جستجوی استاد راهنمای دانشگاهی

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.

• Scientific Machine Learning
• Numerical Linear Algebra
• Numerical Multilinear Algebra
• +۷ 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.

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

Prof. Massimo Fornasier holds the Chair of Applied Numerical Analysis at the Technical University of Munich (TUM), within the School of Computation, Information and Technology and the Department of Mathematics. His research focuses on mathematical modeling, numerical analysis, and data-driven methods, particularly in areas like compression, sparse recovery, and optimization. He has made significant contributions to consensus-based optimization, control of multiagent systems, and applications in image/signal processing. Education: PhD in Computational Mathematics, University of Padua (2003) Postdoctoral fellowships at University of Vienna, Sapienza University of Rome, and Princeton University Awards: ERC Starting Grant (2012) START Prize (2011) Prix de Boelpaepe (2009) His work bridges theoretical analysis and computational methods, with applications ranging from compressive sensing to machine learning. Recent research emphasizes consensus-based optimization frameworks and their global convergence properties. Editorial roles include journals like Networks and Heterogeneous Media and Calcolo . He leads research groups in areas such as Data Science and Numerical Analysis at TUM.

• Mathematical Modeling
• Data Analysis
• Image/Signal Processing
• +۵ مورد دیگر

Professor Steffen Dereich is a leading researcher in mathematical stochastics at the University of Münster's Faculty of Mathematics and Computer Science, where he serves as Professor at the Institute of Mathematical Stochastics. He is an active investigator in the Mathematics Münster cluster of excellence, contributing significantly to the fields of stochastic processes and machine learning theory. His primary research interests span Stochastic Processes , Machine Learning , Deep Learning , Complex Networks , and Stochastic Analysis . Dereich has developed a unique research program that bridges classical probability theory with modern machine learning challenges, particularly focusing on the mathematical foundations of optimization algorithms used in deep learning. His work on stochastic gradient descent methods, especially the Adam optimizer, has provided crucial theoretical insights into convergence properties and optimization landscapes. The 15 most recent publications reveal a strong trend toward mathematical analysis of deep learning, with approximately 70% of his work focusing on neural network optimization, convergence analysis, and theoretical foundations of machine learning algorithms. The remaining publications continue his earlier work on complex networks, stochastic processes, and branching structures, demonstrating how he has successfully connected his foundational work in probability with cutting-edge machine learning research. Professor Dereich actively supervises PhD students and maintains productive collaborations, particularly with Arnulf Jentzen and Sebastian Kassing. His research group at Münster has secured significant funding through the Mathematics Münster cluster, supporting multiple projects including T8: Random discrete structures and their limits, and T10: Deep learning and surrogate methods. His teaching portfolio includes advanced courses on Probability Theory, Stochastic Analysis, Markov Chains, and specialized seminars on Machine Learning and Financial Mathematics, reflecting his dual expertise in theoretical mathematics and applied data science.

• Stochastic Processes
• Machine Learning
• Deep Learning
• +۴ 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.

• Numerical Mathematics
• Scientific Computing
• Partial Differential Equations
• +۹ 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 .

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

Peter K. Friz is an Einstein Professor in Mathematics at TU-Berlin, affiliated with the Institute of Mathematics, and associated with the Weierstrass Institute for Applied Analysis and Stochastics. His research focuses on stochastic analysis, rough path theory, and mathematical finance, with particular emphasis on volatility modeling and applications to quantitative finance. He has held prestigious grants, including ERC Starting and Consolidator Grants, and coordinates the DFG research unit 'Rough paths, stochastic partial differential equations, and related topics.' Friz's work bridges theoretical stochastic analysis and practical financial applications, emphasizing rough path theory and its implications for differential equations and stochastic processes. His collaborations include organizing international conferences and courses on rough paths, with invited lectures at institutions like Cambridge, Paris, and Bonn. Supported by DFG, the European Research Council, and the Einstein Foundation, his research explores geometric aspects of pathwise analysis and stochastic volatility dynamics. He has advised numerous PhD students and maintains active roles in academic administration, including coordinating Berlin Mathematical School programs and teaching advanced topics in stochastic calculus. His contributions to rough path theory and stochastic finance are recognized through his academic leadership and influential publications, including co-authoring the seminal book Multidimensional Stochastic Processes as Rough Paths .

• Stochastic Analysis
• Rough Path Theory
• Mathematical Finance
• +۳ مورد دیگر

Prof. Francesca Biagini is a Full Professor of Applied Mathematics at the University of Munich (LMU), leading the Department of Mathematics within the Faculty of Mathematics, Computer Science, and Statistics. She holds additional roles as Vice President for International Affairs and Diversity at LMU since 2019, and served as President of the Bachelier Finance Society (2022–2023). Her academic career includes professorships at LMU (since 2009) and prior roles at the University of Bologna and Leibniz University Hannover. She specializes in financial and insurance mathematics, focusing on asset pricing, systemic risk, and model uncertainty. Education: PhD in Mathematical Finance (Scuola Normale Superiore, 2001), Laurea in Mathematics (University of Pisa, 1997). She has advised over 14 PhD students and 180+ master/bachelor students, collaborating with institutions like Allianz, MunichRe, and SwissRe. Research: Biagini’s work bridges financial and actuarial mathematics, including stochastic processes, systemic risk modeling, and insurance frameworks. Notable contributions include modeling asset bubbles, xVA calculations, and liquidity-based frameworks. She has published extensively in journals like *Finance and Stochastics* and *Mathematical Finance*. Awards and Activities: Recipient of the Prinzessin Therese von Bayern Preis (2019) and Zonta Clubpreis (2015). She organizes international conferences, serves on editorial boards (e.g., *Mathematical Finance*), and chairs the Munich Risk and Insurance Center. Her research is funded by grants from BayernLB and LMU Excellence programs.

• Financial Mathematics
• Insurance Mathematics
• Stochastic Processes
• +۳ 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.

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

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

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

Prof. Dr. Lubomir Banas is a full-time Professor at the Faculty of Mathematics , University of Bielefeld. His research focuses on numerical analysis of stochastic partial differential equations (SPDEs) , particularly in micromagnetism, phase field models, and stochastic games. He leads Subproject B03 in the SFB 1283 project 'Taming Uncertainty and Profiting from Randomness and Low Regularity in Analysis, Stochastics and Their Applications.' Research Interests: Numerical methods for SPDEs and singular-degenerate PDEs Adaptive finite element techniques and a posteriori estimates Phase field models (Cahn-Hilliard, obstacle potentials) Stochastic games with asymmetric information Computational micromagnetism and magnetostriction Self-organized criticality and nonlinear stochastic flows Recent work includes: 2025: Numerical approximation of biharmonic wave maps and stochastic games 2024: Sharp interface limits for stochastic Cahn-Hilliard equations 2023: Singular-degenerate SPDEs and a posteriori estimates 2022: Stochastic total variation flow and Hamilton-Jacobi-Bellman equations 2021: Nematic electrolytes and homogenization of two-phase flows He serves on examination boards for Bachelor's and Master's programs in Mathematics and Mathematical Physics, and supervises graduate students within the Bielefeld Graduate School in Theoretical Sciences . His publications (over 30) address convergence analysis, error estimation, and computational modeling in applied mathematics.

• Numerical Analysis
• Stochastic Partial Differential Equations
• Computational Physics
• +۱۲ مورد دیگر

Jakob Wagner is a Research Fellow at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Mathematics. He works within the Chair of Optimal Control under Prof. Boris Vexler and serves as an Invited Lecturer at Kutaisi International University in Georgia since 2022. His educational background includes a Master's degree in Mathematics from TUM (2019-2020, grade 1.0) and a Bachelor's degree in Mathematics from TUM (2014-2018, grade 1.2). Wagner's research focuses on Optimal Control of fluid dynamics equations, particularly the Navier-Stokes and Stokes systems. He specializes in Finite Element Methods and rigorous Error Estimates for discretizations of partial differential equations. His work addresses time-dependent problems, state constraints, and boundary control mechanisms in computational fluid dynamics, with significant contributions to stream-function formulations and pressure boundary conditions. Analysis of his publication record reveals a concentrated research trajectory in numerical analysis of incompressible flow control. His work consistently advances theoretical foundations for finite element discretizations, emphasizing fully discrete error analysis and pointwise constraints across Stokes and Navier-Stokes frameworks. Key innovations include novel approaches to blood flow modeling and ocean current reconstruction through coupled ODE systems. Wagner actively mentors students, supervising Master's theses by Alexandro Jedaidi (2025, ocean current reconstruction), Chenhong Lin (2025, Stokes equations), and Hendrik Bruhse (2023, parabolic problems), plus Noah An der Lan's Bachelor thesis (2025, Bayesian experiment design). His teaching portfolio spans Analysis, Optimization, and Numerical Methods courses at TUM and Kutaisi International University. He operates within TUM's Chair of Optimal Control research ecosystem, contributing to international collaborations through conference presentations at GAMM Annual Meetings and specialized symposia, while maintaining active involvement in computational mathematics projects including robotic swarm applications for radio imaging.

• Optimal Control
• Numerical Analysis
• Finite Element Methods
• +۲ 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 .

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

Prof. Miles Simon is a University Professor at Otto von Guericke University Magdeburg since 2011. His academic journey includes postdoctoral positions at the Max Planck Institute for Mathematics in the Sciences (Leipzig) and Humboldt University Berlin, followed by a long tenure at Albert Ludwigs University Freiburg until 2012. He earned his PhD from Melbourne University in 1997 and completed his habilitation at Freiburg in 2007. Simon specializes in geometric analysis, with a focus on Ricci flow, mean curvature flow, and geometric PDEs. Education: High school (Hawker College, Canberra), BSc(Honours) at Australian National University, PhD (Melbourne University), and habilitation (Freiburg). His research explores curvature bounds, singularities, geometric evolution equations, and compactness theorems in geometry. He leads the Project Leader SPP 2026 "Geometry at Infinity" . Teaching includes advanced courses like Analysis III, Differential Geometry, and Geometric Evolution Equations. Group members include Dr. Florian Litzinger and M.Sc. Priyamvada Vishwamitra. His recent work addresses Ricci flows of non-smooth metrics and curvature estimates in geometric flows. Simon’s publications analyze geometric flows in low dimensions, stability of spaces under Ricci flow, and curvature regularity. His projects emphasize nonlinear geometric diffusion equations and geometric evolution dynamics. Award: None explicitly listed. Grants: German Research Foundation (SPP 2026).

• Differential Geometry
• Geometric Analysis
• Partial Differential Equations
• +۳ مورد دیگر