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Kurt MehlhornView profile

Adjunct Professor

• Algorithms
• Data Structures
• Computational Geometry
• +7 more

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-PharabodView profile

Research Fellow

• Algebraic Geometry
• Topology
• Experimental Mathematics
• +5 more

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 BossmannView profile

Associate Professor

• Applied Signal Processing
• Inverse Problems
• Greedy Algorithms
• +8 more

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 EberleView profile

Researcher

• Approximation Algorithms
• Online Optimization
• Stochastic Scheduling
• +3 more

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.

Sarah MorellView profile

Researcher

• Combinatorial Optimization
• Network Flow Problems
• Approximation Algorithms
• +3 more

Sarah Morell is a Researcher at the University of Bremen, where she began her postdoctoral position in April 2025 under the mentorship of Prof. Dr. Nicole Megow. Previously, she completed her PhD at TU Berlin under Prof. Dr. Martin Skutella, focusing on Combinatorial Optimization. Her research emphasizes network flow problems, approximation algorithms, and diversity maximization in algorithms. She holds an M.Sc. in Mathematics from EPFL, Switzerland, with a minor in Theoretical Computer Science. Her master’s thesis, advised by Prof. Dr. Friedrich Eisenbrand, explored algorithms for diversity maximization. Key research contributions include work on the Submodular Santa Claus Problem (SODA 2025), unsplittable flows with arc constraints (Math. Program. 2022), and minimum-cost integer circulations in homology classes (SODA 2021). She has actively participated in workshops and summer schools on combinatorial optimization, theoretical computer science, and discrete mathematics. Professional activities include research stays at Maastricht University (2023) and TU Munich (2020), as well as presentations at institutions like MPI Saarbrücken and the University of Bremen. Her interdisciplinary work bridges discrete algorithms, optimization, and applications in fair resource allocation.

Thorsten RaaschView profile

Professor

• Numerical Mathematics
• Adaptive Discretization Methods
• Partial Differential Equations
• +7 more

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 GroßView profile

Researcher

• Computational Fluid Dynamics
• Numerical Analysis
• Finite Element Methods
• +3 more

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 FrieseckeView profile

Professor

• Electronic structure of atoms and molecules
• Calculus of variations
• Molecular dynamics
• +3 more

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.

Thomas HuckleView profile

Professor

• Numerical Linear Algebra
• Scientific Computing
• Parallel Computing
• +7 more

Prof. Thomas Huckle is a Professor of Scientific Computing at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. His research focuses on numerical linear algebra, parallel computing, and their applications in physics and computer science. Key interests include solving linear problems on parallel architectures, image processing, multigrid methods, preconditioning, and tensor-based high-dimensional problem approximation. Education: Studied mathematics and physics at the University of Würzburg (diploma in mathematics, 1985 PhD, 1991 habilitation). Professional History: DFG-funded research at Stanford University (1993–1994), appointed to TUM in 1995, and member of the Mathematics Department since 1997. Research Interests: Prof. Huckle’s work spans numerical methods for large-scale systems, including structured matrices, regularization techniques, and quantum computing applications. He develops algorithms for parallel computing environments and contributes to software tools like ELPA for eigenvalue problems. Grants and Labs: Engaged in projects such as the ELPA-AEO eigensolver and ESSEX-II initiatives. Active in the SCCS (Scientific Computing and Computational Science) group at TUM, focusing on high-performance computing and numerical methods.

Oliver JungeView profile

Associate Professor

• Numerical Mathematics
• Scientific Computing
• Dynamical Systems
• +4 more

Oliver Junge is an Associate Professor of Numerics of Complex Systems at the TUM School of Computation, Information and Technology, Technische Universität München. His research focuses on developing novel numerical methods for dynamical systems, with applications in molecular dynamics, astrodynamics, systems theory, and image processing. He holds a doctorate from the University of Paderborn (1999) and has conducted research at institutions like Georgia Tech and Caltech. Notable awards include the TopMath Supervisory Award (2020) and the Research Prize from the University of Paderborn (2004). Educational Background: He studied mathematics and computer science at the Universities of Darmstadt, Bordeaux, Hamburg, and Bayreuth before completing his PhD in Paderborn. His academic career includes roles as a junior professor in Paderborn and associate professor at TUM since 2005. Research Interests: Junge specializes in numerical mathematics and scientific computing, emphasizing the analysis and implementation of numerical methods for complex systems. His work bridges theoretical rigor and practical applications, particularly in computational fluid dynamics, optimal control, and topological data analysis. Awards: His accolades include the Dilthey Prize (1998) and recognition for his contributions to computational methods in dynamical systems. Grants & Advising: While specific grants or advisee names are not listed, his publications indicate extensive collaborative research and mentorship in computational mathematics and systems theory. Labs/Teams: Collaborates with interdisciplinary teams at TUM and internationally, focusing on numerical methods and their applications in engineering and natural sciences.

Christoph KnochenhauerView profile

Professor

• Financial Mathematics
• Stochastic Control Theory
• Machine Learning in Finance
• +3 more

Prof. Christoph Knochenhauer is a Professor of Financial Mathematics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. His academic career includes a PhD in Mathematics (2015) from Technical University of Kaiserslautern and Dublin City University, followed by a postdoctoral position at the University of Trier. He served as Junior Professor for Stochastics and Quantitative Financial Mathematics at TU Berlin (2019) before joining TUM in 2023. His research focuses on financial mathematical applications of stochastic control theory, machine learning methods in finance, and probabilistic analysis of partial differential equations. Recent work explores optimal investment strategies for retail and institutional investors, dynamic decision-making under uncertainty, and numerical methods for stochastic systems. Key contributions include explicit solutions for optimal investment problems and convergence analyses of deep learning algorithms for PDEs. Prof. Knochenhauer has been recognized with the Joseph A. Schumpeter Prize (2017) and the Gauss Young Researcher Award (2015). His publications span topics like mean field games, fractional Brownian motion models, and systemic risk valuation. While no advising records or grants are explicitly listed, his work underscores advancements in stochastic finance and computational methods.

Harald RäckeView profile

Professor

• Algorithms
• Theoretical Computer Science
• Approximation Algorithms
• +5 more

Prof. Dr. Harald Räcke is a Professor at the Technische Universität München (TUM) , affiliated with the TUM School of Computation, Information and Technology in the Effiziente Algorithmen department. His research focuses on the design and analysis of algorithms, particularly approximation algorithms, online algorithms, graph partitioning, scheduling, routing in parallel systems, and metric embeddings. He holds a PhD from the University of Paderborn and has held academic positions at institutions including Carnegie Mellon University (USA), Toyota Technological Institute (USA), and the University of Warwick (UK) before joining TUM in 2011. His work has been recognized with prestigious awards, including the Best Paper Awards at STOC 2008 and FOCS 2002. Key research trends in his publications emphasize theoretical computer science and algorithm design, with a focus on optimizing network-related problems such as congestion minimization, balanced partitioning, and oblivious routing. His recent work explores dynamic graph algorithms and sparsification techniques, reflecting advancements in handling large-scale network challenges. Education: PhD in Algorithms and Complexity (2003), University of Paderborn Studies in Computer Science at University of Paderborn Professional Milestones: Postdoc: Carnegie Mellon University (Pittsburgh, USA) and Toyota Technological Institute (Chicago, USA) Assistant Professor at University of Warwick (UK) Joined TUM in 2011 His scientific awards highlight contributions to theoretical computer science: Best Paper Award, 40th ACM Symposium on Theory of Computing (2008) Best Paper Award, 43rd IEEE Symposium on Foundations of Computer Science (2002)

Elisabeth UllmannView profile

Associate Professor

• Uncertainty Quantification
• Scientific Computing
• Numerical Analysis
• +3 more

Prof. Elisabeth Ullmann is an Associate Professor for Scientific Computing and Uncertainty Quantification at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information, and Technology and the Department of Mathematics. She holds a PhD from TU Bergakademie Freiberg (2008) and has held academic roles including postdoctoral positions at the University of Bath, University of Hamburg, and University of Maryland. Her research focuses on developing efficient algorithms for uncertainty quantification in partial differential equations with random coefficients, Bayesian inverse problems, and rare event simulation. She is an Associate Editor for the SIAM Journal on Scientific Computing and SIAM/ASA Journal on Uncertainty Quantification , and teaches courses on numerical methods for uncertainty quantification and partial differential equations. Her work emphasizes multilevel Monte Carlo methods, stochastic Galerkin discretizations, and probabilistic numerical methods. Notable contributions include error analysis for rare event probabilities and multilevel estimators for high-dimensional problems. She collaborates internationally and maintains an active research group in Scientific Computing & Uncertainty Quantification at TUM.

Antonia Wachter-ZehView profile

Associate Professor

• Coding Theory
• Post-Quantum Cryptography
• Information Theory
• +5 more

Antonia Wachter-Zeh is an Associate Professor at the Technical University of Munich (TUM) in the School of Computation, Information and Technology. She heads the Coding and Cryptography (COD) group and holds an ERC Starting Grant and the DFG Heinz Maier-Leibnitz Prize. Her research focuses on coding theory, post-quantum cryptography, and their applications in storage, communication, and machine learning. Education: M.Sc. in Electrical Engineering/Communications Engineering, Ulm University (2009) PhD in Electrical Engineering (2013), jointly from Ulm University and Université de Rennes 1 Research Interests: Coding Theory: Rank-metric codes, insertion/deletion correcting codes, DNA storage Post-Quantum Cryptography: Code-based and lattice-based schemes Coded Computing: Secure federated learning, privacy-preserving distributed algorithms Recent Publications: Focus on coded distributed computing, DNA-based storage, and cryptographic protocols. Key contributions include FedGT for malicious client detection and LowMS for rank-metric code-based KEMs. Awards: Johann Philipp Reis Award (2023) ERC Starting Grant (2019-2024) DFG Heinz Maier-Leibnitz Prize (2018) Grants & Teams: Leads projects like PQ-Prime (encrypted computing), DiDaX (DNA data storage), and PQ-CROWD (cryptography with skew codes). Collaborates internationally with institutions like ICL and DLR. Labs/Teams: Coding and Cryptography (COD) group at TUM, active in organizing workshops like MWCC and ITW.

Simone WarzelView profile

Professor

• Mathematical Physics
• Quantum Systems
• Disordered Quantum Systems
• +5 more

Prof. Dr. Simone Warzel is a Professor of Mathematical Quantum Physics at the Technical University of Munich (TUM), leading the Chair of Mathematical Quantum Physics. She holds academic positions in the TUM School of Computation, Information, and Technology and the Department of Mathematics. Her research focuses on mathematical physics, particularly quantum systems, including disordered systems, many-body quantum systems, and spectral theory. Education: Completed her PhD in Erlangen, followed by postdocs at Princeton University and other institutions. Joined TUM in 2008. Research Interests: Interplay of randomness and quantum effects (e.g., quantum glasses), correlated many-body systems (fractional quantum Hall, quantum spin systems), and techniques spanning analysis, probability (random operators), and statistical mechanics. Key Awards: John von Neumann Fellowship (2013), Young Scientist Award (2009), Alfred P. Sloan Fellowship (2007). Grants: Collaborative DFG Grants (2019–), DFG Grants (2014–2018), NSF Grants (2003–2005). Labs/Teams: Active in Munich Center for Quantum Science and Technology (MCQST), International Association of Mathematical Physics (IAMP).