جستجوی استاد راهنمای دانشگاهی

• Scientific Machine Learning
• Numerical Linear Algebra
• Numerical Multilinear Algebra
• +۷ مورد دیگر

Peter Benner is a Professor and Director at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, where he leads the Computational Methods in Systems and Control Theory group. He also holds an Honorarprofessor position for Mathematics at Otto-von-Guericke Universität Magdeburg since 2011. Benner has previously served as Managing Director of the Max Planck Institute during multiple periods (2013-2014, 2021-2022, and 2025-2026), demonstrating his leadership in the field. Benner's research focuses on Scientific Machine Learning, Numerical Linear and Multilinear Algebra, Model Order Reduction and Reduced-order Modeling, Numerical Methods in Systems and Control Theory, PDE Constrained Optimization, High-performance and Power-aware Computing, and Mathematical Software development. His work bridges theoretical mathematics with practical engineering applications, particularly addressing challenges in large-scale dynamical systems. Analysis of his recent publications reveals a strong emphasis on developing efficient computational methods for complex systems. Benner has pioneered approaches combining model order reduction with tensor methods to tackle high-dimensional problems in uncertainty quantification and PDE-constrained optimization. His work shows a consistent trend toward integrating data-driven techniques with traditional model-based approaches, particularly for nonlinear and parametric systems. Throughout his career, Benner has actively mentored students and collaborated with researchers worldwide, delivering numerous invited talks at prestigious institutions and conferences across Europe, North America, and Asia. His research has received significant funding, supporting the development of mathematical software and computational methods for industrial applications. Benner leads the Computational Methods in Systems and Control Theory department at the Max Planck Institute, which focuses on developing and implementing advanced numerical methods for large-scale dynamical systems. The group maintains strong connections with both theoretical mathematics and practical engineering applications, particularly in fluid dynamics, energy systems, and control theory.

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.

Karsten Urbanمشاهده پروفایل

استاد

• Numerical Mathematics
• Scientific Computing
• Partial Differential Equations
• +۹ مورد دیگر

Prof. Karsten Urban is a Full Professor of Numerical Mathematics at the University of Ulm, leading the Institute for Numerical Mathematics. He holds roles such as Dean of Studies in Computational Science and Engineering (CSE) and Deputy Spokesman for the Research Association for Scientific Computing in Baden-Württemberg. He is an active member of prestigious societies including the Deutsche Mathematikervereinigung (DMV) and SIAM. His academic journey includes a PhD from RWTH Aachen (1995), Habilitation (2001), and a full professorship at Ulm since 2005. Research focuses on numerical methods for PDEs, reduced basis techniques, multiscale simulations in fluid mechanics, biomechanics, quantum sciences, and financial mathematics. He has pioneered wavelet-based methods and collaborated with industries on ship propulsion and energy trading models. His work integrates mathematical rigor with real-world applications, emphasizing model reduction and computational efficiency. Editorial Roles: Managing Editor of Advances in Computational Mathematics , Editor of SN Partial Differential Equations and Applications . Awards: Teaching award of Baden-Württemberg (2005), Science-Economy Cooperation Awards (2004, 2008). Administrative Roles: Member of the University Council and ASIIN expert committee. Supervises doctoral students in numerical analysis, quantum simulations, and biomechanics. Active in interdisciplinary projects, including quantum systems (IQST) and fracture healing modeling in collaboration with biomechanics experts. His contributions bridge academia and industry, driving innovation in computational methods.

Martin Greplمشاهده پروفایل

استاد

• Numerical Methods for Partial Differential Equations (PDEs)
• Model Order Reduction
• Reduced Basis Methods
• +۴ مورد دیگر

Professor Dr. Martin Grepl is a faculty member at RWTH Aachen University, where he holds the Lehr- und Forschungsgebiet Optimierung mit partiellen Differentialgleichungen (Teaching and Research Area in Optimization with Partial Differential Equations). He has been affiliated with RWTH Aachen since 2009, first as a Professor (W1) and since 2014 as a Professor (W2). Education: Diplom-Ingenieur (Aerospace Engineering), University of Stuttgart (2000) Master of Science (Mechanical Engineering), MIT (2001) Doctor of Philosophy (Mechanical Engineering), MIT (2005) His research focuses on numerical methods for partial differential equations (PDEs) , particularly model order reduction , reduced basis methods , finite element methods , and optimal control for parametrized PDEs. He also investigates parameter estimation , inverse problems , and control constraints in elliptic and parabolic PDE systems. The scientific awards he has received include the Studienstiftung des deutschen Volkes (1997-2000), a Fellowship from the Dr. Jürgen Ulderup-Stiftung (1998-1999), and the Lehrpreis der Fachschaft Mathematik/Physik/Informatik (2011). His work spans applications in manufacturing , medical physics , and fluid dynamics , as evidenced by his patents and collaborative research. His publications demonstrate expertise in reduced basis methods for nonaffine/nonlinear PDEs , trust region optimization , and error bounds for real-time and many-query scenarios. His collaborations often involve interdisciplinary applications, including thermal conduction , welding processes , and glomerular filtration modeling .

• Probabilistic Machine Learning
• Physics-Informed Machine Learning
• Industrial Applications of Data Science
• +۴ مورد دیگر

Markus Lange-Hegermann serves as Professor of Mathematics and Data Science at Ostwestfalen-Lippe University of Applied Sciences (TH OWL) since 2018 and holds a board position at the Institute for Industrial Information Technology (inIT). His career bridges academic research and industrial applications, with expertise in translating machine learning theory into practical engineering solutions for automation and manufacturing sectors. His educational foundation includes a Diplom (Master equivalent) in Computer Mathematics from RWTH Aachen University (2004-2008) followed by a Dr. rer. nat. (PhD equivalent) in algorithmic differential algebra (2008-2014). Prior to academia, he gained industry experience at FEV GmbH as an R&D engineer (2014-2017) and P3 automotive GmbH as a Data Science Consultant (2017-2018). Lange-Hegermann's research centers on probabilistic machine learning with distinctive emphasis on physics-informed approaches. He develops Gaussian process methodologies that incorporate differential equations to model time dependencies, uncertainties, and physical constraints in industrial systems. His work enables robust data-based modeling and optimization for cyber-physical systems, with applications spanning predictive maintenance, process control, and quality assurance in manufacturing. Analysis of his 15 most recent publications (2024-2025) reveals consistent innovation in physics-integrated machine learning, particularly using Gaussian processes to solve partial differential equations and optimal control problems. The research demonstrates strong industrial applicability across domains including medical imaging, material science, automotive engineering, and brewing processes, with recurring themes of anomaly detection in time-series data and uncertainty-aware decision making. His scientific contributions have earned significant recognition: Forschungspreis TH OWL (2024) Top reviewer award at NeurIPS (2023) Outstanding reviewer award at NeurIPS (2021) Best poster award at Bosch AI CON (2019) Borchers Plakette for outstanding dissertation (2014) Springorum Denkmünze for outstanding diploma (2009) As chairman of the Data Science study program and vice chairman of undergraduate examination boards, Lange-Hegermann actively shapes academic curricula while supervising graduate theses. His governance roles include serving on professorship search committees at multiple institutions and contributing to examination regulations. He maintains active research funding through collaborations with industrial partners and reviews proposals for initiatives like It's OWL and 3IA Côte d’Azur. Lange-Hegermann leads the Mathematics and Data Sciences research group within inIT, fostering collaboration between theoretical machine learning and industrial automation. He co-founded AICOmmunityOWL and the Informatics Europe working group on Data Analysis and Reporting, while organizing machine learning reading groups and data science hackathons to bridge academic research with industrial problem-solving.

• 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 .

Irwin Youseptمشاهده پروفایل

استاد

• Electromagnetics
• Numerical Analysis
• Optimization
• +۳ مورد دیگر

Prof. Dr. Irwin Yousept is a Full Professor of Mathematics at Universität Duisburg-Essen, leading the research group AG Optimal Control of Partial Differential Equations. His work focuses on the mathematical analysis and numerical solutions of electromagnetic problems, particularly in superconductivity and inverse problems. He holds a PhD from TU Berlin (2008) and has held academic positions at TU Darmstadt and TU Berlin. His research includes PDE-constrained optimization, numerical analysis, and applications in high-temperature superconductivity and electromagnetic shielding. Affiliations: Universität Duisburg-Essen, Fakultät für Mathematik Education: Diplom (2005), PhD (2008) in Mathematics from TU Berlin Research interests span Maxwell's equations, numerical methods for PDEs, and optimal control, with applications in superconductivity, electromagnetic shielding, and induction heating. He has authored over 40 publications and received awards including the Richard-von-Mises-Preis GAMM (2014). Current grants include DFG-funded projects on inverse problems and superconductivity.

Boris Vexlerمشاهده پروفایل

استاد

• Numerical Analysis
• Optimal Control
• Partial Differential Equ as Mathematics (summa cum laude)
• +۱۰ مورد دیگر

Prof. Boris Vexler, born in 1977 in Moscow, Russia, is a Professor of Optimal Control at the TUM Department of Mathematics under the TUM School of Computation, Information and Technology . He has served as Dean of Studies since 2015 and as speaker of the International Research Training Group IGDK 1754 since 2012. Education: Diploma (2000) and Ph.D. (2004) in Mathematics from the University of Heidelberg; Habilitation (2008) from the University of Graz. His research focuses on numerical analysis of partial differential equations (PDEs) , particularly finite element methods for optimal control problems governed by parabolic, elliptic, and hyperbolic PDEs. Key contributions include error estimates , adaptive discretization , and handling state constraints and measure-valued controls . Recent publications emphasize transient Stokes equations , Navier-Stokes control , and sparsity-constrained optimization . His work often integrates scientific computing and uncertainty quantification . Scientific Awards: Award for best supervisor of the elite degree program TopMath (2018) Finalist, ECCOMAS Prize for best dissertation (2005) Leslie Fox Prize in Numerical Analysis, 2nd place (2004)

Volker Mehrmannمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Differential-Algebraic Equations
• Control Theory
• +۴ مورد دیگر

Volker Mehrmann is a full professor at the Technical University of Berlin in the Institute of Mathematics , Faculty II - Mathematics and Natural Sciences. He has held academic positions at Chemnitz University of Technology and RWTH Aachen University . His roles include leadership in research centers: Spokesperson for the DFG Research Center Matheon (2008-2016), President of the European Mathematical Society (2017-2022), and committee member of the Cluster of Excellence MATH+. PhD: Bielefeld University (1982) Habilitation: Bielefeld University (1987) His research interests span Numerical Linear Algebra , Differential-Algebraic Equations (DAEs) , Control Theory , and Industrial Mathematics . Recent work focuses on port-Hamiltonian systems and model order reduction for multi-physics applications. Key scientific contributions include: ERC Advanced Grant (2011-2016) on multi-physics systems Hans Schneider Prize (2019) SIAM Fellow (2011) and AMS Fellow (2022) He serves as editor-in-chief of Linear Algebra and Its Applications and contributes to numerous editorial boards. His leadership roles include presidency in the European Mathematical Society and GAMM .

Dominik Huberمشاهده پروفایل

پژوهشگر

• Dynamic Resource Management in High-Performance Computing (HPC)
• Parallel & Distributed Programming Models
• Hardware-aware Programming
• +۶ مورد دیگر

Dominik Huber is a Ph.D. candidate and researcher at the Technical University of Munich , affiliated with the Chair of Computer Architecture & Parallel Systems . His work focuses on Dynamic Resource Management in High-Performance Computing (HPC) , with expertise in Parallel & Distributed Programming Models and Hardware-aware programming . He has actively contributed to teaching courses like Parallel Programming Systems and Advanced Computer Architecture . His research emphasizes adaptive resource allocation in hybrid HPC clusters, leveraging technologies such as MPI Sessions , PMIx , and frameworks like LAIK and XBraid . Recent projects include the DynRes software suite for dynamic resource management and collaborations on quantum-HPC integration. Huber has advised students on topics ranging from Dynamic Resource Management in Charm++ to CI Systems for HPC Software , and his publications address challenges in malleability, scheduling, and power-constrained environments. Current affiliations include participation in the SEANERGYS (EuroHPC) and PlasmaPEPS projects.

Joscha Gedickeمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +۳ مورد دیگر

Joscha Gedicke is a Professor at the Institute for Numerical Simulation (University of Bonn), specializing in Numerical Analysis , Finite Element Methods , and Scientific Computing . His research focuses on adaptive algorithms, error estimation, and computational methods for partial differential equations (PDEs) and optimal control problems. Contact: gedicke@ins.uni-bonn.de | +49 228 73-69835 Teaching: Lectures on Hybrid High-Order Methods (V5E1), Adaptive Finite Element Methods (S4E1), and Discontinuous Galerkin Methods (V5E5). Research Trends: Gedicke's work spans Numerical Methods for PDEs , Adaptive Finite Element Analysis , Mixed and Discontinuous Galerkin Formulations , and Error Estimation . His recent publications emphasize Virtual Element Methods and Robust Discretizations for magnetostatic and optimal control problems. Collaborative Networks: He collaborates with researchers in computational mathematics, including institutions like TU Munich, University of Milano-Bicocca, and the University of Bonn's research seminar on Mathematics of Computation .

Martin Schmidtمشاهده پروفایل

استاد

• Mathematical Optimization
• Energy Systems
• Mixed-Integer Nonlinear Optimization
• +۳ مورد دیگر

Martin Schmidt is a Full Professor (W3) for Nonlinear Optimization at the Department of Mathematics, Trier University, since 2019. He has held leadership roles in research training groups and international committees, focusing on mathematical modeling and optimization of energy systems, gas networks, and market equilibria. His work bridges mixed-integer nonlinear optimization , bilevel optimization , and robust methods with applications to real-world energy challenges. Education: PhD in Mathematics (2013), Diplom in Mathematics (2008), both from Leibniz University Hannover. Editorial Roles: Editorial Board member of Journal of Optimization Theory and Applications and Optimization Letters , Associate Editor for OR Spectrum and EURO Journal on Computational Optimization . His research integrates complex physical systems (e.g., gas transmission networks) with game-theoretic models to analyze energy markets. Recent publications emphasize robust optimization , decomposition techniques , and machine learning integration in bilevel frameworks. Awards highlight his contributions to gas market feasibility , linear bilevel optimization , and practical applications in energy systems. Collaborations span institutions like Universidad Zaragoza, Sapienza University, and Forschungszentrum Jülich.

Andrea Waltherمشاهده پروفایل

استاد

• Nonlinear Optimization
• Algorithmic Differentiation
• Mathematical Optimization
• +۳ مورد دیگر

Andrea Walther is a Professor of Mathematical Optimization at the Humboldt University of Berlin, holding a position within the Faculty of Mathematics and Natural Sciences. She leads the Mathematical Optimization research group at the Institute of Mathematics, focusing on algorithmic differentiation, nonlinear optimization, and applied mathematics. Her academic journey includes a Diploma in Business Mathematics (1996, University of Bayreuth), a PhD (1999, TU Dresden), and habilitation (2008, TU Dresden). She has held roles such as Junior Professor at TU Dresden (2007–2008) and Professor at the University of Paderborn (2009–2019) before joining Humboldt in 2019 as a MATH+ Professor. Education : 1991–1996: Studies in Business Mathematics, University of Bayreuth 1996: Diploma in Business Mathematics, University of Bayreuth 1999: PhD in Mathematics, TU Dresden 2008: Habilitation, TU Dresden Her research interests center on optimization methods, particularly algorithmic differentiation (e.g., ADOL-C software), nonsmooth optimization, and applications in engineering and machine learning. She leads initiatives like the Cluster of Excellence MATH+ and contributes to projects such as the Transregio 154. Key Projects: Co-PI of DFG Project 'Mixed-integer non-smooth optimization for gas market problems' (2020–2022) Principal Investigator in MATH+ Projects (EF3-7, AA2-7) Co-developer of ADOL-C, a widely used tool for algorithmic differentiation Notable awards include being a SIAM Fellow. Her work bridges theoretical advancements and practical applications, with contributions to energy sector optimization, inverse problems, and computational frameworks for solving complex systems.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

• Energy Systems Modeling
• Geothermal Energy
• Optimization Algorithms
• +۳ مورد دیگر

Smajil Halilovic is a Researcher at the Chair of Renewable and Sustainable Energy Systems at the Technical University of Munich . His work focuses on energy systems modeling and optimization, particularly for geothermal and renewable energy integration. Current projects include Geo.KW , a coupled hydrothermal and infrastructure model for urban-scale geothermal use Research emphasizes optimization techniques for groundwater heat pump systems Key contributions in PDE-constrained optimization and thermal resource assessment Halilovic has published extensively in Renewable Energy and Energy Conversion and Management , with recent work on spatial optimization of geothermal systems and urban heat planning. His collaborations with Prof. Thomas Hamacher and Kai Zosseder highlight his role in advancing sustainable energy infrastructure. He teaches Mathematical Modeling of Complex Systems in the Energy Field and contributes to interdisciplinary projects like the Interdisciplinary project internship: Concept development of a renewable energy system in a developing country . His publications demonstrate expertise in geothermal integration, optimization algorithms, and urban energy modeling.