جست‌وجوی استاد و پژوهشگر

Richard Larssonمشاهده پروفایل

پژوهشگر

• Climate Science
• Atmospheric Radiation
• Remote Sensing Technology
• +۳ مورد دیگر

Dr. Richard Larsson is a Researcher at the University of Hamburg's Department of Earth System Sciences, affiliated with the Meteorological Institute. His work focuses on atmospheric radiation, climate science, and remote sensing technologies. He contributes to planetary atmosphere studies, particularly in Mars exploration and Earth's middle atmosphere dynamics. His research integrates advanced radiative transfer modeling, instrument development for space missions, and data assimilation techniques. Key research areas include: Zeeman effect analysis for magnetic field measurements Development of the Atmospheric Radiative Transfer Simulator (ARTS) Design of terahertz sensors for Martian atmospheric profiling Multi-source observation of electrojets and ionospheric phenomena His recent work emphasizes synergizing remote sensing data from satellites (e.g., EZIE mission) and ground-based systems. Projects like the Terahertz Radiometer for Mars and SMILES observation spectra demonstrate his expertise in instrument feasibility studies and atmospheric composition analysis. Research highlights include advancements in radiative transfer modeling for non-LTE conditions and halocarbon radiative forcing estimation. His contributions to climate science involve improving radiative calculation efficiency through sparse quadrature methods. Larsson collaborates on international missions like the Electojet Zeeman Imaging Explorer (EZIE) and contributes to Mars microsatellite sensor development. His work bridges computational modeling with practical instrumentation for planetary and terrestrial atmospheric studies.

Volker Mehrmannمشاهده پروفایل

استاد

• Applied and Numerical Linear Algebra
• Analysis and Numerics of Differential-Algebraic Equations
• Numerical Solution of Differential Equations
• +۳ مورد دیگر

Volker Mehrmann is a Professor of Mathematics (Numerical Mathematics) at Technische Universität Berlin since 2000. He holds a C4-Professorship and has held leadership roles in major research initiatives such as the DFG Research Center MATHEON and the Einstein Center ECMath. His research focuses on applied and numerical linear algebra, differential-algebraic equations, control theory, and industrial mathematics. Mehrmann earned his Ph.D. from Universität Bielefeld (1982) and habilitation (1988). He has authored influential textbooks, including Lineare Algebra , and serves as Editor-in-Chief of Linear Algebra and its Applications . He is a SIAM Fellow, ERC Advanced Grant recipient, and member of prestigious academies like acatech and Academia Europaea. Education: Diplom (1979), Ph.D. (1982), Habilitation (1988) from Universität Bielefeld. Key Roles: Speaker of DFG Research Center MATHEON (2008–2014) President of GAMM (2011–2016) and EMS (2017–2022) Research Highlights: Model reduction, port-Hamiltonian systems, and energy-based modeling Control of complex systems and industrial applications His recent work emphasizes interdisciplinary projects in energy systems (e.g., BMBF-EiFer, Hybrid Energy Networks) and numerical methods for large-scale systems. Mehrmann’s contributions span theoretical advancements and practical implementations, bridging fundamental mathematics with real-world challenges.

Georg Lohoمشاهده پروفایل

استاد مهمان

• Discrete Geometry
• Combinatorics
• Optimization
• +۳ مورد دیگر

Georg Loho is a Visiting Professor at Freie Universität Berlin (FU Berlin), currently acting as head of the Discrete Geometry and Topological Combinatorics group, substituting for Günter Ziegler. He is on leave from his position as an Assistant Professor at the University of Twente in the Discrete Mathematics & Mathematical Programming group. His academic journey includes postdoctoral roles at the London School of Economics (LSE) and EPFL, supported by an ERC Starting Grant, and a PhD from TU Berlin under Michael Joswig. Research Interests: Loho focuses on the interplay between discrete geometry, combinatorics, and optimization, with applications in machine learning, data science, and sustainability. His work bridges theoretical foundations and practical applications, including tropical geometry, algorithm design, and mathematical programming. Articles Trends: Recent publications explore stochastic games, tropical geometry in neural networks, oriented matroids, and optimization algorithms. His work often intersects algebraic geometry, combinatorial optimization, and computational complexity. Grants and Awards: He secured an ERC Starting Grant (2019–2020) and has been involved in projects like the HIM Trimester Program in Bonn. No explicit awards are listed, but his research contributions are widely recognized in discrete mathematics and optimization. Teaching: Loho has taught courses on discrete geometry, sustainability in mathematics, and AI applications. He emphasizes innovative pedagogy, including free open-source course materials and sustainability-focused curricula. Labs/Teams: He leads the Discrete Geometry and Topological Combinatorics group at FU Berlin and collaborates on projects like the MatchTheNet educational game, promoting interactive learning in geometry.

Guido Kanschatمشاهده پروفایل

استاد

• Numerics
• Scientific Computing
• Finite Element Methods
• +۷ مورد دیگر

Professor Dr. Guido Kanschat is affiliated with Ruprecht-Karls-Universität Heidelberg at the Interdisciplinary Center for Scientific Computing (IWR), where he holds the Chair for Mathematical Methods of Simulation. His research focuses on numerics, scientific computing, and finite element methods with applications in radiation transfer, coupled flows, and multigrid solvers. His recent publications emphasize hybrid discretizations, GPU-accelerated multigrid methods, and discontinuous Galerkin techniques for Stokes equations and biharmonic problems. He is a co-founder of the open-source deal.II library and received the Wilkinson Prize for Numerical Software in 2007. Scientific Awards: Wilkinson Prize for Numerical Software (2007)

Klaus Neymeyrمشاهده پروفایل

استاد

• Numerical Analysis
• Linear Algebra
• Chemometrics
• +۳ مورد دیگر

Prof. Dr. Klaus Neymeyr is a Professor of Numerical Mathematics at the University of Rostock, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics. Since 2023, he serves as Prodekan of the faculty. Previously, he was Dean (2014–2023) and an associated member at the Leibniz Institute for Catalysis (LIKAT). He obtained his habilitation in mathematics at the University of Tübingen (2001) and has held academic roles since 2003. His research spans numerical linear algebra, chemometrics, and catalysis, with a focus on eigenvalue problems, multivariate curve resolution (MCR), and spectral data analysis. Notable contributions include advancements in preconditioned eigensolvers and the development of FAC-PACK for feasible solution computation. Neymeyr’s work integrates computational methods with chemical applications, such as catalyst characterization via FTIR and NMR spectroscopy. He has been recognized with awards including the Kowalski Prize (2016) and a highly cited paper designation by ISI (2003). His collaborations extend to interdisciplinary projects in catalysis and biopharmaceutical data analysis. Publications emphasize algorithm development, convergence analysis, and data-driven methodologies. Scientific achievements include patents on energy-optimized pump regulation and contributions to the theoretical foundations of MCR and eigensolver convergence. His research group actively publishes in top journals like SIAM Journal on Matrix Analysis , Analytica Chimica Acta , and Journal of Chemometrics .

Robert Beinertمشاهده پروفایل

مدرس ارشد

• Applied Mathematics
• Inverse Problems
• Optimal Transport
• +۳ مورد دیگر

Robert Beinert is a Senior Lecturer (Privatdozent) in Applied Mathematics at the Technische Universität Berlin, affiliated with the Department of Applied Mathematics within the Faculty of Mathematics and Natural Sciences. He holds a Dr. rer. nat. (Ph.D.) from Georg-August-Universität Göttingen (2015) and completed his Habilitation at Technische Universität Berlin in 2025. His research focuses on inverse problems, optimal transport, phase retrieval, and mathematical imaging, with applications in signal processing and data analysis. He has held academic positions including Research Associate roles at TU Berlin (2020–2025), Karl-Franzens-Universität Graz (2016–2020), and Georg-August-Universität Göttingen (2016). His work bridges theoretical foundations and practical applications, with contributions to regularization techniques, optimal transport theory, and algorithmic development. Key research interests include phase retrieval uniqueness analysis, Gromov-Wasserstein transport, and denoising methodologies for manifold-valued data. His recent publications emphasize advancements in optimal transport frameworks, regularization strategies, and applications in image processing and machine learning.

Florian Beierمشاهده پروفایل

پژوهشگر

• Optimal Transport
• Multi-Marginal Optimal Transport
• Gromov-Wasserstein Distance
• +۳ مورد دیگر

Florian Beier is a Researcher at the Institute of Mathematics, Technical University of Berlin, specializing in Applied Mathematics. Since October 2021, he has served as a research assistant pursuing his Dr. rer. nat. His academic journey includes a Master of Science (2021) and Bachelor of Science (2018), both in Mathematics from TU Berlin, with thesis topics focusing on Optimal Transport and Percolation Theory. Research interests revolve around Optimal Transport theory, particularly multi-marginal and Gromov-Wasserstein formulations, with applications to mathematical imaging and computational geometry. His work bridges theoretical advancements and practical implementations, addressing challenges in transportation barycenters, entropic regularization, and algorithmic design for complex data analysis. Notable achievements include the Best Poster Award at the 2022 International Conference on Curves and Surfaces and recognition for academic excellence at TU Berlin's Dies Mathematicus. He actively contributes to conferences and workshops, presenting groundbreaking research on transport operators and barycenter computations. Current projects involve developing fixpoint iteration methods for Gromov-Wasserstein barycenters and exploring transport kernels for unpaired data analysis. His collaborations span institutions like the University of Tübingen and KTH Royal Institute of Technology, emphasizing interdisciplinary approaches to transport problems.

Denis Belomestnyمشاهده پروفایل

استاد

• Statistics of Stochastic Processes
• Financial Mathematics
• Econometrics
• +۵ مورد دیگر

Denis Belomestny is a Professor of Applied Stochastics at the Department of Mathematics, University of Duisburg-Essen. His academic journey includes a PhD from Lomonosov Moscow State University (2002), postdoctoral work at the University of Bonn, and research positions at WIAS Berlin and Humboldt University Berlin. He currently leads research at the intersection of stochastic processes and financial mathematics. PhD in Mathematics, Lomonosov Moscow State University (2002) W3 Professorship in Applied Stochastics, University of Duisburg-Essen (2011–present) His research focuses on statistics of stochastic processes , optimal stopping/control , and Monte Carlo methods , with applications in financial mathematics and machine learning. Key collaborations include work with John Schoenmakers on multilevel approximation algorithms and Alexey Naumov on variance reduction techniques. Recent publications (2025–2023) explore deep neural networks for SDEs , generative adversarial networks , and nonparametric estimation in complex stochastic models. His work spans stochastic differential equations , financial derivatives pricing , and machine learning-driven statistical inference . He supervises doctoral students, including Sascha Nolte (research: robust optimal stopping without reference models). Current projects involve McKean-Vlasov SDEs , gamma-driven processes , and reinforced optimal control .

• Applied Mathematics
• Optimization
• Computational Mathematics
• +۳ مورد دیگر

Yair Censor is a distinguished Professor in the Department of Mathematics at the University of Haifa. His work focuses on applied mathematics, particularly in optimization, projection methods, and computational algorithms with applications in medical physics and engineering. He has developed foundational techniques like the superiorization method, which balances feasibility-seeking and objective function reduction in iterative algorithms. Research Interests: Optimization theory, convex feasibility problems, superiorization, medical imaging algorithms, and parallel computing. Collaborations: Extensive work with Gabor T. Herman, Aviv Gibali, Reinhard Schulte, and others in computational methods for radiation therapy and tomography. His recent publications (2023–2025) emphasize feasibility-seeking algorithms, superiorization in floorplanning, and perturbation resilience. While no explicit awards or students are listed in the provided text, his contributions to iterative methods and medical physics remain influential.

• Numerical Methods for Non-Local Operators
• Multigrid Methods
• Iterative Solvers
• +۴ مورد دیگر

Steffen Börm is a Professor and Chair of Scientific Computing at the Institute of Computer Science, Christian-Albrechts-University, Kiel. His research focuses on developing efficient algorithms for non-local operators, multigrid methods, and numerical analysis of eigenvalue problems, which are critical for simulating complex scientific phenomena. Research Areas Numerical Methods for Non-Local Operators Multigrid Methods and Iterative Solvers Numerical Analysis of Eigenvalue Problems Scientific Computing Applied Mathematics In teaching, he offers courses on Algorithms and Data Structures , Numerical Mathematics for Engineers , and Numerical Analysis of Eigenvalue Problems , emphasizing computational efficiency and mathematical foundations. His work bridges theoretical algorithm design with practical applications in simulations across physics, engineering, and computer science.

Olga Mulaمشاهده پروفایل

پژوهشگر

• Optimal Transport
• Wasserstein Spaces
• Model Reduction
• +۴ مورد دیگر

Olga Mula is a researcher at Eindhoven University of Technology (TU Eindhoven) in the Netherlands specializing in optimal transport theory, Wasserstein spaces, and model reduction techniques for partial differential equations. Her work bridges theoretical mathematics with practical applications in state estimation and inverse problems. Her research interests focus on Optimal Transport , Wasserstein Spaces , Model Reduction , and Numerical Analysis of PDEs . She develops algorithms for state estimation in metric spaces, particularly focusing on the Wasserstein space of probability measures. Her work includes developing reduced models using barycentric approximation, analyzing convergence properties, and addressing challenges in sensor placement for optimal data acquisition. Her recent publications demonstrate significant contributions to understanding how to build efficient reduced models in Wasserstein spaces for both forward and inverse problems. She has developed theoretical frameworks for state estimation algorithms, analyzed their performance in terms of Kolmogorov widths, and created practical implementations using sparse Wasserstein barycenters. Her work spans pure mathematical theory to applications in image processing and PDE-constrained optimization. Her scientific contributions include: Development of piecewise-affine algorithms for state estimation Nonlinear model reduction on metric spaces for conservative PDEs Sparse approximation using Wasserstein barycenters Applications to shape reconstruction and line completion Theoretical analysis of approximation rates in Wasserstein spaces She collaborates extensively with leading researchers in the field including Cohen, Dahmen, Feydy, and Rai. Her work demonstrates both theoretical depth and practical relevance, connecting abstract mathematical concepts to real-world problems in data assimilation and inverse modeling.

Christiane Tammerمشاهده پروفایل

استاد

• Vector Optimization
• Variational Methods
• Nonlinear Functional Analysis
• +۷ مورد دیگر

Christiane Tammer is a full Professor at the Institute of Mathematics , Faculty of Natural Sciences II, Martin Luther University Halle-Wittenberg. Her research spans variational methods, optimization, nonlinear functional analysis, approximation theory, duality principles, location theory, and inverse problems. She is actively involved in editorial roles as Editor-in-Chief of Optimization and serves on multiple journal editorial boards. Current affiliation: Theodor-Lieser-Str. 5, Halle (Saale), Germany Email: christiane.tammer@mathematik.uni-halle.de Her funded research includes projects on novel algorithms for combined tour and location optimization and multicriteria stochastic optimization and stochastic control theory . Recent publications focus on vector optimization under uncertainty, nonconvex separation techniques, and proximal gradient methods for multiobjective problems.

Malte Prießمشاهده پروفایل

استاد

• Cloud Computing
• Artificial Intelligence
• Software Engineering
• +۵ مورد دیگر

Prof. Dr. rer. nat. Malte Prieß is a Professor for Cloud Technologies at Kiel University of Applied Sciences since October 20224, following eight years as Dean and Professor of Applied Computer Science at Schleswig-Holstein Cooperative State University (DHSH) . He teaches modules including Cloud Computing , Web Applications , and Advanced Cloud Computing , with additional involvement in Agile Development Methods and Software Engineering . Academic Background: Diploma in Physics (with distinction) from Leibniz University Hanover and Max Planck Institute for Gravitational Physics (2002-2007) Dr. rer. nat. (magna cum laude) from Kiel University in Algorithmic Optimal Control (2008-2012) Research Interests focus on the intersection of cloud computing, artificial intelligence, and modern software engineering . His work includes surrogate-based optimization for climate models, AI-driven document capture systems , and ethical considerations in AI deployment within project work. Recent Publications demonstrate expertise in AI vulnerability assessment , deep learning training optimization , and document search algorithms for governmental agencies. Scientific Recognition: Best Paper Award at CLOUD COMPUTING 2025 for "Graph of Effort" vulnerability assessment Accepted fellowship at AI Campus (Stifterverband) for "Teaching AI, learning AI at DHSH" (2022) Research Projects: Central Innovation Programme for SMEs (ZIM): "AI MODULES for the skilled trades" (2024/25) HR dashboard for DRK Schwesternschaft, Lübeck Scalable Data Analytics project under BMBF FHprofUnt program (2018)

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• Computational Structural Dynamics
• +۱۲ مورد دیگر

Dr.-Ing. Norbert Hosters is a Research Associate and Chief Engineer at the Chair for Computational Analysis of Technical Systems (CATS), Faculty of Mechanical Engineering, RWTH Aachen University. He has been active since 2020 and serves as General Secretary of the German Association for Computational Mechanics (GACM). His work bridges advanced computational methods with engineering applications. His research focuses on numerical methods for fluid-structure interaction , computational fluid and structural dynamics , isogeometric analysis , and aerothermoelasticity . He also explores applied quantum methods and physics-informed neural networks for solving complex PDEs and optimizing industrial processes. His interdisciplinary work spans mechanical, biomedical, and computational engineering. The recent publications (2023–2025) demonstrate a strong trend toward integrating machine learning with traditional simulation techniques, particularly in partitioned FSI , multiphase flow , shape optimization , and biomedical simulations such as LVAD modeling. His work appears in high-impact journals like Scientific Reports , Computers & Fluids , and International Journal for Numerical Methods in Engineering , as well as major conferences including GACM and CMBE. He is actively involved in teaching courses on Numerical Methods for Fluid-Structure Interaction , Isogeometric Analysis , and Simulation Methods in Mechanical Engineering . He offers student projects and supervises research, though no named advisees are listed. He has no listed scientific awards or fellowships. His research is conducted within the CATS chair, a leading group in computational mechanics, contributing to both fundamental methods and industrial applications. He plays a key role in academic service through GACM leadership.

Moritz Diehlمشاهده پروفایل

استاد

• Optimization
• Control Systems
• Embedded Systems
• +۳ مورد دیگر

Prof. Moritz Diehl is a Professor at the University of Freiburg, leading the Systems Control and Optimization Laboratory within the Department of Microsystems Engineering (IMTEK) and affiliated with the Department of Mathematics. Born in Hamburg, Germany, he holds a Ph.D. from Heidelberg University (2001) and previously served as a professor at KU Leuven (2006–2013), where he directed the Optimization in Engineering Center (OPTEC). His research focuses on optimization and control, emphasizing numerical methods for engineering applications, particularly embedded systems and renewable energy. Key areas include model predictive control (MPC), nonlinear optimization, and real-time control systems. Education: He studied physics and mathematics at Heidelberg University and the University of Cambridge (1993–1999), culminating in a Ph.D. in Scientific Computing. His academic journey includes roles at KU Leuven and Freiburg, where he has developed influential tools like the AWEbox framework for airborne wind energy systems and the acados optimization library. Research Interests: His work spans numerical optimal control, MPC algorithms, and their applications in robotics, energy systems, and automotive engineering. Recent advancements include collision-free motion planning, real-time NMPC with convex-concave constraints, and stochastic control methods for mobile robots. He also explores optimization for hybrid systems, leveraging finite elements and switch detection for nonsmooth dynamics. Publications: His 2023–2025 work highlights contributions to MPC stability, energy-efficient control systems, and software tools like LCQPow for quadratic programming. His research bridges theory and practice, addressing challenges in industrial processes, renewable energy integration, and autonomous systems. Labs & Teams: He leads the Systems Control and Optimization Lab, fostering interdisciplinary projects in optimal control, robotics, and sustainable energy. His group collaborates on tools like acados, emphasizing real-time feasibility and scalability for complex systems.