جست‌وجوی استاد و پژوهشگر

YingXing Chengمشاهده پروفایل

پژوهشگر

• Computational Material Science
• Frequency-Dependent Polarizable Force Fields
• Time-Dependent Density Functional Theory
• +۳ مورد دیگر

YingXing Cheng is a Researcher and Postdoc at the University of Stuttgart, affiliated with the Institute of Applied Analysis and Numerical Simulation and the department of Numerical Mathematics for High Performance Computing. Their work focuses on advancing computational methods in material science and quantum chemistry, including frequency-dependent polarizable force fields, Time-Dependent Density Functional Theory (TDDFT), and machine learning applications. Research interests include molecular density decomposition, relativistic quantum effects, and high-throughput computational studies. They contribute to software engineering for scientific computing, particularly in developing frameworks like ACKS2ω and integrating machine learning for material property prediction. Publications highlight advancements in polarizable force fields, vacancy formation energy analysis, and algorithmic approaches for defect prediction. Their work bridges theoretical chemistry, numerical analysis, and high-performance computing to address challenges in material design and characterization. No scientific awards are listed, though their contributions to computational methodologies are notable. Cheng collaborates on projects involving molecular dynamics simulations and electronic structure analysis, contributing to both academic and applied research in computational materials science.

Tobias Ehringمشاهده پروفایل

پژوهشگر

• Optimal Control
• Numerical Mathematics
• Kernel Methods
• +۳ مورد دیگر

Tobias Ehring is a Researcher at the University of Stuttgart, affiliated with Faculty 08 and the Institute of Applied Analysis and Numerical Simulation. His work focuses on surrogate modeling for optimal control problems using kernel approximation techniques. He holds an M.Sc. degree and is part of the Numerical Mathematics working group. Contact details include office room 7.164 at Pfaffenwaldring 57, Stuttgart.

Thorsten Hohageمشاهده پروفایل

استاد

• Inverse Problems
• Partial Differential Equations
• Regularization Theory
• +۴ مورد دیگر

Prof. Thorsten Hohage is a Full Professor at the Institute for Numerical and Applied Mathematics, Georg-August University of Göttingen since 2009. He leads the Inverse Problems Group, focusing on computational and theoretical aspects of inverse problems in partial differential equations, regularization theory, and numerical algorithms. His research spans applications in optics, medical imaging (MRI), helioseismology, and X-ray phase contrast imaging. **Career**: Full Professor (W3) since 2009 Associate Professor (W2) 2007–2009 Junior Professor 2002–2007 Postdoc at Zuse Institute Berlin (2000–2002) Research Assistant at Johannes-Kepler University Linz (1996–2000) **Research Interests**: Inverse scattering and PDE-based inverse problems Regularization theory for statistical and deterministic inverse problems Efficient numerical methods (e.g., learned infinite elements) Applications in helioseismology, phase retrieval, and medical imaging **Notable Contributions**: Developed methodologies for transparent boundary conditions, variational source conditions, and regularization techniques in Besov spaces. His work bridges theory and practice in computational inverse problems. **Affiliations**: Member of the Max Planck Institute for Solar System Research (since 2017) and part of the GGNB Physics of Biological and Complex Systems program.

Markus Weimarمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• Approximation Theory
• +۴ مورد دیگر

Jun.-Prof. Dr. Markus Weimar is a professor of Numerical Analysis at the Julius-Maximilians-Universität Würzburg. Previously, he held positions including Interim Professor (W2) and Assistant Professor (Juniorprofessor) at Ruhr University Bochum (RUB). His research focuses on numerical methods for partial differential equations, approximation theory, and high-dimensional problems. He has expertise in adaptive wavelet methods, regularity theory, and discrepancy theory. Education: Ph.D. (summa cum laude) in Mathematics from Friedrich-Schiller-University Jena (2013), Diplom (M.Sc.) in Mathematics with focus on Analysis and Applied Computer Science (2009). He has held postdoctoral and visiting positions at institutions like KU Leuven, University of Sydney, and University of Basel. Research interests include regularity of solutions to PDEs in Besov/Triebel-Lizorkin spaces, adaptive finite element/wavelet methods, and multivariate integration. He has organized workshops such as the European Numerical Mathematics and Advanced Applications Conference and contributed to conferences like ICIAM and ENUMATH. Scientific awards include a Global Young Faculty membership (Mercator Foundation, 2017-2019) and travel grants from the Isaac Newton Institute and Banff International Research Station. He actively supervises Ph.D. students like Janina Hübner and has co-authored over 20 peer-reviewed articles. His work bridges theoretical foundations (e.g., function space regularity) with computational methods (e.g., adaptive algorithms).

Raphael Kruseمشاهده پروفایل

استاد

• Stochastic Differential Equations
• Numerical Analysis
• Monte Carlo Methods
• +۱ مورد دیگر

Raphael Kruse is Professor for Numerics of Stochastic Differential Equations at Martin Luther University Halle-Wittenberg. His research develops numerical methods for stochastic evolution equations and Monte Carlo techniques, with applications to PDEs and optimal control. He currently serves as Vice-Dean for Education in the Department of Natural Sciences II. Professor Kruse teaches undergraduate courses in numerical mathematics, probability theory, and differential equations, plus graduate seminars on stochastic numerics. He has supervised 6+ master's theses and 4 bachelor's theses on computational mathematics topics. Awards include the Dr. Klaus Körper Prize (2013) and WLUG PhD award (2013) Leads projects on numerical methods for SDEs with DAAD and DFG funding

Verena Wolfمشاهده پروفایل

استاد

• Stochastic Modeling
• Systems Biology
• Computational Biology
• +۵ مورد دیگر

Verena Wolf is a full professor in the Modeling and Simulation group at Saarland University since April 2012. She holds a PhD in Computer Science from the University of Mannheim (2008) and previously served as a junior research group leader at the Excellence Cluster Multimodal Computing and Interaction. Her research focuses on stochastic modeling, biochemical reaction networks, and systems biology. She is an Associate Editor of the ACM Transactions on Modeling and Computer Simulation and leads the students’ lab InfoLab Saar . Education: PhD in Computer Science, University of Mannheim, 2008 Postdoc, École Polytechnique Fédérale de Lausanne (EPFL), 2008–2009 Research Interests: Develops methods for analyzing stochastic systems, with applications to biochemical processes and complex networks. Specializes in hybrid models, Markov chain analysis, and scalable simulation techniques. Recent work includes studying epidemic dynamics, DNA methylation mechanisms, and algorithmic approaches for variance reduction. Publications: Over 100 peer-reviewed articles across venues like ACM TOMACS, PLoS Computational Biology , and IEEE Transactions . Themes include computational methods for stochastic systems, machine learning applications in bioinformatics, and formal verification of AI agents. Labs & Roles: Heads the InfoLab Saar, contributes to the Saarland Center for Bioinformatics , and chairs conferences like QEST’19 and CMSB’20 .

Clemens Thielenمشاهده پروفایل

استاد

• Discrete Mathematical Optimization
• Network Optimization
• Approximation Algorithms
• +۳ مورد دیگر

Prof. Clemens Thielen holds the Professorship for Optimization and Sustainable Decision Making at TUM Campus Straubing, Technical University of Munich. He previously served as Junior Professor at TU Kaiserslautern (2013–2019) and was appointed to the Professorship for Complex Networks at TUM Campus Straubing in 2019. His research focuses on discrete mathematical optimization, including network optimization, approximation algorithms for multiobjective problems, and practical applications like healthcare scheduling and infrastructure planning. He earned his PhD in Mathematical Optimization from TU Kaiserslautern in 2010, with studies at the University of Cambridge. Notable awards include the 2024 EURO Prize for OR for the Common Good and a 2018 teaching nomination. His work bridges theoretical advancements and real-world applications such as flood mitigation, traffic emission reduction, and crane logistics optimization. Education: PhD in Mathematical Optimization, Technical University of Kaiserslautern (2010) Studies in Mathematics at Technical University of Kaiserslautern and University of Cambridge Research Interests: Network optimization and approximation algorithms Multiobjective decision-making and sustainable resource allocation Applications in healthcare, transportation, and infrastructure Awards: EURO Prize for OR for the Common Good (2024) Nomination for Teaching Award of Rhineland-Palatinate (2018) Labs/Teams: Active in the Optimization and Sustainable Decision Making research group at TUM Campus Straubing, collaborating on projects such as municipal flood mitigation and healthcare scheduling.

• Neural Networks
• Hardware-Accelerated Computing
• FPGA Systems
• +۴ مورد دیگر

Walter Stechele is a Professor at the Technical University of Munich (TUM), holding a position in the Department of Integrated Systems within the TUM School of Computation, Information and Technology. His research focuses on hardware-software co-design for neural networks, embedded systems optimization, and edge computing applications. Key areas include FPGA acceleration of convolutional neural networks (CNNs), quantization-aware training, and adversarial robustness in multi-bit networks. He also investigates sensor fusion, automotive imaging systems, and medical image registration techniques. His work bridges theoretical advancements in machine learning with practical deployment challenges on resource-constrained hardware. Notable contributions include methodologies like MATAR (multi-quantization-aware training) and HW-flow-fusion (inter-layer scheduling for CNN accelerators). His research addresses critical issues such as numerical stability in 8-bit Winograd convolutions and optimizing imaging through automotive windshields. Leveraging FPGA platforms, he explores energy-efficient implementations of binarized neural networks (BNNs) for tasks like driveable area detection and gesture recognition on edge devices. His publications frequently emphasize real-world applications in autonomous driving, robotics, and medical imaging, demonstrating a commitment to translating algorithmic innovations into deployable systems.

Kai Hebelerمشاهده پروفایل

مدرس

• Many-body theory
• Nuclear astrophysics
• Density functional theory
• +۱ مورد دیگر

Dr. Kai Hebeler is a Lecturer in the Theory Center of the Institute of Nuclear Physics at the Technical University of Darmstadt, Germany. His research focuses on many-body theory, nuclear astrophysics, and density functional theory. He holds a doctoral degree from TU Darmstadt and has conducted postdoctoral and research roles at institutions including ECT* (Trento), CEA (Saclay), and TRIUMF (Vancouver). His work emphasizes nuclear interactions and their applications to extreme astrophysical environments like neutron stars. Research interests include advanced many-body methods, nuclear structure calculations, and the interplay between nuclear theory and astrophysical phenomena. Hebeler’s contributions have advanced the understanding of dense matter properties and neutron star equations of state.

• Numerical Analysis
• Geometric Integration
• Quantum Dynamics
• +۵ مورد دیگر

Prof. Christian Lubich is a Professor in the Mathematical Institute at the University of Tübingen, Germany. His research focuses on numerical analysis, geometric integration, quantum dynamics, and partial differential equations. He has authored influential books such as *Geometric Numerical Integration* (with E. Hairer and G. Wanner) and *From Quantum to Classical Molecular Dynamics*. His work emphasizes stability, accuracy, and computational efficiency in numerical methods for high-dimensional and oscillatory systems. Lubich's contributions span dynamical low-rank approximation, oscillatory Hamiltonian systems, and wave propagation in dispersive media. He has developed novel algorithms for tensor networks and matrix differential equations, addressing challenges in quantum many-body dynamics and stiff evolution problems. His research bridges numerical analysis with applications in physics, chemistry, and engineering. Key areas of exploration include: (1) structure-preserving integrators for long-time simulations, (2) error analysis of finite difference methods in semi-classical regimes, and (3) parallel algorithms for tensor-based computations. His work on pseudospectra and eigenvalue optimization has advanced stability analysis in linear dynamical systems. Lubich collaborates extensively on boundary element methods, finite element algorithms for evolving surfaces, and variational discretization techniques. His publications reflect a commitment to both theoretical rigor and practical computational solutions for complex physical systems.

Tabea Tscherpelمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations (PDEs)
• +۳ مورد دیگر

Tabea Tscherpel is a tenure-track professor at the Department of Mathematics of TU Darmstadt, specializing in Numerical Analysis and Scientific Computing. Previously, she held postdoctoral positions at Universität Bielefeld (under Lars Diening) and RWTH Aachen (under Sebastian Noelle), following her DPhil from the University of Oxford under Endre Süli’s supervision. Her research focuses on numerical methods for PDEs, finite element methods, and computational fluid dynamics. Key research interests include exact integration for finite elements, divergence-free spaces, and energy-consistent discretizations. Her work bridges theoretical analysis with practical implementation, addressing challenges in computational geometry and adaptive methods. She actively contributes to conferences such as the Biennial Numerical Analysis Conference (2024) and plans to lead workshops on adaptive finite element methods. Contact: Office S4|10 101a, TU Darmstadt | tscherpel@mathematik.tu-darmstadt.de

Kersten Schmidtمشاهده پروفایل

پژوهشگر

• Computational Mathematics
• Partial Differential Equations
• Numerical Analysis
• +۵ مورد دیگر

Kersten Schmidt is a Researcher at the Department of Mathematics, Technische Universität Darmstadt, specializing in Numerical Analysis and Scientific Computing. His work focuses on mathematical modeling of wave phenomena, including electromagnetism, acoustics, and coupled systems, with applications in photonics and engineering. He holds a PD (postdoctoral qualification) and has contributed to advancements in finite element methods, homogenization techniques, and boundary layer analysis. Research Interests Partial differential equations modeling wave propagation Numerical methods: Finite Element Methods (FEM), boundary element methods, high-order discretizations Asymptotic analysis and homogenization for multiscale problems Applications in acoustics, electromagnetism, and plasmonics Modelling of thin structures and interface conditions Publications Recent work includes studies on: Acoustic-structure interaction optimization Homogenization techniques for periodic structures Impedance boundary conditions for viscous acoustics Nonlinear eigenvalue problems in waveguide systems Efficient numerical methods for photonic crystals and plasmonic particles Affiliations Member of the Numerical Analysis and Scientific Computing group at TU Darmstadt Collaborations with institutions including École Polytechnique (France), University of Zagreb (Croatia), and TU Wien (Austria)

Alexander Dietzمشاهده پروفایل

مدرس

• Geometry and Approximation
• Volumetric Subdivision
• Subdivision Surfaces
• +۴ مورد دیگر

Alexander Dietz is a Lecturer in the Department of Mathematics at Technische Universität Darmstadt. His research focuses on geometry and approximation, with specialized interests in volumetric subdivision, subdivision surfaces, and elliptic PDEs on subdivision surfaces and volumes. He has contributed to isogeometric analysis and geoscience software tools such as standardized Schoeller diagrams. He has presented at conferences including the Rhein-Ruhr-Workshop (2020–2022), the International Conference on Approximation Theory (2023), and the International Geometry Summit (2023). His teaching includes pre-course mathematics for computer scientists and courses on constructive geometry and mathematics for engineering disciplines. Notable achievements include the Higher Education Teaching Certificate (2018–2020), a research stay at USI Lugano (2023), and the 2023 Athene Award for Good Teaching in Mathematics at TU Darmstadt.

• Mathematical Logic
• Algebra
• Lattice Theory
• +۴ مورد دیگر

Christian Herrmann is a Professor at the Department of Mathematics, Technische Universitaet Darmstadt. His research focuses on modular lattices , Arguesian lattices , *-regular rings , and quantum logic . He has collaborated with researchers like Doug Pickering, Michael Roddy, Marina Semenova, and Martin Ziegler. Research Interests: Algebraic structures, lattice theory, orthogonal geometry, computational complexity of quantum logic, and representation theory of regular rings. Educational Contributions: Authored classroom notes on topics including linear algebra, algebraic structures, and geometric representations. Publications: Specializes in equational theories, geometric representations, and complexity problems related to modular and ortholattices. Collaborations: Worked extensively with Doug Pickering, Michael Roddy, Marina Semenova, Martin Ziegler, and others. His research spans theoretical mathematics and its intersections with computer science.

Michael Kohlerمشاهده پروفایل

استاد

• Nonparametric Statistics
• Uncertainty Quantification
• Machine Learning
• +۳ مورد دیگر

Prof. Michael Kohler is a W3 Professor for Mathematical Statistics at the Technical University of Darmstadt since 2007, within the Department of Mathematics. His research focuses on nonparametric statistics, uncertainty quantification, machine learning, and applications in engineering and finance. He holds a PhD from the University of Stuttgart (1997) and a Habilitation from the same university (2000). His work includes contributions to regression estimation, Monte Carlo methods for pricing options, and statistical methods in computer linguistics. Education: PhD in Mathematics (1997), Habilitation in Mathematics (2000), both from the University of Stuttgart. Earlier studies include a Diploma in Mathematics and Computer Science (1995) and scholarships from the German Academic Scholarship Foundation (1993-1995). Research Interests: Nonparametric regression, uncertainty quantification using surrogate models, statistical machine learning, deep neural networks, and applications in finance and engineering. He has developed methods for quantifying uncertainty in technical systems and improving regression estimates under minimal assumptions. Key Achievements: Awarded the Research Prize by the German Mathematical Society (1998) and the Best Teaching Prize from TU Darmstadt's Mathematics Department (2006). His work on deep learning and nonparametric regression has been published in top journals like IEEE Transactions on Information Theory and Annals of Statistics. Awards: Research Prize (1998), Best Teaching (2006) Grants: Not explicitly mentioned, but extensive research output suggests active grant involvement Labs/Teams: Leading research on uncertainty quantification, deep learning, and statistical methods at TU Darmstadt