جست‌وجوی استاد و پژوهشگر

Christian Liebscherمشاهده پروفایل

استاد

• Advanced Transmission Electron Microscopy
• Materials Science
• Energy-Related Materials
• +۶ مورد دیگر

Prof. Christian Liebscher is a Professor of Advanced Transmission Electron Microscopy at the Ruhr University Bochum , affiliated with the Faculty of Physics and Astronomy and the Research Center Future Energy Materials and Systems (RC FEMS). His work focuses on developing cutting-edge TEM techniques to understand energy-related materials' atomic-scale structure-functionality relationships. He combines aberration-corrected scanning TEM (STEM), 4D-STEM, and in-situ microscopy with machine learning to analyze complex material datasets. Education and Career: 2000–2006: Study of Materials Science at the University of Bayreuth. 2006–2010: PhD at the University of Bayreuth (summa cum laude) with a thesis on phase and dislocation analysis in superalloys. 2011–2014: Postdoc at the University of California, Berkeley, and the National Center for Electron Microscopy (Lawrence Berkeley National Laboratory). 2014–2015: Staff scientist at the University of Duisburg-Essen. 2015–2024: Group leader at the Max Planck Institute for Sustainable Materials in Düsseldorf. Research Interests: Prof. Liebscher’s research bridges microscopy innovation and materials understanding. He emphasizes atomic-scale characterization of interfaces, defects, and grain boundaries in metals and alloys using advanced STEM and 4D-STEM. His work addresses how structural features—like segregation, strain, and phase transitions—impact material properties. He also pioneers machine learning tools to automate data analysis from microscopy and tomography, advancing materials dataspaces. Key topics include energy materials (e.g., PEM fuel cells), high-entropy alloys, and nanomaterials for applications like semiconductors and electromagnetic absorption. Scientific Contributions: His publications highlight trends in grain boundary phase transitions, microstructure-property correlations, and integration of AI into microscopy. For example, recent work explores how grain boundary complexions affect mechanical strength in alloys and how in-situ TEM reveals deformation mechanisms under realistic conditions. He has contributed significantly to methodologies like scanning precession electron diffraction tomography and unsupervised machine learning for atomic-resolution datasets. Labs and Collaborations: Prof. Liebscher leads the Advanced Transmission Electron Microscopy group at RUB, building on his previous leadership at the Max Planck Institute. His lab collaborates with institutions like the Lawrence Berkeley National Laboratory and integrates interdisciplinary approaches combining experimental microscopy with computational modeling.

Christoph Heinzlمشاهده پروفایل

استاد

• Scientific Visualization
• Visual Analytics
• Immersive Analytics
• +۳ مورد دیگر

Christoph Heinzl is a Professor of Cognitive Sensor Systems at the University of Passau since September 2022. He leads the Knowledge-based Image Processing research group at the Fraunhofer Development Center X-ray Technology (EZRT) . His academic background includes a PhD in Informatics and a Habilitation in 2022 , both from TU Wien . Research Focus: Scientific visualization, visual analytics, immersive analytics, virtual/augmented reality, machine learning, and X-ray computed tomography (XCT). Key Trends: Development of novel visualization techniques for complex volumetric data (e.g., dynamic volume lines, visual coherence frameworks), parameter space analysis, and cross-virtuality collaboration tools. Applications: Aerospace component inspection, defect analysis in composites (CFRP, GFRP), porosity quantification, and 4DCT time-series exploration.

Miguel Mahechaمشاهده پروفایل

استاد

• Ecosystem Responses to Climate Extremes
• Biogeography and Ecosystem Functioning
• Earth System Data Science
• +۴ مورد دیگر

Miguel Mahecha is Professor of Environmental Data Science and Remote Sensing at the University of Leipzig, where he serves as Institute Head of the Institute for Earth System Science and Remote Sensing. He is also affiliated with the Remote Sensing Centre for Earth System Research, a collaboration between Leipzig University and the Helmholtz Centre for Environmental Research (UFZ). Mahecha is a member of the German Centre for Integrative Biodiversity Research (iDiv) and serves as Principal Investigator in the Centre for Scalable Data Analytics and Artificial Intelligence. Additionally, he is a Fellow of the European Laboratory for Learning and Intelligent Systems and co-spokesperson for the National Research Data Infrastructure for Earth System Sciences (NFDI4Earth). Full Professor for Modelling Approaches in Remote Sensing, University of Leipzig (since 03/2020) Research Group Leader: Empirical Inference in the Earth System, Max Planck Institute for Biogeochemistry, Jena (12/2012 - 03/2020) PostDoc, Max Planck Institute for Biogeochemistry, Jena (10/2009 - 11/2012) PhD in Environmental Sciences, ETH Zürich (06/2006 - 09/2009) Diploma in Geoecology, Bayreuth University (10/2000 - 04/2006) Mahecha's research focuses on understanding ecosystem responses to climate extremes and human-environment relationships during these events. He investigates macro-ecological dynamics and ecosystem functioning using data-driven methods and high-dimensional Earth observations. A key contribution is his co-development of the Earth System Data Cube concept, which integrates empirical methods with theoretical understanding to analyze complex Earth system interactions. His work spans biogeography, ecosystem functioning, and advanced data science methodologies for environmental monitoring. His recent publications demonstrate a strong emphasis on analyzing compound climate extremes, particularly heatwaves and droughts, and their impacts on ecosystems. Mahecha has pioneered methods using Earth System Data Cubes to integrate diverse environmental datasets, enabling novel insights into biosphere-atmosphere interactions. His research increasingly incorporates artificial intelligence and machine learning approaches to understand spatiotemporal patterns in ecological systems, with applications in real-time forest monitoring and biodiversity assessment. Fellow of the European Laboratory for Learning and Intelligent Systems Co-spokesperson for NFDI4Earth (National Research Data Infrastructure for Earth System Sciences) Mahecha leads multiple significant research projects including Digital Forest (real-time forest monitoring), NFDI4BioDiversity, and XAIDA (extreme events: AI for Detection and Attribution). His work receives funding from diverse sources including EU, DFG, and Stiftungen Inland. He collaborates extensively with the German Centre for Integrative Biodiversity Research (iDiv) and the Centre for Scalable Data Analytics and Artificial Intelligence. His research group, Earth System Data Science (ESDS), focuses on developing methods to extract valuable information from long-term environmental observations to understand coupled Earth system dynamics. At the Remote Sensing Centre for Earth System Research, Mahecha's ESDS group investigates how ecosystem functions respond to climate extremes, societal vulnerability to environmental hazards, and nonlinear interactions in coupled Earth systems. The group leverages citizen science data, remote sensing observations, and advanced computational methods to address pressing environmental questions.

Cornelius Faberمشاهده پروفایل

استاد

• MRI cell tracking
• infection imaging
• MR methodology
• +۶ مورد دیگر

Cornelius Faber is a University Professor in the Department of Radiology at the University of Münster, Germany, where he leads the Experimental Nuclear Magnetic Resonance research group. His work focuses on developing and implementing novel MRI techniques that extend the boundaries of magnetic resonance imaging in terms of spatial and temporal resolution, sensitivity, and specificity for physiological, structural, and molecular changes. He actively participates in the "Cells in Motion" interdisciplinary research initiative at the university. Professor Faber's research spans multiple critical areas in medical imaging and biomedical science. His primary expertise lies in MRI cell tracking , enabling visualization of cellular dynamics in vivo. He has made significant contributions to infection imaging , developing methods to detect and characterize microbial infections using MRI. His work on MR methodology development has advanced quantitative imaging techniques, while his research on multimodal integration in MR and MRI contrast mechanisms has provided deeper insights into molecular and cellular processes. His research bridges physics, engineering, and biomedical applications, with particular relevance to inflammation, cancer, neurological disorders, and cardiovascular disease. Analysis of Professor Faber's extensive publication record reveals a clear evolution from fundamental MRI technique development toward increasingly sophisticated applications in disease models. His recent work demonstrates a strong trend toward multimodal imaging approaches that combine MRI with complementary techniques such as mass spectrometry, optical imaging, and PET. This integration creates comprehensive diagnostic platforms that provide both anatomical and molecular information. A notable pattern is the focus on cellular dynamics, particularly immune cell behavior in inflammatory conditions and tumor microenvironments, with applications spanning neuroscience, oncology, and cardiology. Professor Faber leads a multidisciplinary research team of approximately 15 members, including scientists, doctoral students, technicians, and medical students. His laboratory is deeply integrated with the University of Münster's research infrastructure, particularly the Multiscale Imaging Centre. The group's work contributes significantly to advancing preclinical MRI methodologies while maintaining strong clinical relevance, with numerous publications in high-impact journals across medical imaging, neuroscience, and biomedical engineering disciplines.

• Complex Systems Modeling
• Computational Mathematics
• Applied Mathematics
• +۷ مورد دیگر

Yannis Kevrekidis is a Professor at Princeton University with a distinguished career in computational mathematics and chemical engineering. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich (TUM-IAS) and has held visiting positions at institutions like the Zuse Institute Berlin and Caltech. Education : National Technical University of Athens (Chemical Engineering) University of Minnesota (PhD in dynamical systems) Research Interests : Equation-Free and Variable-Free Modeling Complex Systems Dynamics Multiscale Computation Integration of Machine Learning with Scientific Computing Pattern Formation & Instability Analysis Key Article Trends : Advanced data-driven modeling of dynamical systems Manifold learning for reaction coordinates Projective integration methods Coarse-grained modeling across disciplines Applications in epidemiology, neuroscience, and fluid dynamics Scientific Awards : Guggenheim Fellowship Humboldt Research Award Computing in Chemical Engineering Award (AIChE) Bodossaki Academic Award Allan P. Colburn Award Collaborations : Extensive international collaborations with institutions in Germany, Austria, and the UK Key role in the Complex Systems Modeling and Computation focus group at TUM-IAS

Stephan Raveمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Scientific Computing
• Machine Learning
• +۴ مورد دیگر

Dr. Stephan Rave is a Researcher in the Institute for Analysis and Numerics at the University of Münster. He is affiliated with the Applied Mathematics Münster cluster and serves as an Investigator in Mathematics Münster. His work focuses on numerical analysis, scientific computing, and machine learning, with a strong emphasis on model reduction techniques for complex systems. Education : PhD in Mathematics (2012), University of Münster, thesis on finitely summable K-homology. Master's and Bachelor's degrees in Mathematics from the University of Münster. Research Interests : Dr. Rave specializes in model order reduction (MOR) methods, including reduced basis techniques, localized orthogonal decomposition (LOD), and nonlinear approximation strategies. His work addresses challenges in multiscale modeling, domain decomposition, and parametrized partial differential equations. He also develops open-source software tools like pyMOR for MOR and contributes to initiatives like the MaRDI (Mathematical Research Data Initiative) to enhance interoperability in scientific computing. Projects : Key initiatives include the MaRDI project (2021–2026), EXC 2044 Cluster of Excellence (Geometry-based modeling), and MULTIBAT (lithium-ion battery simulation). His research bridges theoretical developments with practical applications in battery modeling, electrochemistry, and computational fluid dynamics. Grants & Awards : Funded by DFG, the German Federal Ministry of Research, and internal university grants, his work addresses strategic areas like sustainable research software and energy storage systems. He leads projects on distributed model reduction and communication-avoiding algorithms. Teaching : Dr. Rave teaches advanced numerical methods courses, including Model Order Reduction, Numerical Methods for PDEs, and Python-based computational labs. He co-organizes seminars and workshops on MOR and scientific software engineering.

Christoph T. Kochمشاهده پروفایل

استاد

• Electron Microscopy
• Materials Science
• Condensed Matter Physics
• +۳ مورد دیگر

Christoph T. Koch is a Professor of Physics at Humboldt-Universität zu Berlin, where he has held the W3 Chair since 2015. Previously, he held a similar position at Ulm University (2011–2015), supported by the Carl Zeiss Foundation. His research focuses on advanced electron microscopy techniques, including quantitative transmission electron microscopy (TEM), electron holography, and strain mapping. He leads the AG Strukturforschung/Elektronenmikroskopie group, advancing materials science through innovations in imaging and spectroscopy. Education: B.Sc./M.Sc. in Physics at Heidelberg University (1996–1998), followed by an exchange at Arizona State University (1997–1998). PhD in Physics from Arizona State University (2002, advisor: Prof. John C.H. Spence). Postdoctoral research at the Max Planck Institute for Metals Research, Stuttgart (2002–2011). Research interests include: Electron diffraction and phase retrieval Nanometer-scale strain and defect analysis Electron energy-loss spectroscopy (EELS) for plasmonics and bandgap mapping Development of FAIR data infrastructure for materials science Leadership: Managed the Department of Physics at Humboldt University (2020–2024). Collaborates widely, with key co-authors including P.A. van Aken, W. Sigle, and C. Felser. His work bridges experimental microscopy and computational modeling, addressing challenges in semiconductors, ceramics, and 2D materials. Notable contributions include pioneering methods for 3D reconstruction via electron ptychography, dynamic electron diffraction analysis, and strain mapping in advanced CMOS technologies. Current efforts emphasize real-time imaging and AI-driven data analysis in materials research.

• Experimental Fluid Dynamics
• Data Assimilation
• Network Science
• +۳ مورد دیگر

Prof. Dr.-Ing. David E. Rival is a full Professor at the Institute of Fluid Mechanics within the Faculty of Mechanical Engineering at Technische Universität Braunschweig. His research spans interdisciplinary domains at the intersection of experimental fluid dynamics, data assimilation, network science, and bio-inspiration, with applications in renewable energy systems and bio-mimetic engineering. Former Associate Professor at Queen’s University, Canada Doctoral work on dragonfly flight aerodynamics at TU Darmstadt Alexander von Humboldt research fellowship recipient (2020) Postdoctoral associate at MIT studying shape morphing in nature Research chair at University of Calgary on atmospheric sensing His work focuses on unsteady flow phenomena, bio-inspired design, and advanced measurement techniques. Key projects include: Co-chairing NATO AVT task group on flow separation International collaborations with AFOSR, NATO, and ONR Development of cost-effective flow-tracking sensors for natural environments Investigations into shear-thinning suspension dynamics and vortex ring behavior Recent publications demonstrate a strong emphasis on: Large-scale particle tracking with natural light and UAVs Machine learning for sparse data reconstruction in fluid flows Soft coastal protection methods and ecohydraulics Advanced sensing techniques for atmospheric and industrial applications Scientific Awards: 2020: Alexander von Humboldt Research Fellowship Notable research achievements include textbook authorship on Biological and Bio-Inspired Fluid Dynamics (Springer) and media features in The Nature of Things (David Suzuki) and Discovery Channel’s Daily Planet .

• Calculus of Variations
• Continuum Mechanics
• Nonlinear Elasticity
• +۵ مورد دیگر

Prof. Dr. Marco Cicalese is a Professor of Mathematical Continuum Mechanics at the Technical University of Munich (TUM), holding a position in the Department of Mathematics within the TUM School of Computation, Information and Technology. He has been at TUM since 2012, following roles as an Assistant Professor at the University of Naples (2005–2012) and a researcher at SISSA (2004–2005). His research focuses on variational analysis of atomistic and continuous systems, multiscale problems, and geometric inequalities. Education: PhD in Applied Mathematics from the University of Naples (2004), MSc in Physics (details not specified). His editorial roles include Associate Editorships at Acta Applicandae Mathematicae and Mathematics in Engineering . Research interests encompass calculus of variations, nonlinear elasticity, and phase transitions, with contributions to discrete-to-continuum limits and stability of geometric inequalities. Teaching includes courses on partial differential equations, calculus of variations, and mathematical modeling. His work often bridges discrete and continuous models, with applications to materials science and continuum mechanics. Recent publications explore topics like Wulff crystal emergence, fractional vortices, and surfactant effects in phase transitions.

• Theoretical Chemistry
• Computational Chemistry
• Quantum Chemistry
• +۷ مورد دیگر

Frank Neese is the Director and Managing Director (since 2024) of the Max-Planck-Institut für Kohlenforschung in Mülheim an der Ruhr, Germany, where he leads the Department of Molecular Theory and Spectroscopy. He holds honorary professorships at the University of Bonn (since 2013) and the University of Duisburg-Essen (since 2020), reflecting his strong academic affiliations. His research program bridges theoretical chemistry, quantum mechanics, and spectroscopy with applications in bioinorganic and materials chemistry. Education: Diploma in Biology, University of Konstanz (1993) Ph.D. (Dr. rer. nat.), University of Konstanz (1997) Postdoctoral Research, Stanford University (1997–1999) Habilitation, Universität Konstanz (2001) Frank Neese's research focuses on the development and application of advanced quantum chemical methods for understanding molecular electronic structures, particularly in transition metal complexes and metalloenzymes. His work emphasizes spectroscopic simulations (EPR, XAS, MCD, etc.) and reaction mechanisms in catalysis. He is renowned as the lead developer of the ORCA quantum chemistry software, a widely used tool in computational chemistry. His theoretical frameworks integrate density functional theory, wavefunction-based methods, and multiscale modeling to achieve high accuracy in predicting chemical properties. The 15 most recent publications highlight a consistent trajectory in electronic structure theory, with strong emphasis on spectroscopy, transition metal chemistry, and method development. Key themes include double-hybrid functionals, spin-state energetics, spin-orbit coupling, and QM/MM modeling of biological systems. The interdisciplinary nature of his work spans chemistry, biochemistry, and materials science, often targeting challenges in catalysis and energy conversion. Scientific Awards: Gottfried Wilhelm Leibniz Prize (2023) Humboldt Research Award ISACS Award Fellow of the Royal Society of Chemistry Member of the North Rhine-Westphalian Academy of Sciences Member of the Leopoldina Neese has secured extensive third-party funding for his research, enabling a large, interdisciplinary team of scientists and students. He actively mentors PhD and postdoctoral researchers, fostering the next generation of theoretical chemists. His leadership extends to official functions in scientific societies and editorial roles in major chemistry journals. The ORCA development team, which he heads, is a central hub for innovation in computational chemistry software. He leads a vibrant research group focused on method development and applications in molecular spectroscopy and reactivity. The team collaborates internationally and organizes the ORCA User Meeting, fostering a global community of users and developers in quantum chemistry.

Tony Stillfjordمشاهده پروفایل

دانشیار

• Numerical Analysis
• Partial Differential Equations
• Stochastic Optimization
• +۴ مورد دیگر

Tony Stillfjord is an Associate Professor at the Centre for Mathematical Sciences, Lund University, Sweden. He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and Chalmers/University of Gothenburg. Ph.D. and MSc in Numerical Analysis from Lund University Funded by WASP (Wallenberg AI, Autonomous Systems and Software Program) Research Interests : Numerical methods for partial differential equations, splitting schemes, stochastic optimization, and large-scale differential Riccati equations. His work focuses on developing low-rank approximations and robust optimization algorithms. Recent Publications highlight advancements in Lie/Strang splitting for operator-valued Riccati equations, stochastic descent methods, and GPU-accelerated splitting schemes. Software Contributions : Developed DREsplit (MATLAB package for differential Riccati equations) and BST20_CODE for stochastic optimization experiments. Contact : Office at MH:562E, Lund University. Email: tony.stillfjord@math.lth.se . URL: tonystillfjord.net

• Transmission Electron Microscopy (TEM) Method Development
• Charge Particle Optics and Scattering Theory
• Magnetic Nanotextures
• +۳ مورد دیگر

Dr. Axel Lubk is a Group Leader at the Institute for Solid State Research (IFW Dresden) , specializing in advanced electron microscopy techniques for materials science. His research spans four key areas: (1) TEM method development (high-resolution imaging, tomography, holography, and in-situ techniques), (2) charge particle optics and scattering theory , (3) magnetic nanotextures (domain walls, skyrmions), and (4) plasmonics (mode hybridization in heterogeneous structures and semiconductor heterostructures). Dr. Lubk’s work focuses on three-dimensional magnetic texture analysis using electron holography and tomography, particularly in systems like skyrmion tubes , FeGe , and Cr2O3 thin films . He has pioneered techniques for vector-field electron tomography and phase retrieval under varying boundary conditions, advancing nanoscale magnetic imaging. His recent studies include plasmonic properties in AgAu nanosphere chains , thermoelectric multilayer systems , and topological insulators like NiRh2Sb and TaTMTe4 . Dr. Lubk has published extensively in high-impact journals such as Nature Communications and Advanced Materials , with a focus on TEM instrumentation and quantitative analysis . He frequently presents at international conferences like the International Microscopy Congress and European School of Magnetism , emphasizing applications in spintronics , quantum materials , and nanostructured systems . His contributions to holographic vector-field electron tomography and machine learning for spectrum-image data have set new standards in electron microscopy.

Barbara Wohlmuthمشاهده پروفایل

استاد

• Numerical Simulation
• Partial Differential Equations
• Discretization Techniques
• +۴ مورد دیگر

Prof. Barbara Wohlmuth is a full professor in Numerical Mathematics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. She leads the International Graduate School of Science and Engineering at TUM and has held professorships at Stuttgart, Darmstadt, and Berlin universities. Her research focuses on numerical simulation of partial differential equations, multiscale solvers, and coupled multi-field problems with applications in engineering. Education: Studied mathematics at TUM and Université Joseph Fourier in Grenoble, received her doctorate from TUM in 1995, and completed habilitation in Augsburg. Visiting professorships in USA, France, and Hong Kong. Research interests include discretization techniques, predictive modeling, and interdisciplinary collaboration with engineering disciplines. Notable achievements: 2012 Gottfried Wilhelm Leibniz Prize (Germany’s highest academic honor in sciences), 2005 Sacchi-Landriani Prize. Publications emphasize advanced numerical methods in fluid dynamics, geophysics, and biomedical engineering. Active in editorial roles for international journals and scientific committees across Europe and USA. Elected member of Bavarian and European Academies of Sciences. Key contributions include: Development of robust numerical algorithms for exascale simulations Pioneering work in coupled multi-physics modeling Innovative methods for computational contact mechanics Leadership in graduate education initiatives

Corina Andronescuمشاهده پروفایل

استادیار

• Electrochemical Energy Conversion
• Catalysis
• Materials Science
• +۳ مورد دیگر

Corina Andronescu is an Assistant Professor at the University of Duisburg-Essen, leading the Technical Chemistry III department. Her research focuses on electrocatalysis, advanced materials synthesis, and sustainable energy conversion. She has held positions at the Ruhr-University Bochum and the University Politehnica of Bucharest. Education: PhD in Chemical Engineering (2014), University Politehnica of Bucharest Master’s in Chemical Engineering (2011), University Politehnica of Bucharest Bachelor’s in Chemical Engineering (2009), University Politehnica of Bucharest Research interests: Development of novel electrocatalysts for oxygen evolution reaction (OER), CO₂ electroreduction, and alcohol oxidation High-throughput discovery of energy-related materials using combinatorial methods Investigation of nanostructured materials (e.g., perovskites, high-entropy alloys) for industrial applications Integration of plasma and laser-based technologies in catalyst fabrication Operando electrochemical microscopy to analyze catalyst heterogeneity Design of gas diffusion electrodes for scalable chemical production Awards: 2023: Jochem Block Preis (DECHEMA), Junge Kolleg Membership (NRW Academy) 2022: Joachim Walter Schultze Preis, Gottschalk-Diederich-Baedeker-Preis 2018: Ad Astra Research Excellence Award (Romania), Electrochimica Acta Travel Award 2017: RUB Inventor Award for self-healing catalyst films Advising & Grants: While no advisees are listed, her work involves collaborations on high-throughput material discovery and industrial electrolyzer optimization projects. She has secured grants for aerosol-based catalyst synthesis and nanocomposite development. Labs/Teams: Head of the Technical Chemistry III lab at UDE, focusing on electrocatalytic processes and advanced materials characterization using techniques like liquid-cell TEM and SECCM.

Benjamin Stammمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• High-Performance Computing (HPC)
• +۷ مورد دیگر

Prof. Benjamin Stamm is a Professor of Numerical Mathematics at the University of Stuttgart, leading the Chair of Numerical Mathematics for High Performance Computing within Faculty 08. He holds a Ph.D. and master's degree in mathematics from École Polytechnique Fédérale de Lausanne (EPFL) and has previously worked at RWTH Aachen University, Sorbonne Université UPMC Paris 6, UC Berkeley, and Brown University. His research focuses on numerical analysis, scientific computing, and simulations, particularly efficient discretizations for PDEs, eigenvalue problems, error certification, reduced basis methods, and HPC implementations. He develops scalable numerical methods for problems in computational chemistry and physics, emphasizing accuracy, efficiency, and interdisciplinary collaboration with chemists, physicists, and materials scientists. Prof. Stamm’s work includes contributions to domain decomposition methods, polarization energy calculations, and software development like the ddX library. His publications span topics such as model order reduction, quantum simulations, and molecular dynamics. Collaborations and software tools underscore his commitment to bridging computational methods with real-world scientific challenges.