supported by prestigious grants, including ERC grants that funded his group at MTA SZTAKI. He actively leads a research group at CISPA focused on parameterized algorithms and complexity. While specific PhD students are not listed in the provided text, his extensive co-authorship network indicates a strong mentoring and collaborative presence. His work has significant implications for the foundations of computer science and algorithm design. He leads the research group on Parameterized Algorithms and Complexity at CISPA, continuing his long-standing focus on theoretical algorithm design and analysis. His team works on foundational problems in graph algorithms and complexity theory, aiming to develop new algorithmic techniques and understand the limits of efficient computation.

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Jan Hamaekersمشاهده پروفایل

پژوهشگر

Rhenish Friedrich Wilhelm University of BonnGermany

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

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Toni Volkmerمشاهده پروفایل

پژوهشگر

Chemnitz University of TechnologyGermany

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

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Markus Heglandمشاهده پروفایل

استاد

Technical University of MunichGermany

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

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Michael Quellmalzمشاهده پروفایل

پژوهشگر

Technical University of Berlin (TU Berlin)Germany

Dr. Michael Quellmalz is a Researcher in Applied Mathematics at the Technical University of Berlin (TU Berlin), affiliated with Faculty II - Mathematics and Natural Sciences and the Institute of Mathematics. His research focuses on inverse problems, tomography, optimal transport, and Fourier analysis with applications in medical imaging and computational geometry. He actively contributes to the SFB Tomography Across the Scales collaborative research center, advancing reconstruction techniques in diffraction tomography and motion detection. He teaches a variety of courses including Analysis II for Engineering, Harmonic Analysis I, and Numerical Mathematics, emphasizing both theoretical foundations and their practical implementations. His work has been recognized through awards such as the Universitätspreise 2020 and a Digital Fellowship for innovative educational contributions in adaptive feedback systems. Key publications include advancements in sliced optimal transport on spheres, motion detection algorithms, and Fourier-based reconstruction methods. Dr. Quellmalz's research group develops MATLAB toolboxes for applications like FourierODT and NFFT-Sinkhorn, reflecting his commitment to bridging mathematical theory with computational tools. His academic journey includes a PhD from TU Chemnitz (2019) and extensive participation in international conferences, workshops, and collaborative projects across Europe.

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Stéphane Berbenni Vincent Taupinمشاهده پروفایل

استاد

Max Planck Institute for Sustainable MaterialsGermany

Stéphane Berbenni Vincent Taupin serves as Professor at University of Lorraine's School of Engineering within Department 2 IMPACT (Ingénierie des Microstructures, Procédés, Anisotropie, ComportemenT), part of the LEM3 laboratory (UMR CNRS 7239). The department focuses on micromechanics, material processes, and structural behavior. Research centers on developing advanced micromechanical frameworks combining Fourier transform methods with continuum dislocation mechanics. Key contributions include the MFDM-EVPFFT model that integrates Elasto-Visco-Plastic Fast Fourier Transform formulations with Mesoscale Field Dislocation Mechanics theory. This approach captures geometrically necessary dislocation (GND) and statistically stored dislocation (SSD) dynamics in polycrystalline materials, enabling simulation of grain size effects and interface phenomena in two-phase composites. Current work investigates GND density evolution through filtered numerical spectral approaches coupled with stress equilibrium algorithms. Recent publications address small-angle scattering patterns in Ni-based superalloys using phase-field models, demonstrating applications in microstructure characterization. The research group operates within LEM3's extensive infrastructure serving 250 researchers across 50 national and 80 international projects. As part of University of Lorraine's engineering faculty, the position involves collaboration with CNRS and Arts et Métiers institutions. The laboratory maintains three technology platforms supporting mechanical rheology, microscopy, and computational processing for materials research.

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