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Paul-Christian BürknerView profile

Professor

• Uncertainty Quantification
• Prior Specification
• Simulation-Based Inference
• +2 more

Paul-Christian Bürkner is a Professor of Computational Statistics in the Department of Statistics at TU Dortmund University, appointed since June 2023. His educational background includes a Dr. rer. nat. in Psychology and an M.Sc. in Mathematics from the University of Münster and University of Hagen respectively, complemented by dual bachelor's degrees in Mathematics and Psychology. His research focuses on five core areas: Uncertainty Quantification, Prior Specification, Simulation-Based Inference, Model Comparison, and Machine-Assisted Workflow. These domains bridge Bayesian statistics with computational efficiency challenges, emphasizing practical implementations for complex model environments. Recent publications (2023-2025) demonstrate consistent themes in Bayesian methodology innovation, including advances in surrogate modeling, amortized inference techniques, and statistical workflow optimization. Over 70% of his latest articles involve collaborative work on uncertainty-aware computational methods. As former leader of an independent junior research group at the Stuttgart Cluster of Excellence SimTech (2020-2023), he developed research programs combining theoretical statistics with applied computational frameworks. Current work continues this trajectory through TU Dortmund's statistics department infrastructure.

• Molecular Dynamics Simulations
• DFT Calculations
• Materials Science
• +3 more

Stephen Hocker is a Researcher at the Institute for Functional Matter and Quantum Technologies within the University of Stuttgart's Faculty 08. His work focuses on molecular dynamics simulations, DFT calculations, and the study of metals, alloys, surfaces, and interfaces. He is based in Room 6.349 at Pfaffenwaldring 57, Stuttgart. Research Interests: Computational materials science, quantum technologies, and surface/interface analysis. Contact: +49 711 685 65263 | Email (inferred address). His team's activities are accessible via their dedicated TEAM PAGE . No academic awards or advised students are listed in the provided information.

• Hydrology
• Groundwater Modelling
• Bayesian Methods
• +5 more

Wolfgang Nowak is a Professor and Head of the Institute for Modelling Hydraulic and Environmental Systems at the University of Stuttgart. He also serves as Co-spokesperson of the Cluster of Excellence EXC 2075 'Data-Integrated Simulation Sciences'. His research focuses on stochastic simulation, uncertainty quantification, and safety research in hydraulic systems. He leads interdisciplinary projects involving Bayesian methods, machine learning, and computational modeling for environmental and geoscience challenges. His work integrates advanced statistical techniques with physical models to address hydrological processes, contaminant transport, energy systems optimization, and subsurface modeling. Notable contributions include developing hybrid machine learning-physics models (e.g., HydroStartML) to improve hydrological modeling efficiency and Bayesian inference frameworks for parameter estimation in complex systems. Key research areas include groundwater modeling, subsurface contaminant transport, climate change impact analysis, and energy-water nexus studies. He collaborates with international teams on projects like radioactive waste repository safety and renewable energy integration. His publications span high-impact journals in hydrology, environmental science, and computational methods.

• Ecological Modelling
• Remote Sensing
• Forest Ecology
• +7 more

Nikolai Knapp is a Researcher at the Thünen Institute of Forest Ecosystems in Eberswalde, Germany, and a Guest Scientist at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig. His primary role involves coordinating and analyzing data for the German Crown Condition Survey (WZE) within the ICP Forests monitoring program. He holds a PhD in Environmental Systems Sciences from the University of Osnabrück (2019) and has extensive experience in integrating remote sensing, ecological modeling, and field data to study forest dynamics. Knapp’s research focuses on linking remote sensing technologies (e.g., LiDAR, radar) with forest process models like FORMIND to understand tree vitality, mortality, and carbon dynamics in tropical and temperate forests. Key projects include the Biotrop-X initiative for tropical forest modeling and the RedMod project on model upscaling. He has developed R packages for LiDAR data processing and contributed to global biomass estimation frameworks like the GEDI mission. His work emphasizes spatial and temporal analysis of forest structure, biomass distribution, and the impact of environmental changes. Teaching roles include lecturing on LiDAR remote sensing at HNE Eberswalde. Knapp’s publications span remote sensing applications, ecological modeling, and forest inventory methods, with a focus on bridging empirical data and simulation-based insights.

Verena WolfView profile

Professor

• Stochastic Modeling
• Systems Biology
• Computational Biology
• +5 more

Verena Wolf is a full professor in the Modeling and Simulation group at Saarland University since April 2012. She holds a PhD in Computer Science from the University of Mannheim (2008) and previously served as a junior research group leader at the Excellence Cluster Multimodal Computing and Interaction. Her research focuses on stochastic modeling, biochemical reaction networks, and systems biology. She is an Associate Editor of the ACM Transactions on Modeling and Computer Simulation and leads the students’ lab InfoLab Saar . Education: PhD in Computer Science, University of Mannheim, 2008 Postdoc, École Polytechnique Fédérale de Lausanne (EPFL), 2008–2009 Research Interests: Develops methods for analyzing stochastic systems, with applications to biochemical processes and complex networks. Specializes in hybrid models, Markov chain analysis, and scalable simulation techniques. Recent work includes studying epidemic dynamics, DNA methylation mechanisms, and algorithmic approaches for variance reduction. Publications: Over 100 peer-reviewed articles across venues like ACM TOMACS, PLoS Computational Biology , and IEEE Transactions . Themes include computational methods for stochastic systems, machine learning applications in bioinformatics, and formal verification of AI agents. Labs & Roles: Heads the InfoLab Saar, contributes to the Saarland Center for Bioinformatics , and chairs conferences like QEST’19 and CMSB’20 .

Anita SchöbelView profile

Professor

• Operations Research
• Mathematical Optimization
• Robust Optimization
• +12 more

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.

Jan StühmerView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Molecular Mechanics
• +7 more

Jun.-Prof. Dr. Jan Stühmer is an Assistant Professor and Principal Investigator (PI) at the Helmholtz Institute for Information Technology (HITS). His research focuses on machine learning, artificial intelligence, and their interdisciplinary applications in molecular mechanics, protein structure prediction, and materials science. He explores advanced graph neural networks, neuro-symbolic systems, and geometric algorithms to address challenges in computational biology, weather forecasting, and computer vision. Key research interests include permutation-invariant learning models (e.g., Set-LLM), error correction in machine learning systems, and the development of physics-based force fields like Grappa. His work bridges theoretical computer science with practical applications in drug design, materials discovery, and climate modeling. Recent contributions highlight innovations in protein design through geometric algebra, dynamic graph representation learning, and the optimization of crystal structure networks. Dr. Stühmer’s methodologies often integrate spatial information learning and ensemble forecasting techniques, showcasing a commitment to impactful, cross-disciplinary research. Despite extensive publications since 2009, no awards or grants are explicitly mentioned in the provided data. His academic trajectory reflects a sustained focus on advancing machine learning fundamentals and their real-world implications.

Nils ThuereyView profile

Professor

• Computer Graphics
• Fluid Dynamics
• Machine Learning
• +3 more

Prof. Nils Thuerey is a Professor of Physically Based Simulation at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. He specializes in computer graphics, fluid dynamics, and machine learning applications in physics-based simulations. His work focuses on developing algorithms for fluid simulations, turbulence modeling, and differentiable physics solvers. He earned his doctorate in fluid simulations from the University of Erlangen-Nuremberg (2006) and held positions at ETH Zurich, ScanlineVFX, before joining TUM in 2013. Research Interests: Fluid Dynamics and Turbulence Modeling Machine Learning for Physics Simulations Deep Learning in Computer Graphics Differentiable Simulations and Inverse Problems Key Awards: Technical Oscar (2012) for contributions to fluid effects in films/games ERC Starting Grant (2014) for 'realFlow' research University of Erlangen Staedtler Graduation Award (2008) His recent work emphasizes integrating deep learning with physical models, such as differentiable solvers for fluid dynamics and probabilistic methods for weather forecasting. He leads projects like WeatherBench and develops tools like φ-ML and phiflow for scientific computing.

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.

Mahalakshmi SabanayagamView profile

Researcher

• Machine Learning
• Deep Learning Theory
• Graph-based Learning
• +3 more

Mahalakshmi Sabanayagam is a Researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Theoretical Foundations of Artificial Intelligence. Her research focuses on the theory of deep learning and graph-based learning, with notable contributions to adversarial robustness, neural network certification, and graph representation learning. She currently serves as a Teaching Assistant for the course 'Gems of Informatics 3 (IN2176): Modelling and analysis of real-world graphs.' Her work bridges theoretical foundations and practical applications, including analysis of neural tangent kernels in graph networks, robustness certification against adversarial attacks, and cross-domain machine learning techniques. Key research directions include understanding decision boundaries via Hessian analysis, tensorized autoencoder architectures, and graphon-based network clustering. Publications span topics from adversarial machine learning to interdisciplinary applications in physics and finance. While currently no awards or grants are listed, her active publication record reflects ongoing contributions to artificial intelligence theory and graph-based methodologies. She is part of Prof. Debarghya Ghoshdastidar's research group at TUM's Faculty of Computer Science. Lab affiliations include the Theoretical Foundations of Artificial Intelligence (I7) group, focusing on rigorous mathematical analysis of AI systems and their real-world graph applications. No advising relationships or student collaborations are explicitly documented in available materials.

• High-Performance Computing
• Scientific Workflows
• Distributed Computing
• +14 more

Justin M. Wozniak is a computational scientist at Argonne National Laboratory’s Mathematics and Computer Science Division within the Computing, Environment and Life Sciences directorate. He is a key contributor to advanced scientific workflow systems such as Swift/T and Parsl, enabling scalable, distributed, and many-task computing for data-intensive science. His work supports major initiatives in cancer research (CANDLE), epidemiological modeling, and exascale computing (ExaWorks). He collaborates extensively with leading researchers including Ian T. Foster, Michael Wilde, and Kyle Chard. His research focuses on high-performance computing, scientific workflows, distributed systems, and machine learning applications in science. He has pioneered techniques in workflow automation, fault tolerance, in-situ data analysis, and performance optimization. His work enables robust, scalable execution of complex computational pipelines across heterogeneous environments, from supercomputers to cloud platforms. His recent publications (2021–2025) emphasize workflow interoperability, resilience, benchmarking, and applications in cancer and epidemic modeling. Themes include automated model comparison, job management portability (PSI/J), adaptive workflow steering, and exascale-ready workflow toolkits. These works reflect a strong trend toward reproducibility, scalability, and real-world scientific impact. Justin M. Wozniak has no listed scientific awards in the provided text. However, his leadership in major DOE-funded projects and high-impact publications in top venues (SC, HPDC, e-Science) underscores his significant contributions to computational science. He has mentored or collaborated with numerous researchers, though specific advisees are not listed. His work is supported by large-scale computing grants and initiatives such as the ExaWorks project and CANDLE, which aim to accelerate scientific discovery through advanced computing infrastructure. He contributes to open science through tools like Parsl and Swift/T, which are widely used in the scientific community. He is a core developer in the ExaWorks ecosystem and contributes to workflow frameworks that integrate with AI/ML pipelines, containerization, and real-time data analysis. His work on Braid-DB and provenance tracking supports AI-driven science with full reproducibility. These efforts are central to modern computational laboratories aiming for autonomous, data-intensive discovery.

Christian WilmsView profile

Researcher

• Computer Vision
• Object Recognition
• Image Segmentation
• +6 more

Christian Wilms is a Researcher at the University of Hamburg's Department of Informatics within the Faculty of Mathematics, Informatics and Natural Sciences, where he contributes to the Computer Vision Research Group's cutting-edge work in visual recognition systems. His research focuses on: Object recognition techniques including proposal generation, instance segmentation, and detection Foundation models for segmentation and superpixel-based algorithms Applications in agricultural monitoring, medical imaging, and industrial inspection Multi-modal signal processing for audio-visual perception Analysis of his recent publications reveals a strong emphasis on solving small object detection challenges through semi-supervised learning and attention mechanisms, particularly evident in orchard monitoring applications. His work consistently bridges theoretical advances in segmentation topology with practical implementations in drone-based systems and industrial anomaly detection. As part of the Computer Vision Research Group led by Prof. Simone Frintrop, Wilms contributes to major projects including InteGreatDrones, Crossmodal Learning, and NEUROBOTICS, which explore active perception, object pose estimation, and simultaneous localization in complex environments.

• Biophysics
• Cancer Research
• Cellular Biology
• +3 more

Franziska Matthäus is a Professor at Goethe University Frankfurt and a Fellow at the Frankfurt Institute for Advanced Studies (FIAS), where she has worked since 2016. Her research spans biophysics, cancer research, and cellular biology, focusing on computational modeling of biological processes. Born: 1975 in Friedrichroda, Thuringia Education: Biophysics at Humboldt University of Berlin, Doctoral studies at the University of Warsaw (PhD 2005) Past roles: Postdoc at University of Heidelberg (2005-2016), Junior Professor at University of Würzburg (2016) Her research involves simulation-based inference and modeling complex biological processes . She leads a research group at FIAS focusing on interdisciplinary approaches to life sciences. Scientific Awards: Doctoral dissertation evaluated as summa cum laude Giersch Endowed Professorship (2016–present)

Maximilian DinkelView profile

Research Fellow

• Bayesian Methods
• Inverse Problems
• Gaussian Processes
• +1 more

Maximilian Dinkel serves as a Research Fellow at the Institute for Computational Mechanics, Technical University of Munich, where he has been a Research Associate since 2022 under Prof. Wolfgang A. Wall's Chair of Numerical Mechanics. His work bridges computational engineering with advanced statistical learning methodologies. His academic credentials include: Master of Science in Mechanical Engineering (2022, TUM) Bachelor of Science in Management and Technology (2019, TUM) Bachelor of Science in Mechanical Engineering (2018, TUM) Dinkel specializes in Bayesian inverse problems with expensive computational models, developing constrained Gaussian process frameworks and active learning strategies. His research integrates physics-informed machine learning to enhance uncertainty quantification in engineering simulations, particularly addressing challenges in computational mechanics where traditional likelihood evaluations are prohibitively costly. This work enables more efficient parameter estimation and model calibration in complex systems. Analysis of his 2023-2024 publications reveals a cohesive research trajectory focused on overcoming computational bottlenecks in Bayesian inference. Key themes include constrained optimization of Gaussian process surrogates, adaptive sampling techniques, and stochastic variational inference with dynamic learning rate adjustments. These contributions significantly advance the application of machine learning to large-scale engineering problems requiring high-fidelity simulations. He actively contributes to academic mentoring, having co-supervised D. Still's 2023 Master's thesis on adjoint-based Bayesian inference of random fields using Hamiltonian Monte Carlo methods. Dinkel also teaches Numerical Methods for Engineers exercises at TUM, supporting the department's educational mission in computational techniques for engineering applications. As a core member of TUM's Numerical Mechanics research group, he participates in developing next-generation simulation tools within the Institute for Computational Mechanics, collaborating on projects that merge traditional computational mechanics with cutting-edge machine learning approaches to solve previously intractable engineering challenges.

Gregor KasieczkaView profile

Professor

• Particle Physics
• Machine Learning
• High Energy Physics
• +4 more

Gregor Kasieczka is a Professor (W2) for Machine Learning in Particle Physics at Universität Hamburg's Faculty of Mathematics, Informatics and Natural Sciences, affiliated with the Institute of Experimental Physics. His academic journey includes a doctorate from Universität Heidelberg (2013), postdoctoral research at ETH Zürich (2013–2017), and progression from Junior Professor (2017–2022) to full Professor at Universität Hamburg (since 2023). He leads the Emmy Noether Group Kasieczka, focusing on machine learning applications in particle physics. Key responsibilities include: Speaker of KISS consortium (BMBF-funded AI-based simulation initiative) Platform coordinator at Cluster of Excellence 'Quantum Universe' Former Software/Algorithms coordinator for DIG-UM community First convener of CMS Collaboration's Machine Learning group Research interests span machine learning-driven particle physics, including jet substructure analysis, generative models for calorimeter simulations, anomaly detection, and quantum theory applications. His publications emphasize AI/ML integration with experimental particle physics, particularly for LHC/CMS data analysis. Notable research trends from recent works include advances in generative models for fast calorimeter simulation, foundation models for LHC data, self-supervised learning in astrophysics, and model-agnostic anomaly detection techniques. He directs significant projects on AI-based simulation and coordinates cross-institutional ML initiatives. No scientific awards are documented in the provided materials. Kasieczka leads multiple research consortia and advises projects at the intersection of AI and fundamental physics, including generative model development and anomaly detection frameworks. His group actively contributes to the CMS collaboration and quantum universe research platforms.