Philipp Bringmann is a Researcher at the Institute of Analysis and Scientific Computing (E 101) at TU Wien, Austria. His work focuses on numerical methods for partial differential equations (PDEs), with specializations in adaptive finite element methods (FEM), least-squares formulations, and discontinuous Petrov-Galerkin techniques. He holds a PhD in Mathematics from Humboldt-Universität zu Berlin (2020) and has held postdoctoral positions at both TU Wien and Humboldt-Universität. Research Interests : Numerical solution of PDEs Least-squares finite element methods Adaptive mesh refinement Iterative linearization techniques Applications in computational mechanics Teaching : Current: Least-Squares Finite Element Methods (Winter 2024/25) Past: Numerical methods for PDEs (Winter 2023/24) Software Contributions : octAFEM3D: 3D adaptive finite element software MooAFEM: MATLAB-based adaptive FEM toolkit Recent Talks : Presented work on adaptive FEM with inexact solvers at the Chemnitz Finite Element Symposium 2024 .
Michael Feischl is a Professor for Computational PDEs at TU Wien (since 2022) and holds an ERC Consolidator Grant for his project "New Frontiers in Optimal Adaptivity" (2024–2029). His research focuses on partial differential equations with random coefficients, computational micromagnetism (Landau-Lifshitz-Gilbert equation), and optimal adaptive mesh refinement techniques. He leads the Computational PDEs research group within the Institute of Analysis and Scientific Computing. Education and career highlights include roles as Associate Professor at TU Wien (2019–2022), W2 Professor at University of Bonn (2017–2018), and Junior Research Group Leader at KIT (2015–2017). His work bridges numerical analysis, computational physics, and machine learning, with a strong emphasis on rigorous mathematical foundations and algorithmic efficiency. Research interests include: Adaptive finite element and boundary element methods Stochastic modeling and uncertainty quantification Computational methods for micromagnetic simulations Machine learning applications in numerical analysis His recent work explores optimal adaptivity for time-dependent PDEs, neural network-based solvers, and efficient discretization strategies for complex physical systems. Key contributions include advancements in a posteriori error estimation and hierarchical training of neural networks.
Michael Innerberger is a researcher at TU Wien's Faculty of Mathematics and Geoinformation , affiliated with the Research Group Numerics of PDEs . He holds a Dr.techn. (PhD) and Dipl.-Ing. in engineering. His work focuses on adaptive finite element methods (FEM) , numerical analysis , and multigrid solvers for partial differential equations (PDEs), with an emphasis on achieving optimal computational complexity and robust error estimation. Recent contributions include the development of hp-robust multigrid solvers and the MooAFEM MATLAB framework for higher-order adaptive FEM. His research addresses both symmetric and nonsymmetric PDEs, including semilinear and nonlinear cases. Education: Dipl.-Ing. (TU Wien), Dr.techn. (2022, TU Wien) Key Areas: Adaptive mesh refinement, goal-oriented error control, iterative linearization, and numerical software development. Publications highlight advancements in rate-optimal and cost-optimal adaptive algorithms for elliptic PDEs, demonstrating rigorous complexity analysis and practical implementation strategies. Collaborations include Dirk Praetorius, Roland Becker, and Jens Markus Melenk. Labs/Teams: Core member of the Numerics of PDEs research group, contributing to theoretical and computational advancements in scientific computing.
Carl-Martin Pfeiler is affiliated with TU Wien's Research Group Numerics of PDEs (E101-02-2). He holds academic qualifications including Dipl.-Ing. (Master of Engineering) and Dr.techn. (PhD in Technical Sciences). His research focuses on computational micromagnetics, numerical methods for partial differential equations (PDEs), and magnetic skyrmion dynamics. Key contributions include developing the mass-lumped midpoint scheme for skyrmion dynamics simulations and advancing IMEX-type integrators for the Landau-Lifshitz-Gilbert equation. Education: Dipl.-Ing. (Engineering) from TU Wien Dr.techn. (Technical Sciences PhD) from TU Wien Research interests emphasize computational approaches to magnetic phenomena , with a focus on: Numerical analysis of micromagnetic models Algorithm development for efficient simulations (e.g., Commics software) Study of topological spin textures like magnetic skyrmions Stability and convergence of numerical schemes Publications highlight advancements in: Nonlinear dynamics of skyrmions Efficient finite element methods Preconditioning strategies for iterative solvers Chiral skyrmion simulations Collaborations include work with Dirk Praetorius, Michele Ruggeri, and the Commics development team. His work bridges applied mathematics and materials science, addressing challenges in spintronics and nanomagnetism.
Univ.-Prof. Dr. Eva Kopecká is a Professor at the University of Innsbruck's Department of Mathematics, Faculty of Mathematics, Computer Science and Physics. She specializes in Functional Analysis, Geometry of Banach Spaces, Nonlinear Analysis, and Combinatorics. Currently teaching Discrete Mathematics and Introduction to Higher Analysis in the 2024 summer semester, she also leads the Research Seminar in Functional Analysis. Her work focuses on projection algorithms, Lipschitz mappings, and fixed point theory. Notable research includes studies on alternating projections, zone diagrams, and geometric embeddings. She has conducted projects funded by the Austrian Science Fund (FWF), including work on Lipschitz mappings and contraction operators. No formal awards are listed, but her contributions to operator theory and functional analysis are widely recognized. Consultation hours are by email appointment.
Dr. Mher Safaryan is a postdoctoral researcher at the Institute of Science and Technology Austria (ISTA), affiliated with Prof. Dan Alistarh's research group since 2022. Previously, he held postdoctoral positions at King Abdullah University of Science and Technology (KAUST) from 2019-2022 and served as a research technician there from 2016-2019. He earned his Ph.D. in Mathematics from Yerevan State University in 2018 under Prof. Grigori Karagulyan's supervision. Education: Ph.D. in Mathematics (2018, YSU) Past Affiliations: KAUST (2016-2022), Neural Magic/Red Hat (industrial secondment) His research focuses on optimization theory and algorithms for machine learning , particularly in developing communication/computation/memory-efficient methods for large-scale training and federated learning. Key contributions include LDAdam (low-dimensional gradient statistics optimization), GradSkip (accelerated local gradient methods), and Unified Scaling Laws for compressed representations. He has published in top venues like NeurIPS, ICML, ICLR, TMLR, and The Journal of Geometric Analysis. Scientific Awards: Marie Skłodowska-Curie Fellowship (MSCA COFUND IST-BRIDGE) His work bridges machine learning optimization with mathematical foundations from his earlier research in real harmonic analysis. Current collaborations include Prof. Dan Alistarh (ISTA), Dr. Alexandre Marques (Neural Magic), and Prof. Peter Richtárik (KAUST).
Priv.-Doz. Dipl.-Ing.Dr. Paul Surer is a faculty member at the Institute of Mathematics , University of Natural Resources and Life Sciences, Vienna (BOKU). His research spans number theory, fractal geometry, and dynamical systems, with a focus on numeration systems and algebraic structures. Education: University Leoben (2005-2009), Universidade Estadual Paulista (UNESP) (2009-2011) Current Position: Associate Professor at BOKU's Institute of Mathematics (since 2014) Research Interests: Development of triangular labyrinth fractals and their geometric properties Rauzy fractals with measure-theoretic independence Innovations in substitutive number systems and coding prescriptions Representation theory for complex numbers using integer digits Applications in structural engineering via digital twin technology Publication Trends: 15 recent works (2014-2023) show expertise in fractal geometry, algebraic number theory, and interdisciplinary applications to structural analysis. Academic Service: Reviewer for journals like Bulletin of the Belgian Mathematical Society , Journal de Théorie des Nombres de Bordeaux , and Ukrainian Mathematical Journal .
Robert Ernstbrunner is a researcher affiliated with the Faculty of Computer Science , contributing to algorithm development and parallel computing. His work focuses on low-rank approximations and fault-tolerant strategies in linear algebra methods. Research Interests: His research spans computational efficiency, numerical linear algebra, and fault tolerance in high-performance computing environments. Key areas include sparse matrix operations and resilience mechanisms for iterative algorithms. Publications: He has published two peer-reviewed papers: (1) a 2022 study on precision-cost trade-offs in low-rank matrix approximations, and (2) a 2020 framework for node-failure resilience in iterative linear algebra methods. Academic Activities: Robert has presented at international conferences, including the 2022 IEEE IPDPS and the 2020 FTXS workshop.
Albert Christopher is an Associate Professor at the Institute of Theoretical Physics - Computational Physics, Technische Universität Graz. His research focuses on plasma physics, magnetic confinement fusion, and computational modeling of fusion devices such as tokamaks and stellarators. He specializes in symplectic integration techniques, neoclassical transport, and the development of hybrid kinetic-MHD models. Key projects include contributions to the EUROfusion program, analysis of resonant magnetic perturbations, and the design of 3D coil systems for future fusion reactors like EU-DEMO. He holds teaching authorizations in Theoretical Physics and Computational Physics. His work emphasizes plasma stability, edge transport modeling, and Bayesian inference for equilibrium reconstruction in devices like Wendelstein 7-X. Recent research includes studies on bootstrap current convergence in stellarators and the validation of numerical solvers for partially ionized plasmas. Christopher's publications span over a decade, addressing topics such as orbit classification in Hamiltonian systems, plasma response to magnetic perturbations, and uncertainty quantification in MHD simulations. His interdisciplinary approach integrates computational methods with experimental data from facilities like ASDEX Upgrade and TCV.
Petter E. Bjørstad is a Professor of Computer Science and Mathematics at the University of Bergen, Norway, where he has served as Head of the Department of Informatics since 2010. Previously, he was Director of the Bergen Center for Computational Science (2000-2010) and Professor in both the Department of Mathematics (2006-2010) and Department of Informatics (1985-2005). He also held a position as Professor of Mathematics at the University of Minnesota during 1996-1997 on unpaid leave from Bergen. Dr. Bjørstad earned his PhD in Computer Science from Stanford University in 1980, following which he completed a postdoctoral fellowship at the Courant Institute of Mathematical Sciences at New York University. Before entering academia, he worked as a Principal Engineer at Det Norske Veritas from 1981-1985. Dr. Bjørstad's research focuses on numerical analysis and high-performance computing, with particular expertise in domain decomposition methods, parallel algorithms for elliptic partial differential equations, and scientific computing. His work bridges theoretical mathematics with practical computational challenges, especially in industrial applications. He has led significant research projects including 'Multiscale Domain Decomposition: Algorithms and Analysis' (2010-2014), funded by the Research Council of Norway and the University of Bergen. His publication record demonstrates consistent contributions to the field of numerical methods and parallel computing over several decades. The research trends show a strong focus on developing efficient algorithms for solving partial differential equations through domain decomposition techniques, with applications spanning structural analysis, reservoir simulation, semiconductor device modeling, and other industrial contexts. His work increasingly emphasizes practical implementation on modern parallel architectures including SIMD and MIMD systems. Large number of research grants from Norway and the European Union Dr. Bjørstad has been actively involved in mentoring students and collaborating with industry through projects like the Europort effort, where industrial codes were ported to parallel computing platforms. His laboratory for parallel computing (Parallab) has been instrumental in advancing practical applications of high-performance computing since its establishment in 1985 with Europe's first 64-processor Intel hypercube. The lab has evolved to include multiple MIMD machines including an Intel Paragon, Parsytec GC/Power-Plus, and DEC α-cluster. As Head of the Department of Informatics, Dr. Bjørstad leads one of Norway's premier computing research units with expertise spanning theoretical computer science, numerical methods, and practical applications in various scientific domains. His leadership has helped establish Bergen as a significant center for computational science in Europe.
Jörg Thuswaldner is a Professor at the Chair of Mathematics, Statistics and Geometry at Montanuniversität Leoben. His research focuses on fractal geometry, number theory, dynamical systems, and combinatorics, with a particular emphasis on topics such as self-affine tiles, continued fraction algorithms, and digit systems. He has published extensively in top-tier journals such as Advances in Mathematics and Journal of Number Theory , contributing to the understanding of fractal structures, metric number theory, and symbolic dynamics. His editorial roles include serving on the boards of Lithuanian Mathematical Journal and Combinatorics and Number Theory , highlighting his influence in academic publishing. He actively collaborates internationally, hosting visiting scholars and participating in conferences to advance interdisciplinary research. Recent work explores applications of fractal geometry to permeable sets and the analysis of Weyl sums with digital restrictions. Thuswaldner’s research spans over two decades, with publications ranging from foundational studies in tiling theory to cutting-edge analyses of non-standard digit systems. His contributions bridge pure mathematics with applications in algebraic geometry and ergodic theory, making him a leading figure in modern mathematical research.
Prof. Otmar Scherzer is a distinguished academic at the Faculty of Mathematics, Department of Mathematics, University of Vienna . His work spans Inverse Problems, Mathematical Imaging, Computational Mathematics, and Applied Mathematics , with a focus on tomography, neural networks, and medical imaging. Over nearly two decades, he has published extensively in journals like Inverse Problems , SIAM Review , and IEEE Transactions . 2025 : Advanced data-driven regularization and neural network applications in inverse problems. 2024 : Explored diffraction tomography, Hilbert space approaches to scattering, and motion detection algorithms. 2023 : Addressed uncertainty quantification, visco-acoustic imaging, and Doppler ultrasound interpolation. His recent research emphasizes uncertainty-aware blob detection, strain reconstruction in elastography , and Fourier-based tomography . Publications frequently integrate numerical experiments and computational inverse methods , reflecting a blend of theoretical and applied mathematics. Prof. Scherzer collaborates with institutions like RICAM (Johann Radon Institute) and contributes to hybrid imaging modalities such as quantitative optical coherence elastography and photoacoustic tomography . He has supervised projects in 3D shape reconstruction and non-convex regularization , though specific student names are not listed in the provided data.
Elena Resmerita is an Associate Professor at the Institute of Mathematics , Alpen-Adria University of Klagenfurt, Austria. She holds a Habilitation in Mathematics (2013) and a PhD in Mathematics (2003) from the University of Klagenfurt and Haifa respectively. 2013: Habilitation, University of Klagenfurt 2003: PhD, University of Haifa 1997: MSc, Al. I. Cuza University of Iasi Her research focuses on inverse problems , regularization methods for ill-posed problems , image restoration , continuous optimization , convex analysis , and iterative methods for convex feasibility problems . Her recent publications highlight advancements in regularization theory, multiscale decomposition, and sparsity-driven optimization. Key contributions include: Nonconvex regularization techniques Entropic Landweber methods Bregman iterations for image processing Convergence analysis of stochastic algorithms She has received prestigious scientific awards : FWF Elise Richter Fellowship IPAM Fellow at UCLA Active in academic service, she co-organizes workshops like the Alps-Adriatic Workshop in Inverse Problems and contributes to the European Women in Mathematics (EWM) as Deputy Convenor and committee member. Her teaching spans Analysis, Calculus, and specialized inverse problem courses.
Carlos Andres Pachajoa Mejia is a faculty member in the Faculty of Computer Science and part of the Research Group Theory and Applications of Algorithms . His work focuses on algorithm design and fault tolerance in high-performance computing environments. Research Areas : Algorithms, Linear Algebra, Fault-Tolerant Computing, Parallel Processing. Key Contributions : Development of node-failure resilience strategies for iterative linear algebra methods, including the Conjugate Gradient Method and checkpoint-recovery techniques. His publications from 2018–2020 reveal a consistent focus on enhancing the reliability of numerical algorithms in distributed systems. Collaborations with researchers like Gansterer and Levonyak underscore his team-based approach to solving computational challenges.
Franziskus Wiesnet is a researcher at the Institute of Discrete Mathematics and Geometry at the Technische Universität Wien, where he currently serves as project manager for the FWF-funded project 'Material Interpretation'. His academic journey includes a mathematics degree from Ludwig-Maximilians-Universität München (2012-2017) and a joint doctorate from the Universities of Trento, Verona, and Munich (2017-2021), supervised by Peter Schuster and Helmut Schwichtenberg. His research focuses on proof theory, constructive mathematics, and formal systems, with particular emphasis on proof assistants like Minlog and Agda. Key areas include constructive algebra, analysis, and the computational content of mathematical proofs. He has contributed to topics such as program extraction from proofs, proof mining, and algorithmic approaches to algebraic structures like maximal ideals and Zariski's lemma. Recent work includes a conference paper on maximal ideals in ℤ[X] and a YouTube channel (LogicLab) offering tutorials on proof assistants and constructive mathematics. His publications span journals like Logical Methods in Computer Science and Information and Computation, with a focus on bridging theoretical logic and computational applications.
