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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.

• Gravitational Wave Astronomy
• Laser Interferometry
• Quantum Optics
• +5 more

Benno Willke is a Senior Scientist and group leader at the Albert Einstein Institute (Max Planck Institute for Gravitational Physics) in Hannover, affiliated with Leibniz Universität Hannover. He leads the Laser Development and Advanced LIGO group, focusing on laser interferometry and gravitational wave astronomy. His work is integral to the LIGO Scientific Collaboration and the GEO600 project. Institution: Leibniz Universität Hannover Research Institute: Max Planck Institute for Gravitational Physics (AEI) Group: Laser Development and Advanced LIGO Location: Hannover, Germany Willke earned his PhD in 1992 and completed his Habilitation in 2009, both from Leibniz Universität Hannover. He has held academic and research positions continuously since 1993, including Junior Scientist (1993–1997), Senior Scientist (since 1998), and Feodor-Lynen Fellow at Stanford University (1997–1998). His academic title of Apl. Prof. reflects his status as an extraordinary professor. His primary research interests include laser interferometry , gravitational wave detection , squeezed light , and laser stabilization . He has made significant contributions to the development and commissioning of high-precision laser systems for gravitational wave detectors like GEO600 and Advanced LIGO. His work enables unprecedented sensitivity in detecting spacetime ripples from cosmic events such as black hole mergers. His recent publications (2022–2025) span major journals including Physical Review D , Classical and Quantum Gravity , and Optics Express . The research trends emphasize detector characterization , quantum noise reduction via squeezed light , laser cavity design (e.g., for ALPS II), and gravitational wave data analysis . These works reflect a strong focus on cutting-edge instrumentation and its application in multi-messenger astrophysics. Willke has received numerous prestigious awards, recognizing his pivotal role in gravitational wave astronomy: Berthold Leibinger Innovation Prize (2023) Bruno Rossi Prize (2017) Group Achievement Award, Royal Astronomical Society (2017) Princess of Asturias Award (2017) Gruber Prize (2016) Special Breakthrough Prize (2016) He has held leadership roles in major scientific collaborations, including the LIGO Science Collaboration (since 1997), the GEO Executive Committee (since 2000), and advisory committees for EGO and LIGO. He has delivered lectures on laser interferometry and experimental physics. Although no formal students are listed, his role as group leader implies mentorship of junior researchers and PhD candidates. He has contributed to large-scale funded projects such as Advanced LIGO and GEO600, involving substantial grants from international agencies. Willke leads the Laser group at the Albert Einstein Institute, which is central to developing and maintaining the laser systems for gravitational wave detectors. The team works on high-power lasers, frequency stabilization, and quantum-enhanced interferometry using squeezed light. They also contribute to next-generation experiments like ALPS II, which searches for axion-like particles using light-shining-through-walls techniques.

Dániel MarxView profile

Professor

• Parameterized Algorithms
• Computational Complexity
• Algorithmic Graph Theory
• +7 more

Dániel Marx is a tenured Professor at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. He leads research in the area of Algorithmic Foundations and Cryptography, with a focus on parameterized algorithms and computational complexity. He has held previous positions at the Max Planck Institute for Informatics and the Institute for Computer Science and Control of the Hungarian Academy of Sciences (MTA SZTAKI). His work is highly theoretical, aiming to understand the precise complexity of algorithmic problems, especially on bounded-treewidth graphs and in parameterized settings. PhD: Budapest University of Technology and Economics, 2005 Postdoc: Tel Aviv University, Humboldt University Berlin Senior Research Fellow: MTA SZTAKI, 2012–2019 Senior Researcher: Max Planck Institute for Informatics, 2019–2020 Faculty: CISPA Helmholtz Center, 2020–present His research interests lie at the intersection of theoretical computer science and discrete mathematics. He is particularly known for his work on parameterized algorithms , fine-grained complexity , and lower bounds . His group investigates algorithmic graph theory problems, including domination, independence, homomorphism, and clustering, especially under structural constraints like bounded treewidth. He has pioneered techniques in dynamic programming, kernelization, and hardness proofs based on the Exponential Time Hypothesis. The recent publications of Dániel Marx reveal a consistent focus on establishing tight complexity bounds for fundamental algorithmic problems. His work spans from exact and parameterized algorithms to approximation and counting problems. A recurring theme is the analysis of problems on bounded-treewidth graphs, where he explores the boundary between tractable and intractable cases. He also contributes to network design (e.g., Steiner problems), clustering, and subgraph counting, often providing complete classifications of complexity based on parameters. European Research Council Starting Grant European Research Council Consolidator Grant Humboldt Research Fellowship for Experienced Researchers Dániel Marx has advised numerous researchers and collaborated widely across institutions. His research is supported by prestigious grants, including ERC grants that funded his group at MTA SZTAKI. He actively leads a research group at CISPA focused on parameterized algorithms and complexity. While specific PhD students are not listed in the provided text, his extensive co-authorship network indicates a strong mentoring and collaborative presence. His work has significant implications for the foundations of computer science and algorithm design. He leads the research group on Parameterized Algorithms and Complexity at CISPA, continuing his long-standing focus on theoretical algorithm design and analysis. His team works on foundational problems in graph algorithms and complexity theory, aiming to develop new algorithmic techniques and understand the limits of efficient computation.

Jan HamaekersView profile

Researcher

• Numerical Algorithms
• High-Dimensional Problems
• Sparse Grids
• +7 more

Dr. Jan Hamaekers is a researcher at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) in Bonn, Germany. He is affiliated with the development of advanced numerical methods for high-dimensional problems in computational science, particularly in quantum mechanics and molecular dynamics. His work is centered on efficient algorithms such as HCFFT, ESPACK, and TREMOLO, and he collaborates on major research projects funded by DFG and EU programs. Research Interests: Development of hierarchical methods for high-dimensional problems Sparse grid and fast Fourier transform techniques Quantum mechanical and molecular mechanical (QM/MM) methods Numerical algorithms for the electronic Schrödinger equation Parallel software for nanoscale dynamics simulations His recent publications reflect a strong trend in computational materials science, multiscale modeling, and machine learning applications in chemistry. He has contributed to software packages like TREMOLO and ATK-ForceField, and his work bridges applied mathematics, physics, and engineering. He teaches practical labs in numerical simulation, indicating an active academic engagement. Scientific Projects: Molecular Dynamics Simulations for Material Science Applications CODICE - Computationally Driven design of Innovative Cement-based materials (EU FP7) Development of efficient algorithms for nanoscale dynamics simulations Multiscale QM/MM simulations of nanotubes and composites (DFG) Dr. Hamaekers has advised or collaborated with multiple researchers, as seen in his extensive co-authorship. He has not received any explicitly mentioned awards in the provided text. He is involved in software development and algorithmic innovation, with a focus on scalability and efficiency in scientific computing.

Andrei KulikovskyView profile

Researcher

• Electrochemical Engineering
• Computational Modeling
• Energy Systems
• +3 more

Dr. Andrei Kulikovsky is a researcher at the Forschungszentrum Jülich GmbH, specifically affiliated with the Institute of Energy Technologies (IET) and its division Theory and computational modeling of materials in energy engineering (IET-3). His work focuses on electrochemical systems, particularly modeling and diagnosing fuel cell performance using impedance spectroscopy. Research Interests: Electrochemical modeling, transport phenomena in fuel cells, impedance spectroscopy, computational methods for fuel cell diagnostics, degradation mechanisms, and materials optimization. The 15 most recent publications highlight his expertise in physical modeling of PEM and SOFC systems, with emphasis on oxygen and proton transport dynamics, flow oscillations, and analytical solutions for impedance spectra. Key subfields include Warburg impedance, dusty-gas models, pore shape optimization, and phase angle analysis. Scientific Contributions: Kulikovsky has developed fast matrix solvers, recurrent impedance models, and analytical frameworks for diagnosing fuel cell transport processes. His work addresses challenges in low-platinum catalyst layers, hydrogen crossover, and water saturation effects.

Thijs RoumenView profile

Assistant Professor

• Human-Computer Interaction
• Digital Fabrication
• Laser Cutting
• +5 more

Thijs Roumen is an Assistant Professor of Information Science at Cornell Tech in New York City and a field member of Computer Science. He serves as Director of the Matter of Tech lab. Previously, he completed his PhD at the Hasso Plattner Institute (HPI) under Patrick Baudisch and worked as a Research Assistant with Shengdong Zhao at the National University of Singapore (NUS) on Portable Laser-Cutting projects. Roumen's research focuses on digital fabrication as a subfield of human-computer interaction (HCI), with particular emphasis on creating technological foundations that allow designers and engineers to build upon each other's work in laser-cut 3D modeling. His work aims to increase model complexity and adoption of digital fabrication technology, making it more accessible and useful for broader populations. His research spans several interconnected areas including laser cutting techniques, structural integrity of fabricated objects, cross-machine compatibility, assembly optimization, and assistive technologies. His publication record reveals a strong trajectory of impactful contributions to the field, with numerous papers at top-tier HCI venues including ACM CHI, UIST, and HRI conferences. His work demonstrates consistent innovation in addressing fundamental challenges in digital fabrication, with particular focus on making fabrication processes more robust, accessible, and collaborative. Several of his papers have received best paper honorable mentions (top-5%) and other awards, including a best demo jury award. Best paper honorable mention (top-5%) Best demo jury award (#1) Roumen is deeply engaged with the academic community through extensive service activities. He has served as subcommittee chair for DIS '24 and CHI'23-'26, UIST'21-'25, and as Doctoral Symposium Chair for UIST'25. He has also held leadership roles including General chair of SCF'23, Demo chair for UIST'21-'24, and various best paper committee positions. His reviewing contributions have been recognized multiple times across major conferences including CHI and UIST. Beyond academia, Roumen is an accomplished marathon runner, having completed major marathons in Chicago (2023, 2:51:22), Berlin (2019), and New York City (2018).

Felix KrahmerView profile

Associate Professor

• Optimization and Data Analysis
• Compressed Sensing
• Unlimited Sampling
• +7 more

Prof. Dr. Felix Krahmer is an Associate Professor for Optimization and Data Analysis at the Department of Mathematics, Technical University of Munich (TUM) . His research focuses on mathematical foundations of data science, with expertise in Compressed Sensing Unlimited Sampling Quantization of Signals and Images Neural Networks Uncertainty Quantification Low-Rank Recovery He leads the Data Science research group within TUM's School of Computation, Information and Technology, collaborating with institutions like TU Berlin, RWTH Aachen, and Technion. His recent publications span topics like Robust blind deconvolution under adversarial noise Kronecker-structured dimensionality reduction Hysteresis in unlimited sampling systems Efficient sparse FFT algorithms Notable scientific awards include two August-Wilhelm-Scheer Visiting Professorships (2016, 2019) for hosting international collaborators. Former and current students include Olga Graf Dominik Stöger Claudio Verdun Felipe Pagginelli Patricio Juana Kostin He has co-organized workshops at SAMPTA, Oberwolfach, and ISIT, serving as associate editor for journals including SIAM Journal on Matrix Analysis and Frontiers in Signal Processing.

• Numerical Analysis
• Computational Mathematics
• Sparse Signal Processing
• +4 more

Dr. Toni Volkmer is a researcher affiliated with the Faculty of Mathematics at Chemnitz University of Technology. His work focuses on computational mathematics and signal processing, particularly in high-dimensional data analysis and sparse Fourier transforms. His research intersects numerical analysis, approximation theory, and algorithm design for efficient data processing. Key research areas include Sparse spectral estimation Rank-1 lattice sampling Multivariate function approximation High-dimensional data analysis Fast matrix-vector operations Graph Laplacian computations His publications demonstrate expertise in developing sublinear-time algorithms for harmonic analysis and creating efficient numerical methods applicable to modern data science challenges. While no formal awards are listed, his contributions to scientific computing have been documented in multiple peer-reviewed publications since 2012.

Remco I. LeineView profile

Professor

• Nonlinear Dynamics
• Nonsmooth Mechanics
• Stability Theory
• +6 more

Remco I. Leine is a Full Professor and Director of the Institute for Nonlinear Mechanics at the University of Stuttgart. He earned his M.Sc. from Delft University of Technology, Ph.D. from Eindhoven University of Technology, and Habilitation from ETH Zurich, where he later became a Titular Professor. His academic journey includes postdoctoral research at Technical University Munich, INRIA Rhône-Alpes, and ETH Zurich. Leine's research interests sit at the intersection of nonsmooth dynamics and nonlinear dynamics, focusing on stability theory for nonsmooth systems, friction-induced vibrations, bifurcations in nonsmooth dynamical systems, and multibody systems with impact and friction. Recent work explores Koopman–Hill stability analysis, nonsmooth integration algorithms, and model reduction techniques for complex mechanical systems. Key trends in his publications include advances in numerical methods (e.g., nonsmooth RATTLE algorithm, generalized-alpha method), stability analysis of periodic orbits, modeling of frictional and impact phenomena, and applications to systems like the tippedisk and vibro-impact energy sinks. His work bridges theoretical developments with practical simulations in fields such as rockfall dynamics and multibody contact problems. Scientific awards include a prestigious Fellowship from the Royal Dutch Academy of Sciences (2002) . Advising highlights numerous current and completed PhD students, such as Paul Haacker, Tianxiang Dai, and Jonas Breuling. He contributes to editorial work as a Topical Associate Editor for Nonlinear Dynamics and a scientific board member for JTCAM . The Institute for Nonlinear Mechanics , which he directs, is a hub for research on nonsmooth dynamical systems. He actively participates in workshops and conferences , including keynote lectures at ENOC 2024 and ECCOMAS 2021, and co-organizes European Network for Nonsmooth Dynamics symposia.

Tim JahnView profile

Assistant Professor

• Statistical inverse problems
• Stochastic optimization
• Dimension reduction
• +3 more

Tim Jahn is a MATH+ junior research group leader for "Mathematics of Data Science" at the Technical University of Berlin since September 2023. Previously, he served as Acting Professor for "Data Assimilation" at the University of Potsdam from October 2023 to March 2024, and as a Research Associate with the "Hausdorff Postdoc" position at the University of Bonn from September 2021 to August 2023. Dr. Jahn received his Doctorate with highest honors (summa cum laude) from the University of Frankfurt in April 2021, following an M.Sc. in Mathematics (2016, grade 1.0) and B.Sc. in Physics (2015, grade 1.1), both from the same institution. His academic journey included an Erasmus exchange at Stockholm University in 2014. Dr. Jahn's research focuses on statistical inverse problems , stochastic optimization , dimension reduction , and analysis of neural networks . His work bridges theoretical mathematics with practical applications in data science, particularly in developing regularization methods that function without precise noise characteristics. His publication record shows a clear evolution from fundamental mathematical theory toward increasingly applied work connecting with machine learning methodologies, with his 2024 paper on "Early Stopping of Untrained Convolutional Neural Networks" representing a significant contribution at this intersection. His research program has been supported through competitive fellowships including the prestigious Hausdorff Postdoc fellowship at the University of Bonn and his current MATH+ junior research group leadership at TU Berlin, funded by the Berlin Mathematics Research Center. As leader of the "Mathematics of Data Science" group within the Institute of Mathematics, Dr. Jahn directs research that develops rigorous mathematical foundations for modern data analysis techniques, with particular emphasis on uncertainty quantification and the interface between classical inverse problems and contemporary machine learning approaches.

Simone PreussView profile

Researcher

• Boundary Element Method
• Viscothermal Acoustics
• Acoustic Metamaterials
• +5 more

Simone Preuss, M.Sc., is a Research Associate at the Chair of Vibro-Acoustics of Vehicles and Machines at the Technical University of Munich (TUM). She is affiliated with the TUM School of Engineering and Design, Department of Engineering Physics and Computation. Her research focuses on advanced computational acoustics techniques, particularly the Boundary Element Method (BEM), viscothermal acoustics, and acoustic metamaterials. Her recent work explores the integration of machine learning with BEM for robust sound field calculations and damping quantification. She has contributed to projects involving fluid-structure interaction, inverse acoustics, and numerical methods in Python. Her publications address challenges in thermoviscous acoustics, fast multipole BEM, and isogeometric analysis. Simone actively teaches courses such as Numerical Acoustics in Python and collaborates with colleagues including Steffen Marburg, Ahmed Mostafa Shaaban, and Johannes Schmid. Her research bridges theoretical advancements in acoustics with practical applications in engineering mechanics and vehicle acoustics.

Eva DarulovaView profile

Associate Professor

• Programming Languages
• Software Verification
• Program Synthesis
• +3 more

Eva Darulova is an Associate Professor at the Department of Information Technology, Uppsala University, and Adjunct Faculty at Max Planck Institute for Software Systems (MPI-SWS). Previously held tenure-track faculty at MPI-SWS and completed her PhD at École Polytechnique Fédérale de Lausanne (EPFL) under Viktor Kuncak. Current research: Applications in numerical/embedded domains, program synthesis, software verification, approximate computing Key projects: Blossom (fuzzing), Lassie (HOL4 tactics), Pine (floating-point loops), LTLTalk (robot instruction), Incarnate (3D printing), Icing (compiler semantics), Flipper (NLP interface), FloVer (error checking), Daisy (accuracy analysis), Rosa (real compiler) Teaching: Program Design (2021/22), Program Synthesis (2021), Program Analysis (multiple years), Advanced Program Analysis (2019), Static Program Analysis (2017), Approximate Computing seminar (2016) Service: Artifact evaluation chair (ASPLOS'22), PC member (ECOOP'22, ESEC/FSE'22, EMSOFT'21, PLDI'21) Research Trends: Focus on numerical computing verification, approximate computing frameworks, and program synthesis techniques. Publications span TACAS, EMSOFT, ISSTA, POPL, CAV, FMCAD, TOPLAS, with emphasis on floating-point error analysis, mixed-precision optimization, and compiler verification. Scientific Recognition: Nominated for EAPLS best paper award (TACAS'21). Co-advisor for Anastasiia Izycheva and mentor for multiple MSc/BSc students including Mustafa Hafidi, Joachim Bard, Nikita Zyuzin, Youcef Merah, Safya Alzayat, Anastasiia Izycheva. Collaborations: Works with researchers at EPFL, MPI-SWS, University of Saarland, University of Washington, and Saarbrücken/Kaiserslautern institutions.

Jason KayeView profile

Research Fellow

• Numerical Analysis
• Computational Quantum Physics
• Quantum Many-Body Problems
• +4 more

Jason Kaye serves as a Flatiron Research Fellow at the Flatiron Institute since September 2019, jointly appointed by the Center for Computational Mathematics and Center for Computational Quantum Physics. His expertise lies in developing robust numerical algorithms for computational quantum physics challenges. His academic credentials include: Ph.D. in Mathematics from New York University M.S. in Mathematics from Cambridge University B.S. in Mathematics from Brown University Dr. Kaye specializes in high-order adaptive methods and fast algorithms for compressing structured operators, with direct applications to integral/differential equations in quantum systems. His research significantly advances many-body Green’s function methods, quantum embedding frameworks, and electronic structure calculations—bridging numerical mathematics with quantum many-body problem solving. This work enables scalable simulations of complex quantum phenomena through algorithmic innovation. Embedded within both computational centers, he contributes to interdisciplinary teams advancing mathematical foundations for quantum physics simulations, leveraging the Institute's focus on high-performance computational science.

Stefan FunkenView profile

Professor

• Numerical Analysis
• Finite Element Method
• Boundary Element Method
• +7 more

Prof. Dr. Stefan Funken serves as Professor of Numerical Analysis, Dean of the Faculty of Mathematics and Economics, and Deputy Director of the Institute for Numerical Mathematics at the University of Ulm. His distinguished academic career spans over two decades with significant contributions to numerical mathematics and computational methods. His educational journey includes: 1982-1986: Vocational training as energy plant electronics technician at RWE 1986-1988: Abitur (secondary education) at Friedrich-Spee-Kolleg in Neuss 1988-1993: Mathematics studies at University of Hannover and Brunel University, earning MSc in Numerical Analysis (1992) and Diplom (1993) 1996: PhD with dissertation 'Fast solution methods for FEM-BEM coupling equations' at University of Hannover 2002: Habilitation on 'Contributions to a posteriori error estimation in the numerical treatment of elliptic partial differential equations' at University of Kiel Prof. Funken's research focuses on developing and analyzing discretization methods for partial differential and integral equations. His work emphasizes efficient algorithm implementation in MATLAB, C++, and Python, with applications spanning engineering simulations, corrosion analysis, and fluid dynamics. He has made seminal contributions to adaptive mesh refinement techniques, a posteriori error estimation frameworks, and the coupling of finite element and boundary element methods. His recent publications reveal a consistent research trajectory focused on practical implementations of numerical methods, particularly through MATLAB-based tools. The work demonstrates evolution from theoretical foundations to robust software implementations that serve both academic research and industrial applications, with particular emphasis on computational efficiency and error control. Prof. Funken actively mentors doctoral researchers, with current students working on adaptive mesh refinement algorithms, polygonal mesh analysis for corrosion simulation, and lubrication oil validation. He has developed extensive educational resources including the 'Mathe? Logisch!' YouTube channel (launched 2018) and specialized tutorials for LaTeX and MATLAB learning. He maintains active professional engagement through memberships in the German Mathematical Society (DMV) and the Society for Applied Mathematics and Mechanics (GAMM), contributing significantly to the mathematical community through these organizations. Leading specialized research groups in numerical mathematics, Prof. Funken has developed critical software tools including epsBEM (Efficient P-Stable Boundary Element Methods) and AMESHREF (Matlab-Toolbox for Adaptive Mesh Refinement in 2D). His team bridges theoretical numerical analysis with practical computational implementations, creating accessible educational resources and open-source tools that democratize advanced numerical methods for students and researchers worldwide.

• Deep Learning
• Natural Language Processing
• Physics Simulations
• +3 more

Dennis Fast is a Researcher affiliated with the Data Science and Text-based Information Systems (DATEXIS) research group at Beuth University of Applied Sciences Berlin. He joined the DATEXIS team as a research assistant in January 2022, collaborating on developing transformer-based biomedical language models with Paul Grundmann, Alexei Figueroa, and Conor Fallon. His work bridges computational engineering and data science, focusing on deep learning applications in natural language processing and physics simulations. Education: MSc in Computational Engineering (specializing in Acoustic Simulations) from BHT Berlin (2018), and a second MSc in Data Science at BHT Berlin (started 2021). Professional experience includes roles as computational engineer and technical coordinator in European rail vehicle acoustics projects (Shift2Rail FINE1 & FINE2), where he specialized in data generation, analysis, and visualization. Research interests emphasize deep learning algorithms, real-world applications of large language models, and interdisciplinary approaches combining physics simulations with machine learning. Active in acoustic modeling for transportation systems, contributing to noise prediction frameworks and railway innovation assessments. Publications focus on acoustic analysis methodologies for railway systems, published in venues such as Euronoise and Internoise. Current projects explore biomedical NLP applications while maintaining expertise in simulation-driven engineering solutions.