جستجوی استاد راهنمای دانشگاهی

Tobias Breitenمشاهده پروفایل

استاد

• Model Reduction
• Control Theory
• Optimal Control
• +۳ مورد دیگر

Prof. Dr. Tobias Breiten is a Professor of Mathematics at Technische Universität Berlin, affiliated with Faculty II - Mathematics and Natural Sciences and the Institute of Mathematics. His research focuses on model reduction, control theory, and numerical methods for complex systems. Education: Diploma in Technomathematik (2009, TU Kaiserslautern), PhD in Mathematics (2013, Otto-von-Guericke University Magdeburg), Habilitation (2018, University of Graz). Roles: Associate Professor (2019-2020, University of Graz), Junior Professor (2020-2022, TU Berlin), Full Professor since 2022. Research interests include structure-preserving model reduction, nonlinear observer design, and optimal control of port-Hamiltonian systems. He leads projects on topics like thermoacoustic spectra and nonlinear control perspectives. Notable publications include works on H2 optimal rational approximation, balanced truncation methods, and Mortensen observer theory. His work often bridges numerical analysis and systems theory.

• Graph Theory
• Discrete Mathematics
• Combinatorics
• +۳ مورد دیگر

Stefan Felsner is a Professor at the Institut für Mathematik of Technische Universität Berlin , specializing in Algorithmic and Discrete Mathematics . His work focuses on Graph Theory, Combinatorics, Discrete Geometry, and Order Theory. He leads the Discrete Mathematics Group and has been involved in DFG-funded projects on geometric representations of graphs, arrangements, and order geometry. Education & Career: Studied Mathematics at the University of Vienna. Worked as a research assistant at TU Berlin (1988–1993), then at Freie Universität Berlin (1993–2003) before becoming a Professor at TU Berlin in 2003. He earned his Habilitation in 1997 and has held visiting positions in the U.S. and Canada. Research Interests: Graph drawing, geometric graphs, arrangements of lines/pseudolines, order dimension, and combinatorial structures. He authored the book Geometric Graphs and Arrangements and contributes to editorial boards of ORDER and DMTCS . Recent Work: Focuses on contact representations of planar graphs, pseudocircle arrangements, and flip graph connectivity. His recent articles address convex drawings, Hamiltonian cycles in convex graphs, and counting pseudoline arrangements. He actively participates in the Mittagsseminar for discrete mathematics discussions.

• Optimization
• Control Theory
• Partial Differential Equations (PDEs)
• +۳ مورد دیگر

Prof. Daniel Walter is a Junior Professor at the Institute of Mathematics, Humboldt University of Berlin, within the Faculty of Mathematics and Natural Sciences. His research focuses on non-smooth optimization, optimal control, and numerical analysis of partial differential equations (PDEs). He specializes in developing advanced methods for sensor placement in inverse problems and stabilizing control systems using computational techniques. His work integrates theoretical analysis with practical applications, addressing challenges in feedback stabilization, convergence of optimization algorithms, and data-driven approaches. Notable contributions include studies on extremal points in optimization norms, linear convergence rates of conditional gradient methods, and semiglobal stabilization using neural networks. Key Research Areas: Non-smooth optimization and sparse methods PDE-constrained optimization and control Inverse problems and sensor placement strategies Numerical methods for parabolic and elliptic equations Recent publications emphasize algorithmic advancements in optimization, with a focus on acceleration and convergence guarantees. His interdisciplinary approach bridges mathematical theory with engineering applications, particularly in stabilization and parameter estimation. Advising & Grants: While no specific grants or advisees are listed, his research indicates active involvement in training through cutting-edge projects in optimization and control.

Gunter Bolchمشاهده پروفایل

استاد

• Performance Modelling
• Process Control
• Operating Systems
• +۶ مورد دیگر

Dr. Gunter Bolch was a Professor and former head of the Performance Modelling and Process Control research group at the Department of Computer Science 4 (Distributed Systems and Operating Systems) of Friedrich-Alexander-Universität Erlangen-Nürnberg. He held positions such as Akademischer Direktor and was a Visiting Professor at institutions like the Catholic University of Rio de Janeiro. His work focused on performance modeling using queueing networks, stochastic Petri nets, and Markov chains, with applications in telecommunications and operating systems. Education: Studied Telecommunication at Technical Universities of Karlsruhe and Berlin (Ph.D. 1973). Retired in 2006 but remained active in research and academia until his passing in 2008. Research interests spanned performance evaluation, process control, and analytical methods for computer systems. He authored/co-authored 7 books and over 130 publications, including influential works on queueing networks and MOSEL modeling language. Key contributions include the PEPSY and MOSEL tools for performance analysis. Collaborated internationally on conferences like ASMTA and ESS. His work integrated theoretical models with practical applications, impacting both academia and industry.

Anita Schöbelمشاهده پروفایل

استاد

• Operations Research
• Mathematical Optimization
• Robust Optimization
• +۱۲ مورد دیگر

Anita Schöbel is a Professor in the Department of Mathematics at the Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU) and serves as the Director of the Fraunhofer Institute for Industrial and Financial Mathematics (ITWM) in Kaiserslautern. She is a leading figure in operations research and mathematical optimization, with a strong focus on public transportation systems, robust optimization, and multi-objective decision-making. Her dual roles bridge academic research and industrial application, particularly in logistics, healthcare, and energy systems. Her research interests include: Robust and integer optimization Public transport planning (timetabling, line planning, delay management) Facility and hub location problems Multi-objective optimization under uncertainty Algorithmic methods in transportation networks The analysis of her recent publications reveals a consistent focus on integrating robustness into transportation planning, using machine learning to enhance schedule reliability, and advancing theoretical frameworks for multi-objective optimization. Her work often combines mathematical rigor with real-world applicability, especially in public transit and pandemic modeling. She has contributed significantly to the development of optimization models for public health during the COVID-19 crisis. Her scientific awards include leadership roles in major professional societies: President of the European Association of Operational Research Societies (EURO), 2022–2023 President of the German Society for Operations Research (GOR), 2019–2020 Anita Schöbel has been actively involved in grant-funded research and collaborative projects, including the DFG Research Group FOR2083 on integrated transportation planning, the EU project EASIER, and the BMBF project SynphOnie. She is currently co-spokesperson of the DFG Graduate College 2982 on 'Mathematics of Interdisciplinary Multiobjective Optimization'. She also serves on advisory boards such as the steering committee of HLRS and the Fraunhofer Strategic Research Field on Next Generation Computing. She leads the research group 'Optimization' at RPTU and is associated with initiatives like LinTim (software for transport planning), QuanTUK (quantum computing applications), and GRK 2982. Her leadership extends to academic governance, including membership in the RPTU University Council and the Departmental Council of Mathematics.

Edith Cohenمشاهده پروفایل

استاد مهمان

• Algorithms Design
• Data Mining
• Big Data Analytics
• +۱۰ مورد دیگر

Edith Cohen is a Research Scientist at Google and a Visiting Full Professor in the School of Computer Science at Tel Aviv University, where she contributes to academic research and teaching. Her career spans leading industrial research labs, including AT&T Bell Labs, Microsoft Research, and Google Research. Undergraduate and M.Sc.: Tel Aviv University (1985, 1986) Ph.D.: Stanford University, Computer Science (1991) Her research centers on scalable algorithms for massive data, with key contributions in sketching (e.g., MinHash, HyperLogLog), sampling techniques, graph mining (centrality, influence, similarity), data streams, and differential privacy. She designs principled algorithmic solutions that balance accuracy, speed, and storage in large-scale systems. The recent articles highlight a strong focus on robustness and privacy in streaming and learning systems, particularly under adversarial or adaptive settings. Her work bridges theoretical rigor with practical impact, applying advanced statistical and algorithmic methods to real-world data challenges. ACM Fellow (2017) IEEE Communications Society William R. Bennett Prize (2007) ISF and NSF Research Grants She has advised several students and interns at AT&T and Google, and has served on program committees and editorial boards of top computer science venues. Her work has led to numerous patents in data summarization, network routing, and query processing. She actively collaborates with researchers across institutions and continues to publish in premier conferences such as STOC, KDD, ICML, and PODS.

• Theoretical Computer Science
• Algorithms
• Mathematical Optimization
• +۷ مورد دیگر

Parinya Chalermsook is a Professor of Algorithms at the University of Sheffield, affiliated with the Foundations of Computation Group in the Department of Computer Science, Faculty of Engineering. He also holds a visiting associate professor position at Aalto University, Finland. His research focuses on theoretical computer science, particularly the interplay between algorithms and mathematical optimization, with strong interests in extremal combinatorics and their applications in TCS. His work spans parameterized complexity, approximation algorithms, computational complexity, and discrete optimization. The recent articles and talks highlight a strong trend in fine-grained and parameterized computational geometry, graph algorithms, and the synergy between continuous and discrete optimization. His research is deeply theoretical, often bridging mathematical disciplines with algorithmic challenges. Simons-Berkeley Research Fellowship (2017) ERC Starting Grant (~1.4M Euro, 2017–2024) Academy of Finland Research Fellowship (~900K EUR, 2017–2022) He has supervised numerous PhD students and hosted postdoctoral fellows, fostering a vibrant research group. His research has been supported by major grants from the European Research Council and the Academy of Finland. He actively contributes to the academic community through program committees (e.g., STOC, SODA, ICALP) and organizing workshops at Dagstuhl and Hausdorff Institute. He is a key member of the Foundations of Computation Group at Sheffield and has previously contributed to the TCS communities at Aalto University and Max Planck Institute for Informatics.

Adam Lipowskiمشاهده پروفایل

استاد

• Reinforcement Learning
• Complex Networks
• Statistical Mechanics
• +۴ مورد دیگر

Adam Lipowski is a Professor at the Faculty of Physics, Adam Mickiewicz University in Poznań, Poland, specializing in the intersection of statistical physics and complex systems theory. His research bridges theoretical physics with computational modeling to address emergent phenomena in social and biological contexts. His primary research domains include: Reinforcement Learning in multi-agent decision-making Statistical mechanics on complex networks Opinion dynamics and phase transitions Ising and voter model generalizations Nonequilibrium thermodynamics of social systems Emergence of collective behavior Recent work analyzes directed random graphs through mean-field approximations, revealing novel phase transition behaviors in combined Ising-voter frameworks. His PLOS ONE publications demonstrate how reinforcement learning resolves linguistic ambiguities in signaling games, while Physical Review E contributions establish fluctuation relations for nonequilibrium voter models with surface tension effects. The recurring theme across his Entropy and Physical Review publications is the adaptation of classical statistical mechanics tools to unconventional domains like social dynamics and language evolution. Professor Lipowski maintains active international collaborations, particularly with António Luís Ferreira (University of Coimbra), Dorota Lipowska, and Aleksandra Napierała-Batygolska, reflected in his 106+ publications and 39 co-authors. His research program consistently explores how microscopic interaction rules generate macroscopic patterns in complex adaptive systems.

Kurt Mehlhornمشاهده پروفایل

استاد مدعو

• Algorithms
• Data Structures
• Computational Geometry
• +۷ مورد دیگر

Kurt Mehlhorn is a renowned Adjunct Professor at the Max Planck Institute for Informatics and holds an honorary professorship at Saarland University. He leads the 'Algorithms and Complexity' department at MPI, where he has mentored numerous PhD students and postdocs, many of whom now hold academic positions globally. Education: Not explicitly detailed in the text, but his career has spanned foundational contributions to computer science. Affiliations: Max Planck Institute for Informatics (since its founding), Universität des Saarlandes (honorary), and multiple academic societies. His research focuses on algorithms, data structures, computational geometry, parallel computing, and algorithmic economics. He pioneered projects like LEDA, a foundational library for efficient algorithms and data structures, influencing tools like CGAL and STXXL. His work bridges algorithm correctness and efficiency, impacting fields from genome sequencing to VLSI design. Recent articles emphasize fair division (EFX allocations), market equilibria, and algorithmic game theory, reflecting his enduring influence on theoretical and applied computer science. Awards: Erasmus Medal (2014), Leibniz Prize, Konrad-Zuse Medal, and honorary doctorates from Magdeburg, Waterloo, and Aarhus. Labs/Teams: Leads MPI's Algorithms and Complexity group; collaborates with institutions like DFKI and the Center for IT-Security, Privacy, and Accountability on Saarland's interdisciplinary campus.

Eric Pichon-Pharabodمشاهده پروفایل

پژوهشگر ارشد

• Algebraic Geometry
• Topology
• Experimental Mathematics
• +۵ مورد دیگر

Eric Pichon-Pharabod is a postdoctoral researcher at the Numerical Algebraic Geometry group of the Max Planck Institute for Mathematics in the Sciences in Leipzig. He completed his PhD at Université Paris-Saclay in the MATHEXP team (previously SpecFun) at Inria Saclay under the supervision of Pierre Lairez and Pierre Vanhove, defending his thesis on September 27, 2024. His educational background includes doctoral studies at Université Paris-Saclay, where he developed expertise in computational algebraic geometry and period computations. His PhD work focused on efficient algorithms for numerical approximations of periods of algebraic varieties, building on previous research in the MATHEXP team at Inria Saclay. Eric's research spans algebraic geometry, topology, and computational mathematics with emphasis on numerical methods. He specializes in developing practical algorithms for computing periods and homology of complex algebraic varieties, including hypersurfaces and elliptic surfaces. His work bridges theoretical mathematics with concrete computational implementations, primarily using SageMath. He has made significant contributions to understanding Galois groups of symmetric families, period computations for Calabi-Yau varieties, and applications to mathematical physics. His publication record reveals a strong focus on computational techniques in algebraic geometry, with recent work exploring connections between period computations, monodromy representations, and physical applications. His algorithms have enabled computations that were previously infeasible, such as period calculations for smooth quartic surfaces on standard laptops. Eric actively contributes to the mathematical community through the lefschetz-family SageMath package and as co-organizer of the Universe+ online seminar. His interdisciplinary approach connects pure mathematics with physics and computer science, demonstrating how numerical methods can illuminate deep theoretical questions in algebraic geometry.

Florian Bossmannمشاهده پروفایل

دانشیار

• Applied Signal Processing
• Inverse Problems
• Greedy Algorithms
• +۸ مورد دیگر

Florian Bossmann is an Associate Professor and Doctoral Supervisor at the School of Mathematics, Harbin Institute of Technology. His research focuses on applied signal processing with an emphasis on algorithm design and applications in inverse problems, sparsity, and compressed sensing. His work spans seismic exploration, ptychography, and video processing. Education: PhD (Mathematics, University of Göttingen, 2010-2013), Diploma in Mathematics (University of Duisburg-Essen, 2005-2009). Research interests include greedy methods, structured sparsity, and data denoising. His recent articles address topics like object reconstruction via K-approximation graphs and phase retrieval in ptychographic imaging. No scientific awards explicitly stated. Active in mentoring doctoral students and collaborations in applied mathematics and engineering. Labs/Teams: Engaged in interdisciplinary research groups focused on signal processing and mathematical modeling.

Franziska Eberleمشاهده پروفایل

پژوهشگر

• Approximation Algorithms
• Online Optimization
• Stochastic Scheduling
• +۳ مورد دیگر

Dr. Franziska Eberle is the Head of the MATH+ Junior Research Group in the Institute of Mathematics at Technische Universität Berlin. She leads research in approximation algorithms, focusing on uncertain environments such as online and stochastic models. Her work addresses scheduling, resource allocation, and combinatorial optimization under uncertainty. Education: PhD in Computer Science, Universität Bremen (2020) M.Sc. in Mathematics for Operations Research, TU München (2016) B.Sc. in Mathematics, TU München (2014) Research Interests: Franziska’s research spans approximation algorithms for stochastic and online scheduling, robust optimization, and algorithmic frameworks for commitment models. She has contributed to load balancing, matroid optimization, and learning-augmented algorithms. Her work bridges theoretical foundations and practical applications in scheduling and resource management. Recent Contributions: Developed algorithms for configuration balancing under stochastic requests (Mathematical Programming, 2024) Optimal online throughput maximization for unrelated machines (ACM Transactions on Algorithms, 2023) Robust scheduling frameworks for speed-uncertain machines (Mathematical Programming, 2023) Teaching: Franziska has taught courses on approximation algorithms, discrete optimization, and practical optimization modeling. Recent offerings include advanced topics in online optimization (Summer 2024). Labs/Teams: Her research group includes PhD student Sebastian Bruchhold, focusing on cutting-edge problems in optimization under uncertainty.

Thorsten Raaschمشاهده پروفایل

استاد

• Numerical Mathematics
• Adaptive Discretization Methods
• Partial Differential Equations
• +۷ مورد دیگر

Thorsten Raasch is a Professor of Numerical Mathematics at the Institute of Mathematics, Johannes Gutenberg University Mainz, Germany. He is affiliated with the Department of Physics, Mathematics, and Computer Science, and leads research in numerical analysis, particularly adaptive methods for PDEs and inverse problems. Research Interests: His work focuses on adaptive discretization methods for partial differential equations and inverse problems, non-smooth numerical optimization , wavelet systems , multilevel frames , and GPGPU computing . These interests are deeply rooted in applied and computational mathematics, with applications in scientific computing and engineering. The most recent articles show a strong trend in developing adaptive wavelet-based numerical schemes for solving inverse and stochastic PDEs, with a particular emphasis on sparsity, convergence analysis, and preconditioning. His publications span high-impact journals in numerical analysis and inverse problems, indicating sustained scholarly activity. Teaching: He has taught a variety of courses including Fundamentals of Numerics, Convex Optimization, Numerical PDEs, Spectral Methods, and advanced seminars on topics like semi-smooth Newton methods and reduced basis methods. Thorsten Raasch earned his doctorate in 2007 from Philipps University of Marburg and has held academic positions at JGU Mainz, the University of Siegen, and the University of Rostock. He was appointed W2 Professor at JGU Mainz in 2015 and previously held a junior professorship there. No scientific awards are mentioned in the provided texts. Advising and Grants: While no students or grants are explicitly listed, his role as a professor and active researcher suggests involvement in supervising graduate students and participating in research projects, possibly within the 'Mathematics of Computation' research network. Labs and Teams: He is part of the Numerical Mathematics Working Group at JGU Mainz and contributes to the 'Mathematics of Computation' research network, indicating collaboration within a structured research environment.

Sven Großمشاهده پروفایل

پژوهشگر

• Computational Fluid Dynamics
• Numerical Analysis
• Finite Element Methods
• +۳ مورد دیگر

Dr. Sven Groß is a researcher at the Institute for Geometry and Practical Mathematics at RWTH Aachen University, Germany, with an office in Hauptgebäude 237. His work focuses on advanced numerical methods for fluid dynamics, particularly incompressible two-phase flows, and he is a core developer of the DROPS software package for parallel flow simulations. He maintains active research collaborations across computational mathematics and chemical engineering disciplines. His educational background includes a diploma thesis (2002) and doctoral thesis (2008), both completed at RWTH Aachen University. The doctoral work, titled "Numerical methods for three-dimensional incompressible two-phase flow problems," earned him the prestigious Borchers-Plakette award. Groß's research centers on Computational Fluid Dynamics with emphasis on Finite Element Methods for interface problems. His expertise spans adaptive 3D FE techniques, level set methods, XFEM implementations, and parallelization strategies for two-phase flow systems. Current investigations address preconditioning for unfitted finite element methods, mass transfer in falling films, and high-performance computing approaches for gas absorption processes. His methodologies bridge theoretical numerical analysis with industrial applications in chemical process engineering. Analysis of his recent publications reveals a strong trajectory toward robust preconditioning techniques for interface problems and high-fidelity simulations of reactive multiphase systems. The work increasingly integrates high-throughput computing with traditional numerical methods, demonstrating growing emphasis on computational efficiency for complex industrial-scale problems while maintaining mathematical rigor in error analysis. His scientific recognition includes: Borchers-Plakette (RWTH Aachen, 2009) for doctoral studies Friedrich-Wilhelm-Preis (RWTH Aachen, 2003) for diploma thesis Springorum-Denkmünze (RWTH Aachen, 2003) for diploma Groß has secured significant research funding through Collaborative Research Center SFB 540, leading Project C7 on numerical methods for wavy falling film simulations and contributing to former Project C6 on parameter estimation. He actively mentors students through research collaborations and has supervised numerous diploma/PhD projects, with co-authors including Kirchhart, Ludescher, and Jankuhn appearing consistently in his publication record. As a principal investigator for the DROPS project, he leads a cross-disciplinary team developing parallel adaptive multigrid techniques for incompressible flow simulations. The project maintains strong industry connections with chemical engineering groups focused on falling film reactors and mass transfer optimization, with recent extensions into high-performance computing architectures for industrial-scale simulations.

Gero Frieseckeمشاهده پروفایل

استاد

• Electronic structure of atoms and molecules
• Calculus of variations
• Molecular dynamics
• +۳ مورد دیگر

Prof. Gero Friesecke is a Professor of Mathematics at the Technical University of Munich (TUM), leading the Chair of Analysis within the Department of Mathematics. He is affiliated with the TUM School of Computation, Information and Technology. His research focuses on electronic structure theory, calculus of variations, molecular dynamics, multiscale methods, and optimal transport. He has held academic positions at institutions like the University of Oxford and the University of Warwick before joining TUM in 2004. Education includes a PhD from Heriot-Watt University (1993) and postdoctoral work at Carnegie Mellon University, Universität Freiburg, and ETH Zurich. Notable awards include the Whitehead Prize (1999) and the Oberwolfach Prize (1995). His work spans theoretical and applied mathematics, with contributions to quantum chemistry, materials science, and optimal transport algorithms. He co-edits journals like the Archive for Rational Mechanics and Analysis and serves on scientific advisory boards, including the Max Planck Institute for Mathematics in the Sciences. Recent research emphasizes optimal transport applications in data science and quantum chemistry, with publications on topics like Wasserstein barycenters and self-assembly processes. His leadership roles include Dean of the Faculty of Mathematics at TUM (2012-2015) and co-founding the GAMM activity group on molecular systems modeling.