Prof. David Peleg is a Professor in the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science. His research focuses on algorithms (particularly graph algorithms and approximation methods for NP-hard problems), distributed computing (including fault tolerance and lower bounds), and communication networks (protocols and complexity analysis). He is affiliated with the Jacob Ziskind Building, Room 255. Research interests emphasize foundational aspects of distributed systems and network protocols. His work bridges theoretical computer science with practical network challenges, addressing scalability and efficiency in communication primitives.
Gurprit Singh is a Researcher at the Max Planck Institute for Informatics, Saarbrücken, Germany. His work focuses on advancing Monte Carlo integration techniques and their applications in generative AI, physically based rendering, and optimization. He has contributed to conferences such as SIGGRAPH, Eurographics (EG), and Pacific Graphics (PG), serving in roles like Technical Program Committee member and co-chair for Doctoral Consortium programs. Roles: Associate Senior Researcher, Conference Co-chair (EGSR 2021), and active in academic service. Research Interests: Monte Carlo methods, MCMC sampling, gradient-based optimization, and generative models. His research bridges rendering, optimization, and AI, with notable work on noise optimization in diffusion models and perceptual error minimization. He has received the Best Student Paper Award at ICPRAM 2025.
Dr. Yuto Bekki is a Researcher at the Max Planck Institute for Solar System Research (MPS) , affiliated with the Solar and Stellar Interiors Department . His groundbreaking work in helioseismology and solar physics focuses on characterizing long-period solar oscillations through computer simulations . These oscillations, linked to the Sun's rotation, provide critical insights into the deep convection zone and its turbulent dynamics. Education : University of Tokyo (BSc, MSc) International Max Planck Research School on Solar System Science at the University of Göttingen (PhD, 2018-2022) Dr. Bekki's research explores solar inertial modes , Rossby waves , and their role in understanding stellar interiors . His work has earned international recognition, including the Patricia Edwin PhD Thesis Prize (EPS) and an honorable mention (IAU). His simulations and theoretical frameworks have advanced the study of solar differential rotation , angular momentum transport , and convective processes . Recent publications highlight trends in numerical modeling using tools like Dedalus , validation of anelastic approximations , and analysis of Rossby wave eigenfunctions . These studies collectively enhance our ability to probe the Sun's interior structure and rotational dynamics through helioseismology. Scientific Awards : Patricia Edwin PhD Thesis Prize (EPS Solar Physics Division, 2023) Honorable mention (IAU Sun and Heliosphere Division, 2023) Dr. Bekki is supported by the ERC Synergy Grant WHOLE SUN , which funds his ongoing research into solar oscillations and stellar dynamics . He collaborates closely with the MPS helioseismology group and international teams to translate simulation results into observational diagnostics.
Joël Ouaknine is Scientific Director at the Max Planck Institute for Software Systems (MPI-SWS) located at the Saarland Informatics Campus in Saarbrücken, Germany. He leads the Foundations of Algorithmic Verification research group and serves as an Associate Editor for the Journal of the ACM (JACM) since 2023 and previously for the Journal of Computer and System Sciences (JCSS) from 2014-2023. His research focuses on the Foundations of Algorithmic Verification and Theoretical Computer Science, particularly on decision, control, and synthesis problems for discrete and continuous linear dynamical systems using tools from number theory, Diophantine geometry, and algebraic geometry. His work also encompasses automated verification of real-time, probabilistic, and infinite-state systems, logic applications to verification, automated software analysis, and concurrency. His research integrates deep mathematical techniques with computer science theory to solve fundamental verification problems. Analysis of his recent publications reveals a strong trend toward solving decision problems in linear dynamical systems, with increasing emphasis on connections to number theory and algebraic geometry. His work bridges theoretical computer science with pure mathematics, particularly in addressing the Skolem Problem and related questions about linear recurrence sequences. Recent publications show growing interest in monadic second-order logic extensions and their applications to dynamical systems. Distinguished Paper Award at LICS 2024 for work on monadic second-order logic with arithmetic predicates ACM SIGBED Best Paper Award in 2024 for research on linear dynamical systems with continuous weight functions LICS Test-of-Time Award Winner in 2025 for a seminal 2007 paper on Metric Temporal Logic Dr. Ouaknine currently supervises PhD students Piotr Bacik, Joris Nieuwveld, and Mihir Vahanwala, and mentors postdocs Quentin Guilmant, Toghrul Karimov, and Isa Vialard. His research is supported by significant funding including an ERC Synergy Grant (2025-2031) as Coordinating Principal Investigator for the DynAMiCs project, and DFG Collaborative Research Centre 248 (2022-2026) as Principal Investigator for the Foundations of Perspicuous Software Systems. His previous ERC Consolidator Grant (2015-2021) supported work on Analysis, Verification, and Synthesis of Infinite-State Systems. He actively contributes to the academic community through service on numerous program committees including ICALP, LICS, CONCUR, and serves as organizer for workshops like Dynaverse and Bellairs. His research group at MPI-SWS collaborates extensively with mathematicians and computer scientists worldwide, creating a vibrant interdisciplinary environment focused on solving fundamental problems at the intersection of computer science and mathematics.
Toghrul Karimov is a postdoctoral researcher at the Max Planck Institute for Software Systems (MPI-SWS) in Saarbrücken, Germany, working on the ERC Synergy Grant “DynAMiCs” since April 1, 2025. He collaborates with Valérie Berthé on decision problems at the intersection of dynamical systems theory, logic, and number theory. His educational background includes: Bachelor’s and Master’s degrees from the University of Oxford (2019) Doctoral degree from Saarland University and MPI-SWS (2024) Dr. Karimov’s research focuses on the algorithmic analysis of linear dynamical systems, with emphasis on decidability, verification, and computational complexity. His work bridges theoretical computer science with mathematical logic and number theory, particularly investigating reachability problems, linear recurrence sequences, and the application of o-minimal structures to verification. He employs techniques from automata theory and model theory to solve long-standing problems in the field. His publication record demonstrates consistent contributions to top-tier venues including LICS, ICALP, and SODA, with a clear trajectory toward resolving fundamental questions in dynamical systems verification. Recent work shows increasing sophistication in handling parametric systems and probabilistic extensions, while maintaining strong connections to number-theoretic foundations. His scientific recognition includes: Distinguished Paper Award at LICS 2024 ACM SIGBED Best Paper Award at HSCC 2024 Currently supported by the ERC Synergy Grant “DynAMiCs”, Dr. Karimov maintains an active collaborative research program without current student supervision. His work involves frequent co-authorship with leading researchers across MPI-SWS and IRIF, reflecting the highly interdisciplinary nature of his investigations. He contributes to MPI-SWS’s theoretical computer science research cluster, focusing on the mathematical foundations of software systems verification through the lens of dynamical systems and logic.
Aaron Kelly holds dual roles as Visiting Scientist at the Max Planck Institute for the Structure and Dynamics of Matter (Theory Department) in Hamburg, Germany, and Assistant Professor at Dalhousie University in Halifax, Canada. His research focuses on theoretical and computational methods for nonadiabatic quantum dynamics, particularly in molecular systems and quantum-classical hybrid environments. He is affiliated with the Center for Free-Electron Laser Science (CFEL) and the Theory Group within the Max Planck Institute. His work addresses challenges in simulating electronic coherence, surface-hopping dynamics, and cavity-modified molecular processes using advanced quasiclassical and mapping Hamiltonian approaches. Recent research highlights include developing efficient algorithms for nonadiabatic dynamics simulations, benchmarking methodologies for multi-state systems, and exploring ultrafast phonon-mediated scattering mechanisms. His contributions bridge quantum mechanics with classical models, enabling studies of exciton dissociation, charge transfer at interfaces, and photon correlations in cavity-QED systems. Key collaborations involve advancing computational tools for vibronic spectroscopy without Born-Oppenheimer approximations and probing zero-point energy effects in molecular systems. His work often emphasizes practical applications of theoretical frameworks to real-world phenomena, such as energy transport in molecular junctions and environmental effects on electronic transitions.
Prof. Michael Kaufmann is a Professor in the Department of Computer Science at Eberhard Karls University of Tübingen, within the Faculty of Mathematics and Natural Sciences. He is affiliated with the Wilhelm-Schickard Institute of Computer Science and leads the Algorithmics team. His research focuses on algorithms, complexity, computational geometry, graph drawing, and network visualization. He holds a prominent role in advancing theoretical computer science and combinatorial optimization. His research interests span algorithms and complexity, approximations, combinatorial optimization, computational geometry, graph drawing applications, and parallelism. His work bridges theoretical foundations with practical applications in network visualization and algorithmic design. Prof. Kaufmann has published extensively in leading venues, including Graph Drawing (GD), International Symposium on Theoretical Aspects of Computer Science (STACS), and International Workshop on Graph-Theoretic Concepts in Computer Science (WG). His publications address challenges in graph visualization, algorithmic techniques, and computational geometry. He advises students through the Algorithmics team and maintains an office at Room B112, reachable via email or phone. His work contributes to both academic and applied domains within computer science.
Prof. Dr. Felix Lindner is a Professor in the Department of Analysis and Applied Mathematics at the University of Kassel. His research focuses on stochastic partial differential equations (SPDEs), numerical analysis of stochastic processes, and their applications in computational mathematics and mechanics. He holds a PhD in Mathematics from Dresden and has contributed extensively to the study of SPDE regularity, numerical schemes for stochastic dynamics, and convergence analysis of approximation methods. Research Interests: Stochastic Analysis and Numerics Stochastic Partial Differential Equations (SPDEs) Numerical Methods for PDEs/SDEs Convergence and Stability of Numerical Schemes Applications in Material Science and Mechanical Engineering Recent Publications Trends: Advances in weak and strong convergence rates for SPDE approximations Stochastic modeling of fiber dynamics and composite materials Development of adaptive numerical methods for SPDEs Analysis of singular behavior in stochastic heat equations Students: Current doctoral advisees include Quinten Kürpick, Manuel Lorenz, Felipe Trolldenier, and P. Tobias Werner. Former student Saeed Hadjizadeh completed his research under Lindner's supervision. Labs/Teams: Lindner leads a research group focused on stochastic computational methods, collaborating with industry partners on fiber dynamics modeling and numerical analysis of mechanical systems.
Corinna Coupette is an Assistant Professor of Computer Science at Aalto University's School of Science, leading the Telos Lab. She holds dual PhDs in Law and Computer Science from the Max Planck Institutes. Her research focuses on computational legal theory, integrating code, data, and law to model complex systems like information societies. She serves as Program Director for the interdisciplinary Master’s Program in Information Networks and is a Research Affiliate at the Max Planck Institute for Tax Law and Public Finance. Education: Dr. iur. (Law, 2018, summa cum laude) and Dr. rer. nat. (Computer Science, 2023, summa cum laude). Awards include the Otto Hahn Medal and Caroline von Humboldt Prize. Her work spans legal network science, graph theory applications, and ethical AI regulation. Active in teaching, she leads courses like 'Algorithms and Society' and 'Information Visualization', and collaborates internationally through ELLIS and the Max Planck Society.
Prof. Dr. Andreas Kleefeld is a Professor at the University of Applied Sciences Aachen (Campus Jülich) in the Faculty of Medical Engineering and Technomathematics, where he teaches courses such as Analysis and Stochastics. He also leads the Algorithm, Tools and Methods Lab (Numerical and Statistical Methods) at the Jülich Supercomputing Centre (JSC), part of the Institute for Advanced Simulation (IAS) at Forschungszentrum Jülich. His research focuses on boundary integral equations, non-linear eigenvalue problems, acoustic and electromagnetic scattering, and inverse problems. He is a Principal Investigator in the Helmholtz Information Program 1, Topic 1. Research Interests: Kleefeld’s work bridges theoretical and applied mathematics, with emphasis on numerical methods for partial differential equations, scattering theory, and resilience modeling in economic systems. His contributions include advancements in direct sampling methods for inverse scattering, spectral Galerkin schemes for stochastic PDEs, and applications of mathematical morphology in color image processing. Recent Publications: His 2025 works explore economic resilience in global supply networks and fourth-order numerical schemes for reaction-diffusion systems. He has also published extensively on inverse scattering techniques for anisotropic materials and boundary value problems. Labs & Teams: As Group Leader at JSC, he oversees the development of numerical algorithms and statistical methods applied to high-performance computing challenges. His lab collaborates on projects within the Helmholtz Association, focusing on information science and simulation infrastructure.
Prof. Dr. Stefan Heim leads the Neuroanatomy of Language working group at Research Center Jülich's Institute of Neuroscience and Medicine (INM-1). His research bridges cognitive neuroscience and computational approaches to language processing. Research Focus His work investigates the structural and functional organization of language networks in the brain, combining neuroanatomical approaches with advanced computational methods. Current projects explore machine learning applications in neuroscience and computational linguistics. Publication Trends Recent publications focus on machine learning innovations including large language models, efficient training techniques, and applications in scientific domains like plasma physics and renewable energy.
Sohan Lal is a postdoctoral researcher at the Technical University of Berlin (TU Berlin), focusing on advanced modeling and runtime support for large-scale HPC clusters under a DFG-funded project. His PhD in Computer Engineering from TU Berlin (2019) explored power modeling and architectural techniques for energy-efficient GPUs. He contributed to EU-funded LPGPU projects on low-power GPU computing, leading tasks and collaborating across consortium members. Previously, he lectured at Shri Mata Vaishno Devi University and worked as an IT specialist in the Government of India. Education: PhD in Computer Engineering, TU Berlin (2019) Masters in Computer Science, IIT Delhi (2011) Bachelor in Computer Science and Engineering, GCET Jammu (2003) His research interests span GPU architecture, power/performance modeling, memory systems, and applied machine learning. Notable contributions include techniques like Selective Lossy Compression (SLC) for GPUs and entropy encoding-based memory compression (E²MC). He received HiPEAC travel/grants and was an ACM SRC semifinalist (2018). Grants & Collaborations: HiPEAC Collaboration Grant for joint work with TU/e DFG-funded postdoctoral research He actively teaches advanced computer architectures and multicore systems at TU Berlin, reflecting his passion for education developed during his early teaching career.
Kemal Rose is a Postdoctoral researcher at KTH Royal Institute of Technology in Sweden, mentored by professor Sandra di Rocco. He holds a PhD from the Max Planck Institute for Mathematics in the Sciences (Leipzig), advised by Simon Telen and Bernd Sturmfels. His research focuses on algebraic geometry and optimization, with contributions to polynomial systems, tropical geometry, and computational methods in algebraic geometry. Key research themes include certification of polynomial system zeros, p-adic and real cubic surfaces, polyhedral homotopy algorithms, and the algebraic degree of sparse optimization problems. His work bridges theoretical mathematics with practical computational tools, emphasizing interdisciplinary applications in symbolic computation and geometric modeling. Publications span topics such as tropical implicitization, polyhedral-type analysis, and toric geometry. Current research trends reflect a focus on leveraging algebraic methods to solve high-dimensional optimization problems with sparse structures. No scientific awards or grants are explicitly listed in the provided materials. His academic advising history is not detailed here.
Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.
George Biros is a Professor at The University of Texas at Austin, holding the W.A. “Tex” Moncrief Jr. Chair in Simulation-Based Engineering Sciences at the Cockrell School of Engineering’s Institute for Computational Engineering and Sciences (ICES). He leads the Parallel Algorithms for Data Analysis and Simulation (PADAS) group. His research focuses on high-performance computing, fast numerical algorithms, and applications in fluid dynamics, biomedical engineering, and inverse problems. Education: BSc, Aristotle University of Thessaloniki (1995) MSc and PhD, Carnegie Mellon University (1996–2000) Research Interests: Biros develops scalable algorithms for scientific computing, including parallel methods for integral and differential equations, complex fluids dynamics, and medical image analysis. His work bridges computational mathematics, engineering, and biomedical applications, with a focus on solving large-scale problems in heterogeneous computing environments. Awards: IEEE/ACM SC10 Gordon Bell Prize (2010) J. Tinsley Oden Faculty Fellowship (2006–2008) Early Career Young Investigator Award, U.S. Department of Energy (2005) Grants & Labs: Leads the PADAS group, which specializes in high-performance algorithms for data and simulation. His work has been supported by the DOE, NSF, and industry partnerships. Collaborates with institutions like TUM through fellowships. Labs/Teams: Director of the PADAS group at UT Austin, focusing on scalable numerical methods and their applications in science and engineering.
