جست‌وجوی استاد و پژوهشگر

• Numerical Mathematics
• Finite Element Analysis
• Optimal Control
• +۳ مورد دیگر

Richard Franz Löscher is a researcher at the Institute of Applied Mathematics specializing in numerical methods for optimal control problems. He holds a BSc, Diplom-Ingenieur (Dipl.-Ing.), and Doctorate in Technical Sciences (Dr.techn.), demonstrating extensive technical education and engineering expertise. His educational qualifications include: BSc Diploma in Engineering (Dipl.-Ing.) Doctorate in Technical Sciences (Dr.techn.) Löscher's research centers on numerical mathematics with deep specialization in finite element methodologies for distributed optimal control problems governed by partial differential equations. His work addresses elliptic, parabolic, and hyperbolic control systems through regularization techniques, adaptive mesh refinement, and robust solver development. Key innovations include variable energy regularization frameworks, mass-lumping discretizations, and complexity-optimal space-time finite element systems that significantly advance computational efficiency and error estimation in constrained control environments. Analysis of his 2024-2026 publications reveals a cohesive research trajectory focused on overcoming computational bottlenecks in optimal control. His work consistently bridges theoretical rigor with practical implementation, emphasizing discretization schemes that balance accuracy and computational cost while addressing state/control constraints across diverse PDE systems. No scientific awards or honors were documented in available sources. Löscher maintains active research collaborations with prominent figures including Ulrich Langer and Olaf Steinbach, evidenced by co-authored publications and peer-review activities for journals like Journal of Computational and Applied Mathematics. His external research engagement includes a July 2024 visit to Delft University of Technology, highlighting international academic partnerships.

Christian Bargetzمشاهده پروفایل

استاد

• Nonlinear Functional Analysis
• Banach Spaces
• Locally Convex Spaces
• +۴ مورد دیگر

Christian Bargetz is a Professor of Functional Analysis at the University of Innsbruck, Austria, affiliated with the Faculty of Mathematics, Computer Science, and Physics (MIP). His primary research focuses on nonlinear functional analysis, Banach space theory, and distribution theory. He teaches advanced courses such as Optimization, Distribution Theory, and Functional Analysis, demonstrating his expertise in both theoretical and applied aspects of his field. Education: Completed his PhD in 2012 at the University of Innsbruck under the supervision of Norbert Ortner. His diploma thesis (2008) explored differential behaviors with Ulrich Oberst. Bargetz has held continuous academic positions since 2008, including roles as a lecturer and researcher. Research Interests: Specializes in iterative projection methods, generic properties of nonexpansive mappings, Fréchet spaces, and vector-valued distributions. His work bridges functional analysis with geometric measure theory and optimization, with applications in metric geometry and topological tensor products. Publications: Over 30 peer-reviewed articles in prestigious journals such as Canadian Journal of Mathematics , Journal of Mathematical Analysis and Applications , and Proceedings of the American Mathematical Society . Recent work includes studies on extremal nonexpansive mappings and Lipschitz function spaces. Grants & Projects: Principal investigator in FWF-funded projects on nonexpansive mappings and Banach spaces. Collaborates internationally, including with institutions in Israel, Poland, and Serbia. Teaching: Leads advanced courses in functional analysis, optimization, and distribution theory. Supervises bachelor's theses and master's projects on topics like extension operators for Lipschitz functions. Affiliations: Active member of the Functional Analysis working group and regularly participates in international conferences such as the Banach Afternoon, Winter School in Abstract Analysis, and DMV-ÖMG Annual Conferences.

Xu Jinchaoمشاهده پروفایل

استاد

• Multigrid methods
• Machine learning
• Finite element methods
• +۵ مورد دیگر

Xu Jinchao is a Professor of Applied Mathematics and Computational Sciences at King Abdullah University of Science and Technology (KAUST) and the Verne M. Willaman Professor of Mathematics at Penn State University. He has held distinguished roles, including Director of the Center for Computational Mathematics and Applications at Penn State since 1997 and is an Affiliated Faculty member of the College of Information Sciences and Technology at Penn State. His research focuses on numerical partial differential equations (PDEs), multigrid methods, machine learning, finite element methods, and domain decomposition methods. He is renowned for pioneering contributions such as the Bramble-Pasciak-Xu (BPX) preconditioner, Hiptmair-Xu (HX) preconditioner, Xu-Zikatanov (XZ) identity, and Morley-Wang-Xu (MWX) element. His work bridges computational mathematics and machine learning, including the development of MgNet, which unifies multigrid methods with convolutional neural networks. Xu has been recognized with numerous awards, including Fellowships from SIAM, AMS, AAAS, and the European Academy of Sciences. Notable accolades include the 2008 DOE Top 10 Breakthroughs for his HX preconditioner and the 1995 Feng Kang Prize for Scientific Computing. He has organized over 100 conferences and serves on editorial boards of top journals such as Mathematics of Computations and Numerische Mathematik . His leadership includes directing research centers and advancing computational science through collaborative efforts.

• Numerical Analysis
• Partial Differential Equations
• Finite Element Method
• +۳ مورد دیگر

Dirk Praetorius is a Professor of Numerics of Partial Differential Equations (PDEs) at the Technische Universität Wien (TU Wien) , affiliated with the Institute for Analysis and Scientific Computing (ASC) within the Faculty of Mathematics and Geoinformation . He leads the research group on Numerics of PDEs and has held various leadership roles, including Institute Director (since 2020) and head of the Numerics research area. His work focuses on numerical methods for PDEs, including Finite Element Methods (FEM), Boundary Element Methods (BEM), adaptive algorithms, and computational micromagnetics. Education and Career: Praetorius earned his Diplom in Mathematics (2000) and PhD in Applied Mathematics (2003) from TU Wien, followed by a Habilitation in Numerical Analysis (2005). He has been a faculty member at TU Wien since 2005, progressing from Assistant Professor to full Professor in 2017. He has also held visiting positions at institutions such as the University of Jyväskylä and RICAM (Linz). Research Interests: His research spans numerical analysis, adaptive FEM/BEM, a-posteriori error estimation, matrix compression, and computational micromagnetics. He has contributed to modeling spin dynamics, magnetic skyrmions, and multiscale systems. His work emphasizes efficient algorithms for large-scale problems and optimal computational complexity. Awards and Editorial Roles: Praetorius received the TU Best Teacher Award (2021) and TU Best Lecture Award (2019). He serves as Senior Editor for Computational Methods in Applied Mathematics (CMAM) and on the editorial board of Applied Numerical Mathematics (APNUM) . He co-founded the outreach initiative TUForMath to promote mathematics education. Grants and Projects: He leads or co-leads several research projects funded by the Austrian Science Fund (FWF), including the collaborative SFB "Taming Complexity in Partial Differential Systems" (2017–2025) and international collaborations with Germany. His work addresses topics like functional error estimates, nonlinear PDEs, and computational design of magnetic devices. Labs and Teams: He contributes to the ASC Institute and coordinates interdisciplinary projects involving computational physics and engineering. His team develops software tools like MooAFEM and Commics for micromagnetic simulations.

Saharon Shelahمشاهده پروفایل

استاد

• Model theory
• Axiomatic set theory
• Applications of logic in algebra
• +۲ مورد دیگر

Saharon Shelah is an Israeli mathematician renowned for his groundbreaking work in mathematical logic and set theory. He currently holds the Robinson Chair for Mathematical Logic at Hebrew University, a position he has held since 1978, and has been a Professor at Hebrew University since 1974. Additionally, he serves as a Distinguished Visiting Professor at Rutgers University since 1986. His educational background includes: B.Sc. from Tel Aviv University (1964) M.Sc. from Tel Aviv University (1967) M.Sc. from Hebrew University (1968) Ph.D. from Hebrew University of Jerusalem (1969), summa cum laude, supervised by Michael Rabin Shelah's research primarily focuses on mathematical logic, particularly model theory and set theory. His work has revolutionized these fields through the development of stability theory, PCF theory, and proper forcing. He has made significant contributions to understanding the connections between logic and other mathematical disciplines including algebra, computer science, and combinatorics. His approach often involves creating general frameworks that solve multiple problems simultaneously, demonstrating extraordinary depth and breadth in mathematical thinking. An analysis of Shelah's extensive publication record reveals consistent innovation across mathematical logic. His work shows a progression from foundational model theory in the 1970s through the development of stability theory, to groundbreaking set theory results in the 1980s and 1990s, particularly in cardinal arithmetic and PCF theory. Recent publications continue to expand abstract model theory while finding new applications across mathematics. His research demonstrates remarkable consistency in quality and originality over five decades. Shelah's exceptional contributions have been recognized with numerous prestigious awards: 2013 AMS Steele Prize 2011 EMET Prize in Mathematics 2001 Wolf Prize in Mathematics 2000 Bolyai Prize 1998 Israel Prize for Mathematics 1992 SIAM George Pólya Prize 1991 Honorary Foreign Member of the American Academy of Arts and Sciences 1988 Member of the Israel Academy of Sciences and Humanities 1983 Karp Prize 1982 Rothshild Prize 1977 Erdös Prize Throughout his career, Shelah has mentored numerous students and collaborated with over 200 coauthors. His research has been supported by multiple grants from prestigious organizations including the Israel Science Foundation and the National Science Foundation. Shelah's work has fundamentally shaped modern mathematical logic, with his classification theory providing frameworks that continue to guide research directions. His ability to connect seemingly disparate areas of mathematics through logical structures has made his contributions exceptionally influential. Shelah maintains an active research group at Hebrew University, where he continues to supervise doctoral students and postdoctoral researchers. His research program, known for its depth and technical sophistication, continues to generate new directions in mathematical logic. The Shelah Archive (http://shelah.logic.at) serves as a comprehensive repository of his extensive publication record, which surpassed 1190 papers by the end of 2024.

• Optimization
• Convex Analysis
• Nonlinear Analysis
• +۳ مورد دیگر

Radu Ioan Bot is a Professor and Dean of the Faculty of Mathematics at the University of Vienna, where he also serves as Head of the Department of Mathematics. His primary affiliations include the Department of Mathematics (Oskar-Morgenstern-Platz 1, 1090 Wien) and the Research Network Data Science (Währinger Straße 29, 1090 Wien). Bot's research centers on optimization theory with emphasis on convex/nonconvex optimization, monotone operators, and dynamical systems. He develops fast algorithms for variational inequalities and monotone inclusions by bridging continuous-time dynamics with discrete optimization methods. His work frequently addresses bilevel optimization, Tikhonov regularization, and second-order dynamics, yielding accelerated convergence rates for complex problems. Analysis of his 15 most recent publications (2023-2025) reveals dominant trends in time-scaling techniques, vanishing damping dynamics, and structured splitting methods. Key contributions include unifying Nesterov acceleration with Heavy Ball dynamics, developing reflected forward-backward algorithms for constrained optimization, and establishing strong convergence guarantees for monotone operator flows. These advances demonstrate consistent innovation in accelerating optimization while maintaining theoretical rigor. No scientific awards were mentioned in the provided source material. Details regarding student advising and research grants were not specified in the available information, though his leadership roles as Dean and Department Head indicate significant administrative responsibilities alongside active research. Bot participates in the University of Vienna's Research Network Data Science, suggesting interdisciplinary engagement in data-driven methodologies with potential applications in machine learning and computational mathematics.

• Scientific Workflow Management
• Distributed Computing
• Numerical Linear Algebra
• +۳ مورد دیگر

Wilfried Gansterer is a Professor at the Faculty of Computer Science, University of Vienna, leading the Theory and Applications of Algorithms research group. His work focuses on numerical algorithms, distributed computing, and machine learning, with notable contributions to graph neural networks and fault-tolerant systems. Active in projects such as Algorithmic Data Science for Computational Drug Discovery (2020–2028) and REPEAL (Resilience vs. Performance in Numerical Linear Algebra, 2016–2020). Research Interests: Dr. Gansterer’s expertise spans graph neural networks, matrix compression, adversarial defense mechanisms, and high-performance computing. His work addresses challenges in efficient computation, resilience against node failures, and optimizing distributed systems. Projects : Algorithmic Data Science for Computational Drug Discovery (2020–2028) REPEAL: Resilience vs. Performance in Numerical Linear Algebra (2016–2020) Verteiltes Rechnen (Distributed Computing, 2007–2014) Awards : 2023 Best Paper Award for work on Crossfire: An Elastic Defense Framework for Graph Neural Networks. Labs/Teams : Directs the Theory and Applications of Algorithms group, focusing on algorithmic innovation in distributed and high-performance computing environments.

• hp-Finite Element Method (hp-FEM)
• Fractional Diffusion Equations
• Boundary Element Method (BEM)
• +۳ مورد دیگر

Prof. Jens Markus Melenk is a Professor at TU Wien's Faculty of Mathematics and Geoinformation, leading the Computational Mathematics Research Group (E101-02-1). His research focuses on advanced numerical methods for partial differential equations, with a strong emphasis on hp-FEM (hp-Finite Element Method), fractional diffusion equations, and wave propagation problems. He has contributed significantly to the development of robust and efficient algorithms for complex domains and heterogeneous media. Key research interests include the application of hp-FEM to fractional operators (e.g., integral fractional Laplacian), boundary element methods (BEM), and the analysis of wavenumber-explicit convergence for Maxwell's equations and elastic wave equations. His work bridges theoretical analysis and computational implementation, addressing challenges in multiscale problems and singular perturbations. Notable contributions: Exponential convergence of hp-FEM for fractional Laplacian problems, wavenumber-explicit error estimates for wave equations in heterogeneous media. Collaborations: Active involvement with researchers like Markus Faustmann, Christoph Schwab, and Dirk Praetorius. Supervised theses: Includes works on hp-FEM for fractional operators, RBF interpolation, and error estimators for elliptic PDEs. His research group develops and analyzes numerical schemes for challenging PDE scenarios, with applications in electromagnetism, acoustics, and computational mechanics.

• Plasma Physics
• Fusion Energy Research
• Computational Plasma Physics
• +۶ مورد دیگر

Univ.-Prof. Dr. Alexander Kendl is a Professor and Director of the Institute of Ion Physics and Applied Physics at the University of Innsbruck. His research focuses on computational plasma physics with critical applications to magnetic confinement fusion energy, particularly addressing challenges in plasma edge physics and turbulence modeling for next-generation fusion reactors. Dr. Kendl received his Doctorate from the Technical University of Munich in 2000, following his Abitur from Gymnasium Schrobenhausen in 1990. He was appointed Associate Professor at the University of Innsbruck in 2010 and promoted to University Professor in 2020. His academic journey reflects a deep commitment to advancing plasma physics through rigorous computational approaches. Dr. Kendl's research interests center on plasma turbulence in magnetically confined systems , with particular emphasis on gyrofluid modeling , edge-localized modes (ELMs) , zonal flow dynamics , and impurity transport in fusion plasmas. His work bridges fundamental plasma physics with practical applications for ITER and DEMO, addressing critical challenges in plasma confinement and stability. He has developed sophisticated computational tools including GREENY, GHW, and TIFF to simulate complex plasma phenomena with high fidelity. His recent publications reveal a strong focus on advancing full-f gyrofluid approaches that capture kinetic effects while maintaining computational efficiency. Dr. Kendl's research spans from fundamental investigations of plasma dynamics to direct applications for current fusion devices like ASDEX Upgrade, with particular attention to hysteresis phenomena, electron-positron plasmas as fundamental testbeds, and the development of novel numerical methods for plasma simulation. As Director of the Institute of Ion Physics and Applied Physics, Dr. Kendl leads a vibrant research group that contributes significantly to the international fusion community. His team develops cutting-edge simulation codes and collaborates extensively with major fusion facilities worldwide, advancing our understanding of plasma edge physics and turbulence.

• Formal Verification
• Probabilistic Systems
• Game Theory
• +۵ مورد دیگر

Krishnendu Chatterjee is a Professor at the Institute of Science and Technology Austria (IST Austria) , Department of Computer Science. His research spans formal verification, probabilistic systems, game theory, and evolutionary dynamics, with over 300 peer-reviewed publications in top venues such as DISC, AAAI, LICS, PNAS, Nature , and Journal of the ACM . His research focuses on developing theoretical foundations and practical algorithms for analyzing complex systems, including Markov decision processes, stochastic games, probabilistic programs, and evolutionary models. He has made significant contributions to topics such as reachability analysis, termination of probabilistic programs, synthesis of controllers, and evolutionary game dynamics. Chatterjee's work is highly interdisciplinary, bridging computer science, mathematics, and biology. He has collaborated extensively with leading researchers worldwide and has been involved in editorial roles and program committees for major conferences in formal methods and theoretical computer science.

Maximilian Brunnerمشاهده پروفایل

پژوهشگر

• Numerics of PDEs
• Finite Element Methods (FEM)
• Contractive Iterative Solvers
• +۳ مورد دیگر

Maximilian Brunner is a Postdoc at TU Wien's Institute of Analysis and Scientific Computing (E 101), specializing in Numerics of Partial Differential Equations (PDEs). He holds a Dipl.-Ing. (MSc) and Dr.techn. (PhD) from TU Wien, with his doctoral thesis on On optimal adaptivity for semilinear PDEs earning the 2025 Study Prize from the Austrian Mathematical Society (ÖMG). His research focuses on adaptive finite element methods (FEM), iterative solvers for nonlinear PDEs, and cost-optimality analysis. He collaborates with Prof. Dirk Praetorius and contributes to projects funded by the Austrian Science Fund (FWF). Previously, he was a PhD student (2020–2024) and taught courses in numerical mathematics and scientific programming. His work bridges theoretical advancements in adaptive algorithms with practical computational efficiency, addressing challenges in nonsymmetric and semilinear PDEs. Education: PhD in Technical Mathematics, TU Wien (2024), supervised by Dirk Praetorius MSc in Technical Mathematics, TU Wien (2020), supervised by Winfried Auzinger Research Interests: His interdisciplinary work centers on developing adaptive FEM strategies for cost-optimal solutions to PDEs. Key areas include: Contractive iterative solvers for nonlinear systems Goal-oriented error estimation for targeted accuracy Quasi-optimal computational cost in adaptive algorithms His methods prioritize minimizing computational resources while maintaining solution fidelity, with applications to elliptic and semilinear PDEs. Awards: 2025 Study Prize (ÖMG) for PhD thesis Grants & Funding: FWF Projects P33216 and P28367 Vienna School of Mathematics membership (2020–present) Labs/Teams: Core member of the Numerics of PDEs research group, part of TU Wien's ASC unit. Active in collaborative projects with international conferences and workshops.

• Convex Optimization
• Nonconvex Optimization
• Monotone Operators
• +۵ مورد دیگر

Radu Ioan Boț is a Professor at the Faculty of Mathematics at the University of Vienna . Since 2020, he has served as Dean of the Faculty of Mathematics and Head of the Institute of Mathematics . He is also the Speaker of the FWF DK Vienna Graduate School on Computational Optimization and a Founding Member of the Research Platform Data Science@Uni Vienna . Education Ph.D. in Mathematics (2003, Chemnitz University of Technology, Summa cum laude ) M.Sc. in Mathematics (1999, Babeş-Bolyai University, Grade: 10 ) Diploma in Mathematics (1998, Babeş-Bolyai University, Grade: 10 ) Research Interests focus on convex and nonconvex optimization , monotone operators , duality theory , and proximal algorithms . His work bridges continuous and discrete time models , emphasizing fast convergence rates and Tikhonov regularization for problems in image processing , machine learning , and inverse problems . Recent Publications highlight advancements in primal-dual splitting , inertial dynamics , and stochastic optimization , with applications to GANs , phase retrieval , and structured convex minimization . Scientific Awards include the 2003 University Prize of Chemnitz University of Technology , 1998 award for graduating with the best grade from Babeş-Bolyai University, and fellowships during his Ph.D. and pre-Ph.D. research stays. Editorial Roles include Editor-in-Chief of SIAM Journal on Optimization (2026–2029) and editorial board membership for journals like Mathematical Programming , Computational Optimization and Applications , and Journal of Optimization Theory and Applications .

• Numerics of Partial Differential Equations
• Computational Micromagnetism
• Optimal Mesh Refinement
• +۳ مورد دیگر

Michael Feischl is a Univ. Prof. at TU Wien's Institute for Analysis and Scientific Computing (E101). He specializes in numerical methods for partial differential equations, computational micromagnetism, and optimal adaptivity. Feischl leads the ERC Consolidator Project 'New Frontiers in Optimal Adaptivity' and has held academic positions at TU Wien, University of Bonn, and Karlsruhe Institute of Technology. His research interests span stochastic perturbations, finite element methods, and machine learning applications in computational mathematics. Feischl's work includes groundbreaking contributions to adaptive finite element methods, optimal mesh refinement strategies, and the numerical analysis of the Landau-Lifshitz-Gilbert equation in micromagnetics. His recent publications focus on advancing computational techniques for PDEs, neural operator networks, and stochastic collocation methods. He has developed algorithms with guaranteed convergence properties and optimal complexity, contributing to both theoretical and applied aspects of computational science. Education: Dipl.-Ing. Dr.techn. (PhD in Technical Mathematics) from TU Wien Awards: ERC Consolidator Grant 2022 Labs/Teams: Heads the 'Computational PDEs' research group at TU Wien's Institute for Analysis and Scientific Computing

• Plasma Physics
• Computational Physics
• Nuclear Fusion
• +۳ مورد دیگر

Patrick Lainer is a researcher at the Institute of Theoretical Physics at Technische Universität Graz (TU Graz), specializing in Computational Physics . His work focuses on plasma physics and nuclear fusion, particularly tokamak modeling, magnetic perturbations, and magnetohydrodynamics (MHD). Research Interests: Plasma Physics Computational Physics Nuclear Fusion Magnetohydrodynamics (MHD) Finite Element Method Applied Mathematics Lainer has contributed to studies on Reactive Magnetic Perturbations (RMP) , neoclassical toroidal viscosity , and impurity transport in tokamaks , with applications to projects like ITER and EU-DEMO . His research often involves hybrid kinetic-MHD models and iterative numerical approaches. Contact: Email: patrick.lainer@tugraz.at

• Numerics of Partial Differential Equations (PDEs)
• Finite Element Methods (FEM)
• Boundary Element Methods (BEM)
• +۳ مورد دیگر

Dirk Praetorius is a Professor of Numerics of Partial Differential Equations at the Vienna University of Technology (TU Wien), within the Institute for Analysis and Scientific Computing, part of the Faculty of Mathematics and Geoinformation. He leads research in numerical methods for PDEs, with a focus on Finite Element Methods (FEM), Boundary Element Methods (BEM), and computational micromagnetics. His work emphasizes adaptive algorithms, a-posteriori error estimation, and efficient numerical solvers. Praetorius has received awards such as the TU Best Teacher Award (2021) and TU Best Lecture Award (2019). He is also a Senior Editor of Computational Methods in Applied Mathematics and serves on the editorial board of Applied Numerical Mathematics . His research interests span numerical analysis, including adaptive methods for PDEs, matrix compression techniques (e.g., hierarchical matrices), and software development for computational science. Notable contributions include the development of open-source software like MooAFEM (adaptive FEM in MATLAB), Commics (micromagnetic simulations), and HILBERT (BEM library). These tools address challenges in adaptive mesh refinement, iterative solvers, and large-scale scientific computing. Praetorius has supervised numerous PhD students and postdocs, contributing to impactful publications in journals such as Numerische Mathematik , SIAM Journal on Numerical Analysis , and Computer Physics Communications . His work bridges theoretical analysis and practical applications, with a focus on achieving optimal computational efficiency and rigorous error control. In addition to research, Praetorius has led initiatives like TUForMath to foster academic engagement and has been a key contributor to collaborative projects in computational electromagnetics and materials science.