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Amadeus GebauerView profile

Researcher

• Cardiac mechanics
• Cardiac growth and remodeling
• Computational mechanics
• +3 more

Amadeus Gebauer is a Researcher at the Chair of Computational Mechanics within the Institute for Computational Mechanics at the Technical University of Munich (TUM), serving as a Research Associate since 2019. His work specializes in computational biomechanics with emphasis on cardiac mechanics modeling, growth and remodeling processes, and multi-physics simulation frameworks. Education: Master of Science (M.Sc.) in Mechanical Engineering, Technical University of Munich, 2019 Research Interests: Gebauer's research centers on cardiac mechanics modeling, including growth and remodeling of cardiac tissue, cardiac active tissue mechanics, and medical image processing. He develops advanced computational methods for parallel and high performance computing, particularly through the 4C multi-physics simulation framework. His work integrates constrained mixture models to simulate organ-scale biological processes, bridging computational mechanics with clinical cardiology applications and focusing on mechanobiological stability in cardiac systems. Publication Trends: Gebauer's publications (2018-2025) demonstrate consistent innovation in computational cardiology, primarily using constrained mixture models to address cardiac growth and remodeling. His recent work introduces adaptive integration techniques for history variables and homogenized modeling approaches, while expanding into software benchmarking for cardiac elastodynamics and gastric motility simulations. These contributions highlight his expertise in developing robust numerical methods for multi-physics biomedical problems, with increasing focus on patient-specific applications and high-performance computing solutions. Teaching and Advising: Gebauer teaches core computational mechanics courses including Finite Elemente and Numerische Festkörpermechanik across multiple semesters. He has supervised diverse student projects ranging from term papers to Master's theses, with notable collaborations including Maximilian Grill's shoulder biomechanics research (2020) and Janina Datz's artery geometry framework development (2021). His advising consistently focuses on cardiac mechanics, computational modeling, and medical device simulation. Research Environment: As part of Professor Wolfgang A. Wall's Institute for Computational Mechanics (LNM) at TUM, Gebauer contributes to a leading research group in computational solid/fluid mechanics. The LNM develops the 4C simulation framework for complex engineering and biomedical challenges, with current emphasis on cardiac growth modeling, multi-physics integration, and high-performance computing applications in personalized medicine.

• Lithium-Ion Battery Production
• Energy Storage Systems
• Production Engineering
• +3 more

Prof. Dr.-Ing. Rüdiger Daub serves as Professor and Chair of Production Engineering and Energy Storage Systems at the Technical University of Munich (TUM), operating within the Department of Mechanical Engineering. His leadership encompasses research direction, academic supervision, and strategic development of battery production technologies at TUM's Garching campus (Boltzmannstr. 15), with active industry collaborations driving innovation in sustainable manufacturing. Daub's research program pioneers advanced production methodologies for lithium-ion and solid-state batteries, focusing on electrode manufacturing, electrolyte filling, and cell assembly processes. His work investigates critical parameter interdependencies affecting battery safety and performance, developing inline monitoring systems and digital twin technologies for real-time process optimization. Key contributions include moisture control in electrode production, electrochemo-mechanical characterization of solid-state systems, and robotics solutions for deformable object assembly, all integrated with machine learning for quality assurance in industrial settings. Analysis of his 2023-2025 publications reveals a dominant research trajectory toward solving production bottlenecks in next-generation energy storage. The work demonstrates increasing integration of computational modeling with empirical validation, particularly in solid-state battery manufacturing and high-voltage electrolyte systems. A notable trend is the cross-pollination of robotics, computer vision, and uncertainty quantification techniques to address complex assembly challenges and distribution shifts in quality monitoring, reflecting industry's urgent need for adaptable, data-driven production systems. Leading TUM's specialized laboratories for battery cell production, Daub's team maintains comprehensive facilities for electrode calendering, electrolyte filling, and cell assembly with integrated tracking and tracing capabilities. The research infrastructure supports collaborative projects with automotive OEMs and battery manufacturers to develop scalable production processes, emphasizing environmental sustainability through water-based electrode production and footprint optimization. Current initiatives focus on digital factory modeling and prelithiation technologies for next-generation battery systems.

Feng ChengView profile

Researcher

• Data-Driven Security Engineering
• Security Analytics
• Network Security
• +19 more

Dr. Feng Cheng is a Senior Researcher and head of the IT Security Engineering (Sec-Eng) Team at the Hasso Plattner Institute (HPI), Germany, and serves as the Representative of the Chair "Internet Technologies and Systems" at the Digital Engineering Faculty, University of Potsdam. He holds a PhD from the University of Potsdam, an MEng from Beijing University of Technology, and a BEng from Beijing University of Aeronautics and Astronautics. His research is centered on data-driven security engineering, security analytics, network security, cloud security, firewall, IDS/IPS, attack modeling, and penetration testing. Dr. Cheng's research interests span a broad range of cybersecurity domains, with a strong emphasis on security analytics , data-driven threat detection , cloud and network security , and authentication . His work integrates advanced data engineering and machine learning techniques to enhance security operations, including intrusion detection, threat intelligence, and security information and event management (SIEM). Key application areas include IoT security, mobile security, and web security. The recent publications of Dr. Cheng and his team reflect a significant trend towards leveraging big data analytics , machine learning , and graph-based methods for cybersecurity. Their work focuses on developing frameworks for blockchain-based public key infrastructures, advanced SIEM analytics, malware detection using NLP, and chaos engineering for cloud security. This demonstrates a strong commitment to creating practical, scalable, and intelligent solutions for modern security challenges in complex, distributed environments. Dr. Cheng is actively involved in the academic community as a member of ACM and IEEE, a program committee member for numerous international conferences, and an organizer of workshops on cloud computing and cybersecurity. He has also served on editorial boards for international journals. He supervises a large group of PhD, master's, and bachelor's students, guiding research on topics such as security analytics, cyber threat intelligence, digital credentials, and behavioral authentication. He leads the Sec-Eng team, which conducts research on a diverse portfolio including the Security Analytics Lab, HPI-VDB (vulnerability database), Lock-Keeper (physical separation technology), and the Security Lab Generator for scenario-driven security training.

• Product Development Methodology
• Lightweight Structures
• Systems Engineering
• +5 more

Prof. Dr. Markus Zimmermann leads the Chair of Product Development and Lightweight Design at the Technical University of Munich (TUM). With a background in mechanical engineering from TU Berlin and the University of Michigan, and a doctorate from MIT on solid-state singularities, he bridges academic rigor with industrial application. His career spans 12 years at BMW focusing on vehicle development before transitioning to academia. Specializes in solution space engineering for robust design Expert in additive manufacturing and systems engineering Develops methodologies for managing design complexity and uncertainty His research focuses on multidisciplinary design optimization and lightweight structures , particularly in robotics and automotive systems . His team applies digital twin frameworks and attribute dependency graphs to enhance design processes. Recent publications emphasize topology optimization in robotic systems and thermal management for medical X-ray sources. Key trends in his 2024-2025 publications include: Topological optimization for additive manufacturing and robotics Application of solution spaces to manage design uncertainty Development of compact X-ray systems for medical therapy Integration of digital twin technologies in industrial contexts

• Medical Image Analysis
• Computer Vision
• Deep Learning
• +7 more

Dimitris N. Metaxas is a Professor in the Department of Computer Science within the School of Arts and Sciences at Rutgers University. His research spans computer vision, medical image analysis, and artificial intelligence, with a particular focus on medical applications including cardiac MRI analysis and foundation models for healthcare. Dr. Metaxas's research interests encompass medical image analysis, computer vision, deep learning, and artificial intelligence. His work demonstrates a strong emphasis on applying advanced machine learning techniques to medical imaging problems, particularly in cardiac analysis. He has made significant contributions to diffusion models, multimodal learning, and efficient AI techniques for medical applications. His research bridges the gap between theoretical computer vision and practical healthcare solutions, with numerous publications in top-tier conferences and journals. His recent publications show a clear trend toward foundation models for medical image analysis, with significant contributions to cardiac MRI segmentation, diffusion models, and multimodal learning. The research spans both theoretical advancements in AI techniques and practical applications in healthcare, particularly focused on improving medical diagnostics through computer vision. His work demonstrates expertise in adapting cutting-edge AI techniques like diffusion models and large language models for specialized medical applications. Dr. Metaxas has mentored numerous students and researchers, as evidenced by his extensive publication record with multiple co-authors across various institutions. His work has received significant attention in the research community, with numerous publications in top venues including CVPR, ICCV, MICCAI, and Medical Image Analysis. His research group focuses on medical image computing, computer vision, and machine learning applications in healthcare. The team works extensively with cardiac MRI data, developing advanced techniques for segmentation, reconstruction, and analysis of 4D cardiac imaging. They are particularly known for their contributions to foundation models in medical imaging and efficient adaptation techniques for specialized medical tasks.

• Chromatography
• Porous Media
• Materials Science
• +7 more

Ulrich Tallarek serves as Professor of Analytical Chemistry in the Faculty of Chemistry at Philipps University of Marburg, where he has held a W3 professorship since 2011. He also serves on the Board of Directors for the Materials Science Center at the university, a position he has held since 2007. His research group focuses on the fundamental understanding of transport phenomena in porous media with applications spanning chromatography, battery technology, and microfluidic systems. The group maintains strong collaborations with institutions worldwide and secures substantial research funding for advanced computational and experimental work. Professor Tallarek's research interests center on functional porous solids, with specific focus on morphology-transport-performance relationships. His work bridges multiple scales from molecular dynamics simulations of solute behavior in nanopores to macroscopic transport in chromatographic columns and battery electrodes. Key research areas include diffusion in hierarchical porous media, electrokinetic phenomena in microfluidic systems, molecular simulation of chromatographic processes, and advanced characterization of porous materials using tomography and other techniques. His group has pioneered multiscale simulation approaches that connect molecular-level surface chemistry to macroscopic transport properties. The research output demonstrates consistent focus on understanding fundamental transport mechanisms in porous systems, with recent publications emphasizing multiscale simulation techniques, molecular dynamics studies of solvent effects in chromatography, advanced characterization of mesoporous structures, and applications to separation science and energy storage. The work shows strong integration of computational modeling with experimental validation across multiple length scales. 2003: Desty Memorial Prize for Innovation in Separation Science, The Royal Institution of Great Britain, London 2006: Young Scientist Award from DECHEMA e.V. 2011: Named Discussion Leader at the 2011 Gordon Research Conference on Physics & Chemistry of Microfluidics 2011–2012: Chairman of the German Chemical Society (GDCh), Marburg 2013: Finalist, World Technology Awards, for category Environment 2013: Named as one of the 100 most influential analytical scientists in the world (The Analytical Scientist Power List) 2017: Recipient of the Silver Jubilee Medal 2017, The Chromatographic Society, UK Professor Tallarek's research has been supported by numerous grants enabling high-performance computing resources, advanced instrumentation, and international collaborations. His group maintains strong ties with industry partners in separation science and analytical instrumentation. The Tallarek Research Group includes postdoctoral researchers, PhD students, and technical staff working across experimental and computational domains. Current projects focus on molecular simulation of chromatographic processes, advanced characterization of porous battery electrodes, and development of novel separation methodologies. The Tallarek Research Group operates state-of-the-art facilities for computational modeling, including access to high-performance computing resources at Forschungszentrum Jülich. The group also maintains experimental capabilities for chromatographic analysis, materials characterization, and microfluidic device development. Their work on physically reconstructed porous media has established new standards for connecting microstructure to transport properties in complex materials systems.

• Building Information Modeling (BIM)
• Civil Engineering Informatics
• Tunneling and Underground Construction
• +5 more

Markus König is a Professor of Informatics in Civil Engineering at Ruhr University Bochum, where he has been researching and teaching since October 2009. His work focuses on Building Information Modeling (BIM), digital construction technologies, and civil engineering informatics, with significant contributions to the development and implementation of digital methods in German construction industry. Dr. König earned his degree in civil engineering with a focus on applied computer science at Leibniz University Hannover, where he also completed his doctorate on cooperative building planning at the Institute for Building Informatics. He subsequently held a junior professorship for Theoretical Methods of Project Management at Bauhaus University Weimar before joining Ruhr University Bochum. His research spans multiple cutting-edge areas including Building Information Modeling (BIM), construction process simulation, tunneling informatics, infrastructure asset management, and the application of artificial intelligence and computer vision in civil engineering. As chair of the Building Informatics Working Group from 2012-2016, he played a key role in developing the first national BIM curriculum for German universities and serves as editor of the book 'Building Information Modeling: Technological Foundations and Industrial Practice.' Analysis of his recent publications reveals a strong trend toward semantic technologies, digital twins, automated compliance checking, and the integration of AI in construction processes. His work increasingly focuses on information containers, ontology development, and the application of large language models to infrastructure data, reflecting the evolving landscape of digital construction. Dr. König's significant contributions to digital construction have been recognized with prestigious awards: Lower Saxony-Bremen Construction Industry Award (2017) for 'services in the development and introduction of digital construction in Germany' Konrad Zuse Medal (2020) While specific details about his advising and grant activities aren't explicitly mentioned in the provided text, his extensive publication record with numerous co-authors suggests active supervision of doctoral students and research staff. His involvement in multiple collaborative research projects is evident from his publication history. At Ruhr University Bochum, Professor König leads a research group focused on civil engineering informatics, with particular emphasis on BIM, digital construction technologies, and their application across the building lifecycle. His team appears to work at the intersection of computer science and civil engineering, developing innovative solutions for construction process optimization, infrastructure management, and digital transformation of the AEC industry.

Maria Francesca SpadeaView profile

Professor

• Medical Image Processing
• Artificial Intelligence in Healthcare
• Radiomics
• +5 more

Prof. Dr.-Ing. Maria Francesca Spadea serves as Director of the Institute of Biomedical Engineering (IBT) at Karlsruhe Institute of Technology (KIT), part of the Helmholtz Association. Her leadership role includes overseeing research initiatives, teaching activities, and administrative responsibilities within the institute. Located in space 512, she maintains regular consultation hours on Wednesdays from 10:30-11:30 am by appointment. Professor Spadea's research spans several cutting-edge areas in biomedical engineering, with particular focus on medical image processing, artificial intelligence applications in healthcare, and radiomics. Her work bridges computational techniques with clinical applications, emphasizing practical solutions for medical imaging challenges. She has pioneered approaches in federated learning for medical image translation, particularly in CT/MRI synthesis for radiation therapy applications. Her research also extends to cancer cell analysis, vascular biomechanics, and medical robotics, demonstrating a broad yet cohesive research portfolio that addresses critical challenges in modern healthcare. Analysis of Professor Spadea's recent publications reveals a strong emphasis on AI-driven medical imaging solutions, particularly in the translation between different imaging modalities (like MRI-to-CT) using federated learning approaches that preserve patient privacy. Her work demonstrates growing specialization in radiation therapy applications, with multiple publications addressing synthetic CT generation for treatment planning. There's also a clear trajectory toward multi-institutional collaboration, as evidenced by her involvement in projects spanning multiple research centers across Europe. Professor Spadea actively mentors numerous students, including M. Krohmer Zabaleta, N. Skupien, and M. Destito, who have completed bachelor's and master's theses under her supervision. Her research group appears well-integrated within the broader Institute of Biomedical Engineering, collaborating extensively with colleagues like P. Zaffino and C.B. Raggio on multiple projects. The group maintains strong connections with clinical partners, as evidenced by publications addressing real-world medical challenges in radiation therapy, cardiology, and neurosurgery. The research activities of Professor Spadea's team are centered within the Institute of Biomedical Engineering at KIT, with particular focus on medical imaging processing and AI applications. Her laboratory appears to specialize in developing computational tools for medical image analysis, with recent work emphasizing privacy-preserving federated learning frameworks that enable multi-institutional collaboration without sharing sensitive patient data. The team maintains active collaborations with clinical departments, particularly in radiation oncology, as evidenced by numerous publications addressing CT synthesis for radiation therapy planning.

Jana ZaumseilView profile

Professor

• Nanomaterials
• Optoelectronics
• Carbon Nanotubes
• +5 more

Professor Jana Zaumseil is a distinguished academic at Heidelberg University, holding the position of Professor for Applied Physical Chemistry at the Faculty of Chemistry and Earth Sciences since 2014. She also maintains a co-opted position with the Faculty of Physics and Astronomy since 2016. Currently serving as Executive Director of the Institute for Physical Chemistry and Spokesperson for the DFG Research Training Group GRK 2948, she leads the Zaumseil research group (also known as the Nanomaterials for Optoelectronics group) at Heidelberg University's Institute for Physical Chemistry. Her educational background includes a PhD in Physics from the University of Cambridge (2003-2007) with a Gates Cambridge Trust Scholarship, and a Diplom (equivalent to M.Sc.) in Chemistry from the University of Leipzig (1997-2022). Prior to her position at Heidelberg, she served as Professor for Nanoelectronics at Friedrich-Alexander-Universität Erlangen-Nürnberg (2009-2014), and completed postdoctoral work at Argonne National Laboratory (2007-2009) following an internship at Bell Laboratories (2002-2003). Zaumseil's research program focuses on the optical and electronic properties of carbon-based nanomaterials, particularly single-walled carbon nanotubes (SWCNTs) and organic semiconductors. Her group specializes in processing, functionalization, characterization and application of these unconventional semiconductors for optoelectronic devices and sensors. They investigate charge transport and light-matter interaction using a wide range of experimental techniques including synthesis, optical spectroscopy, atomic force microscopy, device fabrication, and electrical/optical device characterization. Their work bridges fundamental understanding with potential applications in sensing, imaging, circuits, and energy conversion. Analysis of her recent publications reveals a strong trend toward defect engineering in carbon nanotubes, particularly creating and optimizing luminescent sp 3 defects for near-infrared applications. Her research increasingly integrates fundamental studies of charge transport with practical device applications, especially in neuromorphic computing, biosensors, and thermoelectrics. The interdisciplinary nature of her work is evident in the combination of chemistry, physics, and materials science approaches across her publication record. Dan Maydan Prize for Nanoscience and Nanotechnology (2024) Jahrespreis der Universität Heidelberg (2023) ERC Consolidator Grant (2019) ERC Starting Grant (2012) Alfried-Krupp-Award for Young University Professors (2010) Professor Zaumseil has secured substantial research funding including multiple ERC grants and leads several major collaborative projects such as the ERC Advanced Grant SCALE-NT, Collaborative Research Center SFB 1249, Cluster of Excellence 3D Matter Made to Order, and Research Training Group GRK 2948. She has mentored numerous doctoral and master's students, with her group recently receiving recognition including a Student Poster Presentation Award for Niklas Herrmann. As Dean of the Faculty of Chemistry and Earth Science (2019-2021) and current Vice Dean (2021-), she has played significant leadership roles within the university structure. The Zaumseil research group operates within Heidelberg University's Institute for Physical Chemistry, utilizing advanced facilities for nanomaterial synthesis, optical spectroscopy, and device characterization. The group participates in several major collaborative initiatives including the Cluster of Excellence 3D Matter Made to Order and the Collaborative Research Center SFB 1249, reflecting its integration within Heidelberg's broader research ecosystem focused on molecular systems and materials science.

• Probabilistic Machine Learning
• Physics-Informed Machine Learning
• Industrial Applications of Data Science
• +4 more

Markus Lange-Hegermann serves as Professor of Mathematics and Data Science at Ostwestfalen-Lippe University of Applied Sciences (TH OWL) since 2018 and holds a board position at the Institute for Industrial Information Technology (inIT). His career bridges academic research and industrial applications, with expertise in translating machine learning theory into practical engineering solutions for automation and manufacturing sectors. His educational foundation includes a Diplom (Master equivalent) in Computer Mathematics from RWTH Aachen University (2004-2008) followed by a Dr. rer. nat. (PhD equivalent) in algorithmic differential algebra (2008-2014). Prior to academia, he gained industry experience at FEV GmbH as an R&D engineer (2014-2017) and P3 automotive GmbH as a Data Science Consultant (2017-2018). Lange-Hegermann's research centers on probabilistic machine learning with distinctive emphasis on physics-informed approaches. He develops Gaussian process methodologies that incorporate differential equations to model time dependencies, uncertainties, and physical constraints in industrial systems. His work enables robust data-based modeling and optimization for cyber-physical systems, with applications spanning predictive maintenance, process control, and quality assurance in manufacturing. Analysis of his 15 most recent publications (2024-2025) reveals consistent innovation in physics-integrated machine learning, particularly using Gaussian processes to solve partial differential equations and optimal control problems. The research demonstrates strong industrial applicability across domains including medical imaging, material science, automotive engineering, and brewing processes, with recurring themes of anomaly detection in time-series data and uncertainty-aware decision making. His scientific contributions have earned significant recognition: Forschungspreis TH OWL (2024) Top reviewer award at NeurIPS (2023) Outstanding reviewer award at NeurIPS (2021) Best poster award at Bosch AI CON (2019) Borchers Plakette for outstanding dissertation (2014) Springorum Denkmünze for outstanding diploma (2009) As chairman of the Data Science study program and vice chairman of undergraduate examination boards, Lange-Hegermann actively shapes academic curricula while supervising graduate theses. His governance roles include serving on professorship search committees at multiple institutions and contributing to examination regulations. He maintains active research funding through collaborations with industrial partners and reviews proposals for initiatives like It's OWL and 3IA Côte d’Azur. Lange-Hegermann leads the Mathematics and Data Sciences research group within inIT, fostering collaboration between theoretical machine learning and industrial automation. He co-founded AICOmmunityOWL and the Informatics Europe working group on Data Analysis and Reporting, while organizing machine learning reading groups and data science hackathons to bridge academic research with industrial problem-solving.

Johannes HungerView profile

Researcher

• Condensed Phase Dynamics
• Ionic Liquids
• Electrolyte Solutions
• +7 more

Dr. Johannes Hunger is a Group Leader at the Max Planck Institute for Polymer Research (MPI-P) in Mainz, Germany. He obtained his PhD in Chemistry from the University of Regensburg in 2006 under the supervision of Richard Buchner. Following a postdoctoral fellowship at the FOM Institute AMOLF in Amsterdam (2010-2012) funded by the German Science Foundation (DFG), he joined MPI-P as a group leader in 2012. In 2016, he established his distinguished research group with a starting grant from the European Research Council (ERC). His research focuses on dynamics in the condensed phase with particular emphasis on Coulombic and hydrogen-bonding interactions. His group intensively studies room temperature ionic liquids, electrolyte solutions, and solvation phenomena, elucidating fundamental properties of liquids including molecular-level ion transport and molecular association, as well as the relevance of such interactions in biological contexts (osmolyte action, specific ion effects) and technological applications (solvation dynamics, dissolution phenomena). With ERC funding, his group has intensified research on non-covalent interactions within reactive intermediates in organo-catalysis. The 15 most recent publications (2024-2025) demonstrate continued focus on hydrogen bonding dynamics, ion transport mechanisms, and solvation phenomena across various systems including aqueous interfaces, ionic liquids, and biomolecular environments. His work consistently combines experimental approaches with theoretical insights to unravel fundamental molecular processes. Scientific Awards: Postdoctoral Fellowship from German Science Foundation (DFG) ERC Starting Grant Dr. Hunger leads an active research group currently focused on three main areas: Fundamentals of Asymmetric Organo-Catalysis, Interaction of Osmolytes and Ions with Biopolymers, and Dynamics of Electrolytes. His research program is supported by the ERC grant FASTO-CAT and involves collaborations with numerous international research groups. His group maintains strong connections with the University of Regensburg where he completed his doctoral studies, and with research institutions in the Netherlands where he conducted his postdoctoral work. The research group operates state-of-the-art facilities for studying molecular dynamics, including advanced spectroscopic techniques for probing hydrogen bonding networks and ion transport phenomena at ultrafast timescales.

Thomas WöhlingView profile

Researcher

• Integrated environmental systems modeling
• Surface water-groundwater interactions
• Braided river systems
• +9 more

PD Dr. habil. Thomas Wöhling serves as a Senior Research Scientist and Team Leader for Stochastic Modelling of Hydrosystems at the Chair of Hydrology, Dresden University of Technology's Faculty of Environmental Sciences. His research spans integrated environmental systems modeling with particular expertise in surface water-groundwater interactions, braided river systems, and vadose zone processes. Previously, he held research positions at Water and Earth System Sciences Competence Cluster in Tübingen (2010-2015) and Lincoln Environmental Research in New Zealand (2006-2010). Dr. Wöhling completed his Dipl.-Hydrol. (1999) and PhD in Hydrology (2005) at Dresden University of Technology, followed by habilitation in Stochastic Hydrology (2021). His educational background includes extensive research at the Institute of Hydrology and Meteorology at TU Dresden (1999-2005) where he developed foundational expertise in hydrological modeling. Wöhling's research focuses on integrated modeling of coupled environmental systems , particularly flow and contaminant transport in surface water-groundwater systems, nutrient and energy fluxes in soil-plant-atmosphere systems, and distributed hydrological modeling. His work emphasizes stochastic modeling and uncertainty analysis , with significant contributions to inverse modeling, model calibration, multiobjective optimization, and Bayesian model averaging techniques. He has pioneered methods for evaluating monitoring network worth and data utility for environmental models. His publication record demonstrates consistent contributions to hydrological science, with recent work (2023-2025) focusing on machine learning applications in hydrology, advanced statistical inversion techniques, and complex karst system modeling. Key trends include integration of physics-based and data-driven approaches, improved uncertainty quantification methods, and applications to climate change impacts on water resources. His work bridges theoretical advances with practical applications in New Zealand's braided rivers and European hydrological systems. STAHY Best Paper Award (2018) ASCE Journal of Irrigation and Drainage Engineering Best Reviewer Awards (2008, 2010, 2011, 2015, 2018) ASCE Journal of Irrigation and Drainage Engineering Best Paper Awards (2008, 2009) Dr. Wöhling leads the Stochastic Modelling of Hydrosystems team and has secured funding for numerous projects including Klimakonform, ISOSIM, VAMOS II, and the International Research Training Group 'Integrated Hydrosystem Modelling.' His work combines novel monitoring techniques with modeling and optimal sensor placement to improve prediction reliability for river-groundwater exchange fluxes. He collaborates extensively with international partners, particularly in New Zealand through the Lincoln Agritech's Braided Rivers program. His laboratory work focuses on combining traditional hydrological measurements with advanced computational techniques, including deep learning applications for soil surface hydrology and time-windowed Bayesian analysis for predictive modeling. The team maintains strong connections with field sites in Germany's Saxon region and New Zealand's Canterbury Plains, facilitating integrated theoretical and empirical research approaches.

• Human-Computer Interaction
• Geographic Information Science
• Virtual Reality
• +7 more

Johannes Schöning is a Professor of Human-Computer Interaction at the University of St. Gallen, where he leads a research group focused on developing novel user interfaces that empower individuals and communities with data-driven decision capabilities. His work bridges rapidly advancing technologies with human needs across diverse contexts including geographic information science, public health, medical applications, and extreme environments such as space missions. He publishes extensively at premier HCI venues including ACM CHI, MobileHCI, and DIS, as well as in interdisciplinary journals like NATURE and PLOS ONE. Professor Schöning's research interests center on understanding the interplay between technology and human activities through rigorous methods from AI, computer graphics, and cognitive psychology. His work emphasizes user-centered design methodologies and mixed methods approaches to create interfaces that fit both technological possibilities and human requirements. Key focus areas include virtual and augmented reality applications, accessibility solutions, navigation technologies, and the social implications of digital interfaces in everyday life. His research mission prioritizes theoretical and practice-based inquiry to develop disruptive solutions for real-world problems. Analysis of his recent publications reveals strong trends in virtual reality applications for emotional regulation and accessibility, with increasing emphasis on generative AI integration, environmental awareness, and space-related HCI challenges. His work consistently demonstrates interdisciplinary collaboration across computer science, psychology, geography, and medical fields, with publications showing growing interest in social implications of technology, particularly regarding navigation systems and their externalities. His scientific contributions have been recognized with numerous awards including: Best Presentation Award at IEEE VR 2023 Best Paper Award & Accessibility Award at Interact 2019 10 Year Impact Award 2021 Multiple Honorable Mention Awards at top conferences Professor Schöning actively mentors students across bachelor, master, and PhD levels, with his lab seeking candidates interested in the intersection of HCI, geoinformatics, and ubiquitous computing technologies. He emphasizes creating a 'detox-free academic environment' that focuses on meaningful teaching and research outcomes rather than academic pressures. His group frequently collaborates with international researchers and institutions on projects spanning medical applications, space exploration interfaces, and public health technologies. The research laboratory led by Professor Schöning maintains strong interdisciplinary connections across multiple domains. Current projects include developing AI-powered wearables for blind and low vision users, exploring VR applications for emotional regulation, investigating navigation technologies' social impacts, and creating interfaces for space mission contexts. The lab's work on CubeSat control software and plant visualization for space greenhouses demonstrates their unique focus on extreme environment applications, while their research on citizen participation through generative AI shows engagement with contemporary societal challenges.

Andrea BarthView profile

Professor

• Stochastic Partial Differential Equations
• Uncertainty Quantification
• Monte Carlo Methods
• +4 more

Andrea Barth is a W3-Professor of Computational Methods for Uncertainty Quantification at the University of Stuttgart, leading the Research Group for Computational Methods for Uncertainty Quantification within the Excellence Cluster for Simulation Technology. She holds a Ph.D. from the University of Oslo (2009) and has held positions at ETH Zürich and the University of Stuttgart. Her work focuses on stochastic partial differential equations, numerical methods for uncertainty quantification, and applications in engineering and natural sciences. Education: Ph.D. in Mathematics, University of Oslo (2006–2009) Lecturer/Postdoc at ETH Zürich (2010–2013) Junior Professor at University of Stuttgart (2013–2017) Research Interests: Stochastic PDEs, uncertainty quantification, Monte Carlo methods, Bayesian inverse problems, and numerical analysis of random fields. Her work bridges stochastic analysis and numerical simulations, addressing challenges in modeling and simulating complex systems with uncertainties. Grants & Funding: Principal Investigator in projects like 'Data-Integrated Simulation Science' (ExC 2075) and 'Quantitative Methods for Visual Computing' (SFB/TRR 161). Her research also explores applications in porous media, carbon dioxide storage, and optical flow analysis. Supervision: Advised PhD students including Oliver König, Fabio Musco, and Robin Merkle. Current students focus on topics like deep learning for stochastic PDEs and continuous level Monte Carlo methods.

Rainer HeintzmannView profile

Professor

• Super-resolution microscopy
• Optical microscopy
• Point spread function calculation
• +4 more

Prof. Dr. Rainer Heintzmann serves as Head of the Microscopy Department at the Leibniz Institute of Photonic Technology (Leibniz-IPHT) in Jena, Germany. His research focuses on advancing optical microscopy techniques, particularly super-resolution methods that surpass the diffraction limit to visualize cellular structures at nanoscale resolution. His primary research interests center on structured illumination microscopy (SIM), point spread function modeling, and computational imaging techniques. He has made significant contributions to developing automated multicolor SIM systems, extreme ultraviolet microscopy approaches, and deep learning-enhanced image analysis methods. His work bridges optical physics, computational algorithms, and biomedical applications, with particular emphasis on making advanced microscopy techniques more accessible through open-source hardware and software solutions. Analysis of his recent publications reveals a strong focus on overcoming fundamental limitations in optical microscopy. His research spans from theoretical modeling of optical systems to practical implementations for biological imaging. Key trends include the development of more accurate point spread function calculations, expansion of super-resolution techniques to new wavelength regimes, and integration of machine learning for image analysis and segmentation. Prof. Heintzmann actively collaborates with researchers across multiple institutions, as evidenced by his co-authorship on numerous interdisciplinary publications. His work has appeared in high-impact journals including Nature Methods, Nature Reviews Molecular Cell Biology, and Optics Express, reflecting the significance of his contributions to advancing microscopy techniques. His laboratory at Leibniz-IPHT appears to focus on developing novel microscopy instrumentation, particularly open-source implementations of super-resolution techniques. Recent projects include the openSIMMO platform for automated multicolor structured illumination microscopy and work on extreme ultraviolet microscopy that could potentially extend super-resolution capabilities into the X-ray regime.