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Holger Dette is a Professor and Chair Holder of Stochastics (specializing in Statistics) at the Faculty of Mathematics, Ruhr University Bochum. He leads the prominent Group Dette within the Institute of Statistics, overseeing a team of researchers, doctoral students, and administrative staff including Birgit Tormöhlen as team assistant. His research group is deeply integrated within the university's mathematical ecosystem, collaborating with other research groups across algebra, analysis, numerics, and topology. Dette's research spans mathematical statistics with strong applications in real-world problems. His primary interests include optimal experimental design, time series analysis, functional data, change point problems, nonparametric regression, biostatistics, special functions, goodness-of-fit tests, and random matrices . His work bridges theoretical statistics with practical applications, particularly evident in his collaborations with pharmaceutical giants Novartis and Bayer AG in biostatistics, as well as Quasol, a spin-off company from his statistics institute. His recent publications (2024-2025) reveal a research program increasingly focused on high-dimensional and functional data analysis, privacy-preserving statistics, and novel methodological approaches to longstanding statistical problems. Dette's work shows strong interdisciplinary connections, particularly with biomechanics (analyzing joint angles during fatigue phases) and data science (addressing challenges in the era of big data). His research group is actively involved in multiple DFG-funded projects including the newly established 'Small Data' collaborative research center (Sonderforschungsbereich 1597) and the Spatio-temporal Statistics for the Transition of Energy and Transport (Transregio 391). Dette has received significant recognition including the prestigious Humboldt Research Award . His paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors' achieved second place at the CSAW'24 Applied Research Competition MENA. His research group has also secured multiple significant funding awards from the German Research Foundation (DFG). As an advisor, Dette supervises numerous doctoral and master's students including Pascal Quanz, Marius Kroll, and Carina Graw. His group offers statistical consulting services for scientists and students across bachelor's, master's, and doctoral phases. The group maintains strong industrial partnerships, particularly in biostatistics applications, demonstrating Dette's commitment to translating theoretical statistics into practical solutions for real-world challenges.

• Mathematical Statistics
• Applied Statistics
• Probability Theory
• +6 more

Prof. Thomas Weiland is a Full Professor of Computational Electromagnetics at the Technische Universität Darmstadt since 1989. His research focuses on numerical methods, computational engineering, and multiphysics simulation techniques, particularly in accelerator physics and beam dynamics. He holds a Dr.-Ing. from TU Darmstadt and has held postdoctoral and research positions at CERN and TU Darmstadt. His work includes pioneering contributions to electromagnetic field simulations, including advanced finite element methods, discontinuous Galerkin techniques, and boundary element approaches. Education highlights include his Diplom in Electrical Engineering from TU Darmstadt (1975) and a Habilitation in Experimental Physics from the University of Hamburg (1984). His research spans computational electromagnetics, accelerator physics, and numerical methods for electromagnetic field problems. Notable areas of innovation include transparent boundary conditions, eigenmode calculations, and high-performance simulation frameworks for rotating systems and particle accelerators. His publications emphasize advancements in electromagnetic simulation tools, such as the MagPEEC method and Trefftz-discontinuous Galerkin approaches. Collaborative projects include modeling RF photoinjectors for light sources and electrohydrodynamic droplet dynamics. Technical contributions also extend to wake field analysis in particle accelerators and SAR distribution studies in bioelectromagnetics. Research interests further include multiphysics coupling (thermal-electromagnetic effects in surge arresters), stochastic modeling of electromagnetic systems, and field-circuit co-simulation techniques. His work addresses challenges in large-scale eigenvalue problems, adaptive mesh optimization, and high-precision numerical methods for complex geometries.

• Numerical Methods
• Computational Engineering
• Multiphysics Simulation Techniques
• +2 more

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +3 more

Massimiliano Pontil is a Professor at University College London's Department of Computer Science within the Faculty of Mathematical & Physical Sciences, with additional affiliation at the Italian Institute of Technology in Genoa. He leads cutting-edge research at the intersection of machine learning and dynamical systems theory. His research focuses on developing theoretical frameworks for learning transfer operators of stochastic dynamical systems using kernel methods. Pontil's work provides rigorous spectral learning bounds and addresses representation learning challenges in modeling time-evolving phenomena. His research spans both theoretical foundations and practical applications in data-driven science and engineering. Pontil's recent publications reveal a strong emphasis on Koopman operator theory, with particular attention to spectral analysis, error bounds, and efficient algorithms for large-scale dynamical systems. His work connects kernel methods with dynamical systems theory to create mathematically grounded approaches for forecasting and understanding temporal phenomena. His scientific contributions include multiple publications in top-tier venues including NeurIPS and ICLR, with recent work on invariant representations, long-term forecasting, and randomized algorithms for operator regression. Pontil collaborates extensively with researchers including Vladimir Kostic, Karim Lounici, and Pietro Novelli, leading a productive research group in this specialized area of machine learning theory.

• Machine Learning
• Dynamical Systems
• Optimal Transport
• +5 more

Prof. Dr. Gert Lube is a faculty member at the Institute for Numerical and Applied Mathematics (NAM) within the Faculty of Mathematics and Computer Science at Georg-August-University Göttingen. His research focuses on numerical methods for partial differential equations , with emphasis on stabilized finite element methods , turbulence modeling , and magnetohydrodynamics (MHD) . Workshops Organized : Calibration of Viscosity Models for Turbulent Flows (2010), Variational Multiscale Methods (2008), Local Projection Stabilization (2008), BAIL Conferences. His academic contributions include 15+ publications since 2010 on topics like Navier-Stokes simulations , LES/VMS methods , stabilized FEM , and FEM-BEM coupling . Collaborations span institutions like TU Graz, Saarbruecken University, and DLR Göttingen. Key Research Areas : Finite Element Methods, Turbulence Modeling, MHD, Incompressible Flows, Singularly Perturbed Problems, Parallel Computing. Advisees include PhD candidates working on topics such as non-isothermal flows , mass conservation , hybrid RANS/LES , and domain decomposition .

• Numerical Methods for Partial Differential Equations
• Computational Fluid Dynamics
• Turbulence Modeling
• +2 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.

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

Haim Avron is a Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences, where he has been employed since 2015. His research focuses on numerical computing, high-performance computing, and their applications in scientific computing and machine learning. Education: Completed PhD in Computer Science at Tel Aviv University under Prof. Sivan Toledo, followed by postdoctoral research at IBM T.J. Watson Research Center. Research interests: Foundations of numerical linear algebra and randomized algorithms Tensor-tensor algebra for multiway data representation High-performance computational methods for machine learning Optimization techniques for large-scale systems Recent publications demonstrate strong focus on tensor algebra, randomized numerical methods, and machine learning optimization, with applications ranging from quantum computing to deep learning architectures. Awards: SIAM Activity Group on Computational Science and Engineering Best Paper Prize 2025 Software contributions include development of numerical libraries such as libSkylark for matrix sketching and Blendenpik for least-squares problems.

• Numerical Computing
• High Performance Computing
• Machine Learning
• +3 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.

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

Alexander Tichai is an ERC Group Leader at the Technical University of Darmstadt's Institute of Nuclear Physics, leading the DeformedNuclei research group. His academic journey includes a PhD in Physics (2017) and dual bachelor's and master's degrees in Physics and Mathematics from TU Darmstadt. Prior to his current role, he held postdoctoral positions at CEA Saclay/Université Paris-Saclay (2018-2019) and TU Darmstadt (2019-2025). His research focuses on nuclear many-body theory , including open-shell nuclei, symmetry-broken correlation expansions, and tensor factorization techniques. He specializes in ab initio methods for nuclear structure and dynamics, with applications to neutron stars, neutrinos, and dark matter. Key techniques include coupled-cluster theory, Bogoliubov perturbation methods, and low-rank matrix decompositions. His work bridges theoretical physics and computational science, addressing challenges in heavy-nuclei simulations and precision nuclear modeling. Collaborations include the EUSTRONG and ChiralEFT4DM projects, supported by ERC funding. Tichai’s research also explores interdisciplinary applications like eigenvector continuation and quantum information entropy. He advises the DeformedNuclei research group at TU Darmstadt and contributes to the STRONGINT initiative. His publications emphasize computational advancements for nuclear many-body problems, including data compression and numerical stability in large-scale simulations.

• Nuclear Physics
• Many-Body Theory
• Theoretical Physics
• +3 more

Prof. Dr. rer. nat. Patrick Kürschner is a faculty member at the Mathematisch-Naturwissensch. Zentrum of Leipzig University of Applied Sciences (HTWK Leipzig) . He teaches mathematics and programming for engineering programs including Civil Engineers, BIM, INB, INM, and BIK. His office hours are on Tuesdays from 2:00-3:00 p.m. and by appointment. Research Focus : His work centers on Numerical and applied multilinear algebra with applications to large-scale eigenvalue problems, systems of equations, matrix equations, and matrix functions. He specializes in preconditioning techniques and low-rank approaches for matrix/tensor methods, solution methods for polynomial equations, mathematical systems and control theory (particularly model order reduction and linear-quadratic control), numerical optimization, digital signal processing, data science, and machine learning. Publication Trends : Dr. Kürschner's research spans Numerical Linear Algebra , Control Theory , and Scientific Computing . His work addresses problems in Large-scale Matrix Equations , Model Order Reduction , Polynomial System Solutions , and Low-rank Approximation for applications in Engineering and Data Science . Recent publications focus on efficient numerical methods for Lyapunov and Sylvester equations, time-limited balanced truncation, and connectome regression techniques.

• Numerical and applied multilinear algebra
• Large-scale eigenvalue problems
• Systems of equations
• +11 more

Martin Köhler is a researcher at the Max Planck Institute for Dynamics of Complex Technical Systems, specializing in computational methods within systems and control theory. His work focuses on leveraging modern computer architectures for solving generalized eigenvalue problems and large-scale matrix equations efficiently. Research Interests: Parallel algorithms for system and control theory Generalized eigenvalue problems on advanced computing architectures High-performance computing for matrix equations Multicore and multi-GPU programming Contact: koehlerm@mpi-magdeburg.mpg.de

• Parallel Computing
• Systems Theory
• Control Theory
• +5 more

Dr. Ivo Dravins is a PostDoc at the Chair of Numerical Analysis within the Faculty of Mathematics at Ruhr-Universität Bochum, working in Prof. Katharina Kormann's research group. He focuses on preconditioning techniques for implicit time-stepping algorithms, particularly within the PDExa project. His research spans numerical linear algebra, implicit Runge-Kutta methods, and PDE-constrained optimization. Research Interests: Preconditioning of large-scale linear systems Implicit time-stepping algorithms for PDEs High-order numerical methods Optimal control problems with constraints Numerical linear algebra applications Key Research Trends: His work emphasizes scalable preconditioning strategies for parallel computing environments, with a focus on achieving high-order accuracy in time integration. Recent efforts have addressed stage-parallel Runge-Kutta implementations and spectral analysis of preconditioned matrices in PDE-constrained optimization contexts. Labs/Teams: Member of Prof. Kormann's Numerical Analysis group, contributing to the PDExa project. Associated with the Kormann Group within the Faculty's Numerics division.

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

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

• Numerical Linear Algebra
• Scientific Computing
• Parallel Computing
• +7 more

Divya Sri Praturi is a Scientist in the Climate Surface Interaction research group within the Department of Climate Physics at the Max Planck Institute for Meteorology (MPI-M) in Hamburg, Germany. She works as a postdoctoral researcher on the EU-funded NextGEMS project under the supervision of Prof. Dr. Bjorn Stevens, focusing on high-resolution climate modeling. Her primary research interests span multiple domains of fluid dynamics, including geophysical, astrophysical, and engineering flows. She specializes in turbulence theory, magnetohydrodynamics, compressible flows, and climate dynamics. Her current work investigates the breakdown mechanisms of the Intertropical Convergence Zone (ITCZ) using kilometer-scale coupled atmosphere-ocean-land simulations. Dr. Praturi's publication record demonstrates expertise across theoretical fluid dynamics and applied climate science. Her recent work shows a clear transition from fundamental turbulence research to climate modeling applications, with a focus on understanding how small-scale processes influence large-scale climate phenomena. She has made significant contributions to both compressible turbulence theory and high-resolution climate modeling. Alexander von Humboldt Postdoctoral Fellowship As a postdoctoral researcher, Dr. Praturi has secured funding through the prestigious Alexander von Humboldt Foundation and is currently contributing to the EU's NextGEMS project. Her research bridges theoretical fluid dynamics with practical climate modeling applications, working within collaborative international research frameworks. She is actively involved in the Climate Surface Interaction group at MPI-M, contributing to the institute's mission of advancing climate science through high-resolution modeling approaches. Her work connects fundamental fluid dynamics principles with pressing climate research questions, particularly regarding tropical climate dynamics and scale interactions in Earth system models.

• Fluid Dynamics
• Climate Physics
• Turbulence
• +4 more