جست‌وجوی استاد و پژوهشگر

Michael Ulbrichمشاهده پروفایل

استاد

• Nonlinear Optimization
• Optimal Control
• Numerical Analysis
• +۳ مورد دیگر

Prof. Dr. Michael Ulbrich is a full professor and Chair of Mathematical Optimization at the Technical University of Munich (TUM), within the School of Computation, Information and Technology. He has held this position since 2006 and previously served as Dean of Studies (2007–2010) and Vice Dean of the Faculty of Mathematics (2012–2015). His research focuses on nonlinear optimization, optimal control, and numerical analysis, with applications in fluid dynamics, shape optimization, and PDE-constrained systems. He leads projects in the DFG SPP 1962 and IGDK 1754, and has received prestigious awards including the Howard Rosenbrock Prize (2015) and the Doctoral Award from the TUM Association of Friends (1996). Ulbrich is Editor-in-Chief of Optimization and Engineering and contributes to multiple journals. His work bridges theoretical foundations and practical applications, including CO2 sequestration, fluid-structure interaction, and distributed optimization algorithms. Education: PhD (1996), Habilitation (2002) in Mathematics at TUM. Research stays at Rice University (USA) under DFG funding. Research Areas: Semismooth Newton methods, PDE-constrained optimization, optimal control of Navier-Stokes equations, and distributed parameter systems. Awards: Rosenbrock Prize, Teaching Excellence Awards, and recognition for doctoral work. Leadership Roles: Department Head of Mathematics (2022–), Member of TUM Senate (2019–2022), and Co-Chair of GAMM 2018. Ulbrich has authored influential textbooks like Semismooth Newton Methods for Variational Inequalities and Nichtlineare Optimierung . His recent projects include OptiGeoS (2024–2026) and collaborations on nonsmooth optimization and stochastic algorithms. His academic contributions span over 100 publications, emphasizing both algorithmic innovation and rigorous mathematical analysis.

Barbara Drosselمشاهده پروفایل

استاد

• Complex Systems Theory
• Theoretical Ecology
• Random Boolean Networks
• +۷ مورد دیگر

Barbara Drossel is a Full Professor at the Institute of Solid State Physics within the Faculty of Physics at the Technical University of Darmstadt, where she has been conducting research since February 2002. Her work bridges theoretical physics, complex systems theory, and theoretical ecology, focusing on interdisciplinary approaches to understanding emergent phenomena in natural systems. She leads the AG Drossel research group that investigates the theoretical foundations of complex networks, ecological communities, and quantum systems. Professor Drossel's research spans multiple domains with emphasis on complex systems theory, where she has made significant contributions to understanding random Boolean networks, food web modeling, and the physics of ecological communities. Her work demonstrates how simple rules can lead to complex emergent behavior across different scales, from quantum systems to ecological networks. She investigates how top-down causation operates in complex systems and explores the relationship between microscopic dynamics and macroscopic patterns in diverse contexts. Analysis of her recent publications reveals a consistent focus on theoretical frameworks that connect physics with ecology. Her work shows increasing integration of quantum mechanics with ecological modeling, particularly in understanding emergence and time evolution in complex systems. She frequently employs network theory to analyze ecological communities and has developed innovative approaches to studying species interactions, mutualistic networks, and spatial dynamics in meta-communities. Minerva Fellowship Heisenberg Fellowship DFG Fellowship for research at MIT Professor Drossel has supervised numerous doctoral students whose work spans theoretical ecology, complex systems, and statistical physics. Her research group has secured funding for projects examining the stability of ecological networks, quantum decoherence, and the mathematical foundations of complex systems. She maintains active collaborations with researchers across Europe and has contributed to major theoretical advances in understanding how complexity emerges from simple interactions in diverse systems. The AG Drossel research group operates at the intersection of physics and theoretical biology, maintaining strong connections with both the physics and biology departments at TU Darmstadt. The group combines mathematical rigor with biological relevance, developing models that capture essential features of complex natural systems while remaining analytically tractable. Their work has influenced both theoretical physics and ecological theory, demonstrating the power of interdisciplinary approaches to complex systems.

Ruming Zhangمشاهده پروفایل

عضو هیئت علمی

• Partial Differential Equations
• Inverse Problems
• Numerical Methods
• +۴ مورد دیگر

Prof. Dr. Ruming Zhang is a Tenure-Track Professor at TU Berlin's Faculty II - Mathematics and Natural Sciences, leading the Analysis and Applications group since May 2023. He specializes in numerical methods for partial differential equations and inverse problems, with a focus on wave scattering and periodic structures. His research bridges theoretical analysis and computational techniques, addressing challenges in areas like photonic crystals and non-destructive testing. Education & Career: PhD in Mathematics (Chinese Academy of Sciences, 2014) Postdoctoral Researcher at Michigan Technological University Marie-Curie Fellow (University of Bremen, 2015-2018) Junior Group Leader at KIT (Karlsruhe Institute of Technology, 2018-2023) Research Interests: Analysis and numerical methods for PDEs, inverse scattering problems, waveguide analysis, periodic structures, and their applications in nanotechnology and engineering. His work emphasizes high-order numerical schemes and theoretical frameworks for ill-posed problems. Key Contributions: Development of nonuniform mesh methods for periodic surface scattering, high-order numerical techniques for bi-periodic structures, and monotonicity-based shape reconstruction in waveguides. His methods address challenges in computational wave physics and mathematical modeling. Awards: Richard-von-Mises Prize (GAMM, 2023) Marie-Curie Fellowship (EU FP7-PEOPLE, 2015-2017) Teaching & Mentorship: Offers courses on inverse problems, scattering theory, boundary element methods, and applied analysis. Advises students on thesis topics in mathematical theory for photonic crystals and wave propagation. Actively promotes interdisciplinary collaboration between mathematicians and engineers. Grants & Projects: DFG Grant (2019-2024): Higher-order methods for acoustic scattering in periodic structures Marie-Curie COFUND Fellowship (Bremen TRAC, 2015-2017) Labs/Teams: Leads the Analysis and Applications research group at TU Berlin, focusing on interdisciplinary projects combining mathematical theory with computational tools for real-world applications.

• Quantum Information Theory
• Quantum Many-Body Theory
• Quantum Optics
• +۷ مورد دیگر

Prof. Dr. Jens Eisert is a Professor at the Free University of Berlin, where he leads the Quantum Many-Body Theory, Quantum Information Theory, and Quantum Optics research group (Eisert AG) within the Institute of Theoretical Physics at the Dahlem Center for Complex Quantum Systems. His office is located at Arnimallee 14, Room 1.3.06 in Berlin-Dahlem. His research focuses on the intersection of quantum information theory and condensed matter physics, specifically exploring what information processing tasks are possible using individual quantum systems as information carriers. His group develops mathematical-theoretical foundations of quantum information, particularly in entanglement theory and tomography, while also investigating quantum optical implementations using light modes or cold atoms in optical lattices. A major emphasis of their work is on quantum many-body systems, including static properties, efficient numerical simulation methods like tensor networks, and non-equilibrium quantum dynamics. Recent publications highlight significant contributions in thermalization of quantum systems (Communications Physics 2025), quantum thermodynamics (Nature Physics 2025), and quantum error correction (PRX Quantum 2025). The group's work is characterized by combining the rigor of mathematical physics with physically motivated applicability, frequently leading to direct collaborations with experimental groups. Quantum Information Theory Quantum Many-Body Theory Quantum Optics Entanglement Theory Tensor Networks Quantum Error Correction Prof. Eisert maintains active supervision of numerous PhD students and postdoctoral researchers, with research positions regularly available in areas including quantum error correction, quantum information theory, tensor networks, and quantum simulation. His group has published extensively in top journals including Nature Physics, PRX Quantum, and Physical Review series.

Max Fathiمشاهده پروفایل

استاد

• Optimal Transport
• Probability Theory
• Statistical Physics
• +۹ مورد دیگر

Max Fathi is a Professor of Mathematics at Université Paris Cité, affiliated with the Laboratoire Jacques-Louis Lions (LJLL) and Laboratoire de Probabilités, Statistique et Modélisation (LPSM). He concurrently holds a part-time teaching position at the Department of Mathematics and Applications (DMA) at École Normale Supérieure (ENS). Since 2023, he has been a member of the Institut Universitaire de France (IUF), a prestigious national research fellowship in France. He completed his PhD in 2013 at Université Pierre et Marie Curie under Cédric Villani, followed by a postdoctoral position at the University of California, Berkeley with Lawrence C. Evans and Fraydoun Rezakhanlou. Previously, he was a CNRS researcher at the Institut de Mathématiques de Toulouse before joining Université Paris Cité. His habilitation thesis (2019) focuses on optimal transport applications in analysis and probability. Fathi's research centers on optimal transport theory, particularly its applications to analysis, probability, and statistical physics. Key topics include interacting particle systems, functional inequalities (e.g., Poincaré, log-Sobolev), high-dimensional phenomena, Ricci curvature in discrete/continuous spaces, Stein's method, concentration of measure, and numerical methods for stochastic dynamics. His work is supported by the ANR project 'Conviviality.' He has delivered courses on functional analysis at ENS and participated in summer schools, including an MSRI course on functional inequalities and localization techniques. His teaching materials include lecture notes on optimal transport and stochastic processes. His contributions have been recognized through awards such as the IUF membership. Notable research collaborations include work with Thomas Courtade, Matthias Erbar, and Gabriel Stoltz on topics ranging from stability estimates of inequalities to hypocoercivity and numerical analysis of stochastic systems.

• Optimal Control of Partial Differential Equations
• Variational Inequalities
• Nonsmooth Infinite-dimensional Optimization
• +۵ مورد دیگر

Dr. Constantin Christof is a Lecturer (Akademischer Rat auf Zeit) at the Department of Mathematics , Technische Universität München , with prior roles as a W2 Stand-in Professor at Universität Augsburg and Research Associate at TUM and TU Dortmund. His research focuses on Optimal Control of PDEs , Variational Inequalities , and Nonsmooth Optimization , with applications in Non-Newtonian Fluids and Neural Networks . May 2015 - July 2018: Dr. rer. nat. in Mathematics, TU Dortmund Oct. 2013 - July 2014: MAST (Part III of Mathematical Tripos), University of Cambridge Oct. 2009 - Sept. 2012: B.Sc. in Technomathematics and Mathematics, TU Dortmund Christof's work bridges Finite Element Error Analysis , Sensitivity Analysis , and Physics-Guided Machine Learning , particularly in problems involving Contact Mechanics and Parabolic PDE Constraints . His recent publications address challenges in Semilinear Elliptic PDEs , Obstacle Problems , and Nonsmooth Superposition Operators , with a focus on theoretical and numerical advancements. Scientific awards include the Dissertation Award and Best Graduate Award from TU Dortmund, and the Award for Academic Excellence by the Minister President of North Rhine-Westphalia. He has supervised 11 theses at the Master's and Bachelor's levels, covering topics from Neural Network Surrogate Models to Bingham Fluid Simulations .

Elena Mäder-Baumdickerمشاهده پروفایل

استاد

• Differential Geometry
• Geometric Variational Problems
• Minimal Surfaces
• +۴ مورد دیگر

Prof. Dr. Elena Mäder-Baumdicker holds a Tenure Track position in Geometry at TU Darmstadt since 2019. She was previously a postdoc at KIT and conducted research at Princeton University. Currently, she is organizing conferences such as the BIRS workshop on Non-linear Critical Point Theory and the Women in Geometry 3 conference. Her research focuses on global and local questions in differential geometry, particularly geometric variational problems involving minimal surfaces and Willmore surfaces. She has supervised two active PhD students (Nils Neumann and Jona Seidel), along with several Master’s and Bachelor’s theses. She is a member of the SPP 2026 'Geometry at infinity' and actively engages in academic outreach, including the Meet&Math initiative and public lectures for school students. Her teaching includes courses on differential geometry and seminars on geometric analysis. Education: PhD in Mathematics (2014, Freiburg), Diploma in Mathematics (2010, Freiburg) Research Interests: Minimal Surfaces, Willmore Surfaces, Geometric Flows (e.g., Mean Curvature Flow), Alexandrov Spaces, Variational Problems in Geometry Recent Conferences: Co-organizer of the Ernst Kuwert 60th Birthday Conference (2021), Geometric Analysis meets Geometric Topology (2019), and multiple BIRS events (2023–2025) Grants & Fellowships: DFG-funded research position (2017–2019), Leopoldina Fellowship (2018–2019) Labs/Teams: Leads the 'Variational Problems in Geometry' team at Women in Geometry 3, co-runs the RMU Seminar 'Solution Concepts in PDEs' Her work emphasizes the interplay between geometric analysis and topology, with a focus on singularities in geometric flows and applications of variational methods. She actively participates in promoting gender equality in academia through her role in the AK-Gleichstellung committee.

David Luitzمشاهده پروفایل

استاد

• Quantum Many-Body Systems
• Condensed Matter Physics
• Theoretical Physics
• +۳ مورد دیگر

David Luitz is a Professor of Theoretical Physics at Bonn University, specializing in quantum many-body systems. His research focuses on fundamental aspects of quantum dynamics, including many-body localization, quantum information spreading, and non-equilibrium thermodynamics. He develops advanced numerical methods and software tools such as the DanceQ library to tackle complex many-body problems. His work bridges theoretical physics with computational techniques, addressing topics like entanglement entropy, out-of-time-order correlators, and phase transitions in interacting quantum systems. Key research areas include the interplay between disorder and thermalization, quantum chaos, and the application of quantum Monte Carlo methods to study correlated electron systems. His contributions have advanced understanding of eigenstate thermalization, dissipative dynamics, and topological phenomena in condensed matter systems. Dr. Luitz’s publications highlight innovations in numerical algorithms for many-body systems and experimental predictions for quantum phase transitions. His work often explores the boundaries between localized and thermalizing regimes, with applications to both theoretical and computational physics.

• Mathematical Statistics
• Machine Learning
• Kernel Methods
• +۳ مورد دیگر

Prof. Dr. Nicole Mücke is a Professor in the Institute for Mathematical Stochastics at the Carl-Friedrich-Gauss Faculty of Technische Universität Braunschweig. Her research focuses on mathematical statistics, machine learning, kernel methods, and statistical inference. She explores topics such as neural network theory, inverse problems, optimization, and regularization techniques. Her work bridges theoretical foundations with practical applications in areas like distributed computing and uncertainty quantification. Prof. Mücke’s research portfolio includes contributions to empirical risk minimization, neural operator learning, and gradient-based optimization. She investigates the interplay between overparameterization and generalization in machine learning models, as well as the design of efficient algorithms for large-scale problems. Her publications span topics ranging from distributed stochastic gradient descent to localized kernel regression techniques. Her recent work emphasizes theoretical guarantees for learning algorithms, including convergence rates, statistical performance in high-dimensional settings, and the role of regularization in inverse problems. She also explores methodological advancements in spectral methods, algorithm unfolding, and data-splitting strategies to enhance statistical efficiency. Prof. Mücke’s research is characterized by a strong emphasis on rigorously analyzing machine learning algorithms through the lens of statistical theory and functional analysis. Her contributions address challenges in both classical and modern machine learning paradigms, with a focus on bridging the gap between abstract mathematical frameworks and practical implementation.

Konstantin Nestmannمشاهده پروفایل

پژوهشگر ارشد

• Quantum Thermodynamics
• Open Quantum Systems
• Quantum Information
• +۳ مورد دیگر

Konstantin Nestmann is a Postdoctoral Researcher in the Department of Physics at Lund University's Faculty of Engineering since 2023, funded by an EU Marie Skłodowska-Curie fellowship. His research focuses on quantum thermodynamics and open quantum systems using techniques from statistical field theory and quantum information. His educational background includes a PhD in Physics (summa cum laude) from RWTH Aachen University (2018-2022) and a combined BSc and MSc in Physics from Dresden University of Technology (2011-2018). His doctoral thesis addressed the dynamics of open quantum systems from quantum information and statistical physics perspectives. Nestmann's research centers on non-perturbative quantum master equations, quantum non-Markovianity, and quantum thermodynamics. He develops theoretical frameworks and computational tools, such as the RealTimeTransport library, to study quantum transport in the strong coupling regime. His work bridges fundamental quantum dynamics with practical applications in nanoelectronics. His publication record from 2019 to 2024 reveals a consistent focus on the interplay between time-local and time-nonlocal descriptions of quantum dynamics, with significant contributions to renormalization group methods and fermionic duality. These works span exact solutions for open quantum systems, non-perturbative approximations, and numerical implementations. Scientific awards include: Marie Skłodowska-Curie fellowship Nestmann has served as a lead teaching assistant for Quantum Information and Introduction to Theoretical Physics, and as a teaching assistant for Statistical Physics and Quantum Mechanics. His research is supported by the Marie Curie fellowship and collaborative projects with institutions including RWTH Aachen and Forschungszentrum Jülich. He is an active member of the quantum transport and open systems research community, contributing to open-source software development and collaborative theoretical advancements.

André Uschmajewمشاهده پروفایل

استاد

• Numerical Analysis
• Tensor Methods
• Low-Rank Approximation
• +۷ مورد دیگر

Prof. Dr. André Uschmajew is a full professor and holds the Chair of Mathematical Data Science at the Institute of Mathematics, Faculty of Mathematics, Natural Sciences, and Materials Engineering, University of Augsburg, Germany. He has held prominent research and academic positions at institutions including the Max Planck Institute for Mathematics in the Sciences (Leipzig), University of Bonn, and EPF Lausanne. 2022–present: Chair of Mathematical Data Science, University of Augsburg 2017–2022: Research Group Leader, Max Planck Institute MiS Leipzig 2014–2017: Bonn Junior Fellow Professorship, University of Bonn 2013: Ph.D. in Mathematics, TU Berlin His research centers on the theoretical and computational aspects of low-rank tensor and matrix approximations, with deep connections to Riemannian optimization, functional analysis, and high-dimensional scientific computing. He investigates the geometry of low-rank varieties, convergence of alternating algorithms, and applications in data science and dynamical systems. His work combines rigorous mathematical analysis with algorithmic innovation. The recent publications (2023–2025) reflect a strong focus on optimization methods for low-rank structures, dynamical low-rank approximation for PDEs like the Vlasov-Poisson equation, randomized SVD, Sinkhorn-type algorithms with overrelaxation, and Kronecker product operator approximation. Key themes include convergence analysis, algorithmic acceleration, and applications in scientific computing and signal processing. Although no specific awards are listed, his publication record in top-tier journals such as Numerische Mathematik , SIAM Journal on Optimization , and Foundations of Computational Mathematics indicates significant recognition in applied mathematics and numerical analysis. He advises students and researchers in mathematical data science and numerical analysis, though specific advisees are not named. He teaches courses such as Kernel Methods and Linear Algebra II. He has collaborated with leading researchers including Bart Vandereycken, Daniel Kressner, and Wolfgang Hackbusch. His work is supported through institutional affiliations and likely research grants, though specific grants are not listed. He is actively involved in the development of numerical methods for high-dimensional problems, particularly using tensor networks and manifold optimization. He is affiliated with research teams at the University of Augsburg and previously led a group at the Max Planck Institute MiS Leipzig, focusing on mathematical aspects of data science and tensor methods.

• Mathematical Analysis of Neural Networks
• Numerical Analysis
• Partial Differential Equations
• +۷ مورد دیگر

Dr. Adrian Riekert is a researcher at the Institute for Analysis and Numerical Analysis within the Department of Mathematics and Computer Science at the University of Münster. He is actively involved in the Applied Mathematics Münster research cluster, focusing on the theoretical foundations of machine learning and numerical analysis. His research interests lie at the intersection of deep learning, numerical methods for partial differential equations, and optimization theory. Specifically, he investigates the convergence properties of gradient descent and stochastic gradient descent in training ReLU-activated neural networks, the error analysis of training dynamics with random initializations, and high-dimensional approximation via deep operator learning. His work contributes to overcoming the curse of dimensionality in PDE solvers through neural networks. The recent publications highlight a strong trend in the mathematical analysis of deep learning, particularly focusing on convergence proofs, gradient flows, and error bounds for neural network training. These works often involve collaboration with Prof. Arnulf Jentzen and others, advancing the theoretical understanding of deep learning algorithms. Dr. Riekert has not been listed with any scientific awards in the provided text. There is no information available regarding student advising or research grants. However, his active publication record suggests ongoing research supported by institutional or project-based funding, likely within the broader Mathematics Münster excellence cluster. He is part of the Applied Analysis research group at the University of Münster, which is engaged in projects related to deep learning, high-dimensional PDEs, and stochastic algorithms. This team collaborates on advancing the mathematical theory behind modern machine learning and computational methods.

Paul Mannsمشاهده پروفایل

استادیار

• Mathematical Optimization
• PDE-Constrained Optimization
• Integer Programming
• +۱ مورد دیگر

Paul Manns is an Assistant Professor of Optimization at TU Dortmund University's Department of Mathematics, appointed in 2021. His research specializes in mathematical optimization involving partial differential equations and integer constraints, with emphasis on regularization techniques and trust-region algorithms. Education includes: Ph.D. in Mathematics, TU Braunschweig (2019) Computational Engineering studies, TU Darmstadt Prior research experience includes positions at Heidelberg University, TU Braunschweig, and Argonne National Laboratory (USA), including a James H Wilkinson Fellowship. Recent publications develop novel methods for mixed-integer control problems, domain decomposition, and convergence analysis in non-convex optimization spaces.

Tobias Glasmachersمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Optimization Algorithms
• +۶ مورد دیگر

Prof. Dr. Tobias Glasmachers is a Full Professor at the Institut für Neuroinformatik , Ruhr-Universität Bochum, Germany, specializing in the Theory of Machine Learning . He leads the Optimization of Adaptive Systems group and holds appointments in both Computer Science and Interdisciplinary AI research. Key Research Areas : Optimization algorithms, evolutionary computation, reinforcement learning, supervised learning, and neural networks Technical Focus : Gradient-based methods, support vector machines, and adaptive coordinate descent Applications : Robotics, waste sorting facilities, 3D game environments (e.g., Doom/Minecraft), and human-centered AI design Notable Contributions : Development of LM-MA-ES evolution strategy, Hessian Estimation Evolution Strategy, and tachAId tool for ethical AI design. His work bridges theoretical analysis with practical implementations across diverse domains. Teaching : Offers courses in Informatik 1 - Programmieren, Machine Learning: Supervised Methods, and Evolutionary Algorithms. Supervises numerous Bachelor's and Master's theses on AI/ML applications.

Haye Hinrichsenمشاهده پروفایل

استاد

• Quantum Information Theory
• Exactly Solvable Systems
• Tensor Networks
• +۴ مورد دیگر

Prof. Haye Hinrichsen holds a C3 professorship at the University of Würzburg within the Chair of Theoretical Physics III . His academic journey includes roles as Acting Professor at the University of Wuppertal (2001), Senior Assistant at Duisburg-Essen (2000), and postdoctoral research at institutions like the Weizmann Institute of Science (1995–1997). He earned his PhD in 1993 from the University of Bonn under Prof. V. Rittenberg, focusing on quantum groups. His research interests span quantum information theory , exactly solvable systems , statistical physics far from equilibrium , reaction-diffusion processes , and non-equilibrium phase transitions . Notable achievements include the 2007 Award for Good Teaching and contributions to understanding entropy-based tuning systems in music. His recent work explores topics like causal set propagators in anti-de Sitter spacetime, eigenmodes on hyperbolic lattices, and renormalization techniques in quantum field theories. He has also applied physics principles to music acoustics, developing adaptive tuning schemes using entropy maximization. Key Research Themes: Quantum gravity, non-equilibrium dynamics, topological complexity, and interdisciplinary applications in music. Teaching: Courses include Theoretical Physics I, Computational Physics, and General Relativity. Lab/Affiliation: Chair of Theoretical Physics III at Hubland South, Building M1.