جست‌وجوی استاد و پژوهشگر

Ziyue Liمشاهده پروفایل

استادیار

• High-dimensional Data Mining
• Statistics
• Machine Learning
• +۲ مورد دیگر

Jun. Prof. Dr. Ziyue Li is a Junior Professor in Machine Learning in Smart Markets at the Information Systems Department of WiSo Faculty, University of Cologne, Germany (2022–present). They also serve as Chief Machine Learning Scientist at EWI, Germany. Their academic career includes researcher positions at Hong Kong Science and Technology Park Corporation/SenseTime (2021–2022), Nokia Bell Labs (2019), and doctoral studies at The Hong Kong University of Science and Technology (2017–2021). Dr. Li's research focuses on high-dimensional data mining , machine learning , and smart mobility . Their work combines tensor analysis, graph modeling, and spatiotemporal prediction to solve complex problems in transportation systems and data analytics. They have developed innovative approaches for passenger flow prediction and travel pattern analysis. Their publications demonstrate a strong focus on tensor-based machine learning methods applied to mobility data. Key trends include Integration of graph theory with tensor decomposition Development of spatiotemporal prediction models Applications in urban transportation analytics Hybrid transfer learning approaches Multi-clustering methods for travel pattern analysis Data completion techniques for complex networks Scientific recognition includes Multiple INFORMS Data Mining Section awards IEEE CASE Best Conference Paper Award Hong Kong Ph.D. Fellowship Scholarship HKUST Excellent Research Award Three Minute Thesis Competition recognition

• Applied Mathematics
• Computational Methods
• Control Theory
• +۵ مورد دیگر

Sara Grundel is a leading researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Her work focuses on computational methods in systems and control theory, particularly in model order reduction, gas network simulation, and optimization of energy systems. Education: Diplom in Mathematics, ETH Zurich (2005) PhD in Mathematics, Courant Institute of Mathematical Sciences, New York University (2011) Research Interests: Sara’s research encompasses mathematical control theory, stability analysis, and numerical methods for differential-algebraic equations. She applies these techniques to gas and energy networks, epidemic modeling, and multi-agent systems. Her interdisciplinary work bridges computational mathematics with real-world engineering and public health challenges. Recent Publications: Her 15 most recent articles (2024–2012) demonstrate expertise in parametrized PDEs, model reduction for coupled systems, and control strategies for SARS-CoV-2 containment. Key subtopics include adaptive meshing, stability-preserving algorithms, and optimization of nonlinear network dynamics. Scientific Contributions: Developed clustering-based model reduction techniques for networked systems Investigated hyperbolic discretization methods using Riemann invariants Advanced polynomial root radius optimization with affine constraints Collaborations: Sara frequently collaborates with researchers like Peter Benner and Martin Gersen on energy grid simulations and control theory. She participates in international conferences (GAMM, IEEE CDC, MTNS) and contributes to edited volumes in applied mathematics.

Tomislav Henglمشاهده پروفایل

پژوهشگر

• Machine Learning
• Environmental Data Analysis
• Soil Science
• +۹ مورد دیگر

Dr. Tomislav Hengl is a leading researcher and Technical Director at OpenGeoHub Foundation and Envirometrix BV, specializing in spatial statistics, machine learning, and environmental data science. With over 20 years of experience in predictive soil mapping and geostatistics, he has pioneered open source frameworks for automated global environmental mapping. Co-founder of OpenGeoHub Foundation Initiator of OpenGeoHub Summer Schools (running since 2007) Project leader of OpenLandMap system Recipient of Clarivate Highly Cited Researcher (2021) His research focuses on: Machine learning for spatial/spatiotemporal data Environmental data cube systems Global soil and vegetation mapping Open source geospatial software development Spatio-temporal predictive modeling Cloud computing for Earth observation data Recent research trends include: Development of high-resolution global terrain models Analysis of vegetation productivity using satellite time-series Ensemble machine learning for environmental mapping Integration of multi-source geospatial datasets Applications in climate change impact assessment Advancing open data infrastructures Scientific contributions include: Clarivate Highly Cited Researcher (2021) Over 60 journal publications Founding Vice-Chair of the International Society for Geomorphometry (2011-2015) Development of open source R packages for geospatial analysis

• High Performance Computing
• Performance Engineering
• Performance Modeling
• +۷ مورد دیگر

Gerhard Wellein is a Professor for High Performance Computing at the Department of Computer Science of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He is the head of NHR@FAU (Erlangen National Center for High Performance Computing) and a member of the board of directors of the German NHR-Alliance. Since 2024, he has also served as a Visiting Professor for HPC at the Delft Institute of Applied Mathematics, Delft University of Technology. He holds a PhD in theoretical physics from the University of Bayreuth and has over two decades of experience in HPC education and research. Research Interests: His research focuses on performance modeling and engineering, architecture-specific code optimization, novel parallelization techniques, and the development of hardware-efficient building blocks for sparse linear algebra and stencil solvers. His work bridges computer science, applied mathematics, and computational physics, aiming to maximize efficiency on current and future HPC architectures, including exascale systems. Publication Trends: His recent publications emphasize analytical performance modeling (e.g., Roofline, oscillator models), energy efficiency, GPU optimization, and scalable linear algebra. They reflect a strong focus on both theoretical modeling and practical implementation, with applications in CFD, quantum physics, and molecular dynamics. Scientific Awards: 2011 Informatics Europe Curriculum Best Practices Award (shared with Jan Treibig and Georg Hager) for outstanding teaching contributions in HPC. Grants and Advising: He has led numerous third-party funded projects from the EU, BMBF, and DFG, including EoCoE-III, ESSEX, EXASTEEL, and ProPE. These projects focus on exascale software, performance engineering, fault tolerance, and multiscale simulation. He has mentored multiple researchers and students, contributing to the development of tools such as LIKWID, ClusterCockpit, and GEOPM. Labs and Teams: He leads the HPC research group at FAU and is deeply involved in national and international HPC initiatives. His team collaborates extensively on open-source HPC software and performance tools, fostering a strong community-driven approach to performance engineering.

• High Performance Computing
• Software Engineering for HPC
• Code Generation
• +۱۲ مورد دیگر

Harald Köstler is an Associate Professor and Head of Research at the Erlangen National High Performance Computing Center (NHR@FAU) within the Department of Computer Science at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He leads the research group on HPC Software Design at the Chair of Computer Science 10 (System Simulation), focusing on software engineering for high-performance computing and data analytics. His research interests include: Software Engineering for HPC Code Generation for Numerical Solvers Performance Engineering on Hybrid Architectures Discontinuous Galerkin and Lattice Boltzmann Methods Multigrid Solvers and Parallel Algorithms Performance Portability across CPUs, GPUs, and FPGAs The recent publications highlight a strong trend in developing efficient, scalable, and portable simulation frameworks for complex physical systems. His work emphasizes code generation, performance optimization, and the integration of classical model-driven and data-driven approaches. Key application areas include computational fluid dynamics, geotechnical engineering, and climate modeling, often leveraging the waLBerla and ExaStencils frameworks. Harald Köstler has no listed scientific awards in the provided text. He advises students in the areas of high-performance computing, numerical methods, and software engineering for scientific applications. His research is supported by collaborations within the FAU HPC ecosystem and likely involves grants related to national high-performance computing initiatives. He is a key contributor to the waLBerla framework, a block-structured, high-performance software for multiphysics simulations, and is involved with the ExaStencils project, which focuses on advanced multigrid solver generation. These frameworks form the core of his research team's efforts in scalable scientific computing.

Ingolf Kühnمشاهده پروفایل

استاد

• Macroecology
• Plant Invasion Ecology
• Functional Traits
• +۱۲ مورد دیگر

Ingolf Kühn is a Professor of Macroecology at Martin-Luther University Halle-Wittenberg and Head of the Department of Community Ecology at the Helmholtz Centre for Environmental Research (UFZ) in Halle, Germany. He is also a member of the German Centre for Integrative Biodiversity Research (iDiv) and holds a fellowship at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). His research is centered on plant invasions, functional traits, and biodiversity responses to global change, with a strong methodological focus on spatial and phylogenetic modeling. His research interests span macroecology , plant invasion dynamics , urban and alpine flora , and data-driven ecological modeling . He has led major projects such as Biodiversity Meets Data (BMD) , eLTER PLUS , and AlienScenarios , and contributed to EU frameworks like EuropaBON and DAISIE . He is deeply involved in developing and managing large databases, including BiolFlor and the TRY Plant Trait Database . His recent publications focus on functional traits of invasive plants, biodiversity digital twins, and climate-driven shifts in species distributions. They reflect a strong trend toward integrating big data, machine learning, and macroecological theory to predict ecological change. Scientific Awards: Highly Cited Researcher (2014–2022) Fellow of the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) He serves as Editor-in-Chief of NeoBiota and Associate Editor of Journal of Vegetation Science . His advising spans PhD students and postdocs in the GLIMPSE cohort and iDiv projects. He leads multiple long-term monitoring initiatives, including alpine glacier forefield studies in the Dachstein and Berchtesgaden regions. He is affiliated with key research teams and platforms such as: Macroecology & Vegetation Science Working Group iDiv Science Strategy Board Task Forces: sTWIST, sUMMITDiv, sCoMuCra, sREGPOOL eLTER Research Infrastructure Biodiversity Digital Twin initiative

Jatinder Singhمشاهده پروفایل

استاد

• Accountability in AI Systems
• Responsible AI Development
• Data Governance and Stewardship
• +۷ مورد دیگر

Jatinder Singh is a Professor at the RC Trust and Principal Research Associate (equivalent to Research Professor) at the Department of Computer Science & Technology, University of Cambridge. He is primarily affiliated with the University of Duisburg-Essen, Germany, where he leads the Compliant and Accountable Systems research group within the Law department. His work operates at the critical intersection of computer science, legal frameworks, and societal impact, focusing on practical implementations that align technology with regulatory requirements while addressing user and community concerns. Research interests center on accountability mechanisms for AI systems, responsible development practices, data governance, and privacy/security in emerging technologies. He examines governance, agency, trustworthiness, and transparency gaps in algorithmic systems through interdisciplinary socio-technical lenses. Current work addresses bias in LLMs, stakeholder participation frameworks, and human rights implications in domains like healthcare, maritime enforcement, and consumer IoT, emphasizing contextual awareness and real-world applicability. His 15 most recent publications (2025-2024) reveal dominant trends in AI transparency, fairness proxy development, and legal-compliance engineering. Key focus areas include stakeholder involvement in AI governance, bias mitigation in language models, data justice applications for vulnerable populations, and operationalizing human-centered AI in clinical settings. The work consistently bridges technical implementation with regulatory frameworks like the EU Cyber Resilience Act and GDPR. Scientific Awards: No awards or fellowships were mentioned in the provided text. Advising and Grants: The text does not specify PhD/Master's students or grant details. As leader of an active research group publishing high-impact work on EU regulations and human rights, he likely directs funded projects and mentors early-career researchers, though concrete evidence is absent in the source material. His position suggests involvement in interdisciplinary grant consortia addressing socio-technical challenges. Labs and Teams: Singh leads the Compliant and Accountable Systems research group at University of Duisburg-Essen, which collaborates across university-wide clusters including Artificial Intelligence and Society, Human-AI Interaction, Trustworthy Human Language Technologies, and Verification of Machine Learning. The group develops frameworks for legal compliance in AI, focusing on demonstrable accountability through tools for transparency, bias auditing, and stakeholder engagement in real-world deployments.

Ranit Deمشاهده پروفایل

پژوهشگر

• Global carbon cycle
• Eddy covariance
• Model-Data assimilation
• +۶ مورد دیگر

Ranit De is a Doctoral Researcher in the Department of Biogeochemical Integration at the Max Planck Institute for Biogeochemistry in Jena, Germany. De is affiliated with the International Max Planck Research School for Global Biogeochemical Cycles (IMPRS-gBGC) and works in the Model-Data Integration research group under Dr. Nuno Carvalhais. De's educational background includes: Ph.D. candidate (2021-present) at the International Max Planck Research School for Global Biogeochemical Cycles M.Sc. in Geo-information Science and Earth Observation (Water Resources and Environmental Management) from the University of Twente, Netherlands (2019-2021) B.Tech. in Agricultural Engineering from Sam Higginbottom University of Agriculture, Technology and Sciences, India (2015-2019) De's research focuses on understanding the global carbon cycle through advanced hybrid modeling techniques that combine physically based models with machine learning approaches. De specializes in simulating gross primary production at sub-daily scales using eddy-covariance data and light use efficiency-based models, with particular expertise in understanding interannual variability in terrestrial ecosystem fluxes. Analysis of De's recent publications reveals a strong research trajectory examining how temporal variation of hydrological parameters can improve model performance, investigating parametric uncertainties in land surface models, and exploring the relationship between spatiotemporal variability of model parameters and their controlling factors. De's work bridges the gap between traditional physical modeling and modern data-driven approaches. De has presented research at notable conferences including the EGU Mary Anning conference in June 2025 and the ICOS Science Conference 2024, demonstrating active engagement with the global scientific community. De's methodological innovations in model-data integration contribute significantly to improving biogeochemical models for better climate predictions and understanding carbon cycle dynamics under changing environmental conditions.

Chris-Carolin Schönمشاهده پروفایل

استاد

• Plant Genetics
• Agricultural Biotechnology
• Genomic Selection
• +۳ مورد دیگر

Prof. Chris-Carolin Schön is a full Professor of Plant Breeding at the Technical University of Munich (TUM), holding her position since 2007. She is affiliated with the TUM School of Life Sciences and leads research on molecular biology integration in agricultural processes, focusing on molecular markers in crop breeding, biodiversity characterization, and optimized breeding strategies for complex traits. Her academic journey includes studies in agricultural sciences at Hohenheim University and Oregon State University, followed by postdoctoral work at Hohenheim University and a leadership role at the State Plant Breeding Institute there. Prof. Schön’s research emphasizes enhancing crop resilience through genomic tools, particularly in maize, with studies on drought tolerance, cold adaptation, and genetic diversity utilization. She pioneered Synbreed, an interdisciplinary network for genome-based breeding research in plants and animals. Her awards include the Bavarian State Medal (2024), an Honorary Doctorate from Heinrich-Heine-University (2024), and the Bavarian Order of Merit (2018). Her work spans genomic prediction models, haplotype analysis, and improving imputation quality in genomic data. Key projects include exploiting European maize landraces for resistance traits and developing libraries of doubled-haploid lines to safeguard genetic resources. She collaborates on initiatives like AgroClustEr for synergistic plant and animal breeding, and her lab focuses on traits like photosynthetic efficiency, root system adaptation, and protein networks in crops.

Julija Zavadlavمشاهده پروفایل

استادیار

• Multiscale Modeling
• Machine Learning
• Computational Chemistry
• +۳ مورد دیگر

Prof. Julija Zavadlav is an Assistant Professor of Multiscale Modeling of Liquid Materials at the Technische Universität München (TUM), affiliated with the TUM School of Engineering and Design. Her research integrates physical modeling with machine learning and Bayesian techniques to develop multi-scale simulation frameworks for diverse applications in bioinformatics and engineering. Education: She earned her PhD in Physics from the University of Ljubljana (2015) and conducted postdoctoral research at ETH Zurich (2016–2019), where she received an ETH Postdoctoral Fellowship. Since 2019, she has held her current position at TUM. Research Interests: Her work focuses on advancing machine learning potentials, Bayesian uncertainty quantification, and multi-scale modeling for complex systems like ionic liquids, metal-organic frameworks, and biomolecules. Her ERC Starting Grant (2022) supports the SupraModel project, emphasizing scalable and interpretable models. Awards: Golden Teaching Award 2022 (Best Lecture), ERC Starting Grant 2022, and ETH Postdoctoral Fellowship. Her recent publications emphasize neural network potentials, transfer learning, and computational tools like JaxSGMC for Bayesian analysis. Grants and Labs: While specific lab names are not mentioned, her ERC grant underscores active funding. No formal student advisee list is provided, but her collaborative work suggests involvement in training next-generation computational scientists.

Rüdiger von Eisenhart-Rotheمشاهده پروفایل

استاد

• Regenerative Medicine
• Osteoarthritis Research
• Osteo-Oncology
• +۷ مورد دیگر

Prof. Dr. Rüdiger von Eisenhart-Rothe is a full Professor and Chair of Orthopedics at the Technische Universität München (TUM), leading the Department of Orthopedics and Sports Orthopedics at the Klinikum rechts der Isar. His research focuses on regenerative medicine, osteo-oncology, endoprosthetics, and the application of machine learning in surgical planning. He completed his medical studies at LMU Munich and business administration at the University of Hagen, followed by a habilitation in orthopedics at Frankfurt’s Friedrichsheim Hospital. Notably, he received the Perthes Prize twice (2003, 2010) for contributions to shoulder and elbow surgery. His work integrates advanced imaging techniques, virtual planning, and biomaterial research to address challenges in joint replacement, infection control, and sarcoma management. Recent projects emphasize AI-driven diagnostic tools and personalized surgical approaches. Prof. von Eisenhart-Rothe collaborates with interdisciplinary teams to advance clinical outcomes in orthopedic surgery, particularly in knee and hip arthroplasty. Awarded the Perthes Prize twice, his contributions span academic leadership, clinical innovation, and translational research at TUM’s School of Medicine and Health. Current initiatives include optimizing prosthetic alignment via 3D modeling and evaluating synovial biomarkers for infection diagnosis.

• Thermodynamics of radionuclides
• Actinide chemistry
• Nuclear waste repositories
• +۷ مورد دیگر

Prof. Dr. Vinzenz Brendler serves as Head of the Department of Thermodynamics of Actinides at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) Institute of Resource Ecology. He also holds an Honorary Professor position in Radiochemistry at the University of Applied Science Dresden (HTW) since 2008. His career at HZDR has spanned over three decades, progressing from Junior Scientist (1993-2000) to his current leadership role, with previous positions including Head of Department Surface Processes (2013-2021) and Acting Director of the Institute of Resource Ecology (2012-2013). Dr. Brendler received his Ph.D. in natural sciences from TU Bergakademie Freiberg in 1993 with distinction ("Magna cum laude") and completed his Diploma in Chemical Engineering there in 1989 with "Excellence" honors. His doctoral work focused on water activities in ternary salt systems, while his diploma research examined reaction models for activity data in lithium-zinc chloride systems. His research focuses primarily on the thermodynamics of radionuclides in aqueous solutions and at mineral-water interfaces, with direct applications to nuclear waste repositories. He possesses extensive expertise in experimental capabilities using modern analytical spectroscopic methods, including laser-induced fluorescence and X-ray absorption techniques. His work encompasses geochemical modeling, radionuclide migration studies, sorption processes, risk assessment methodologies, and database development for environmental modeling. Analysis of his recent publications reveals a consistent focus on actinide and f-element chemistry, particularly uranium, europium, and technetium systems. His research employs sophisticated combinations of experimental methods including spectroscopy, batch experiments, and computational modeling to understand complex geochemical processes relevant to nuclear waste disposal. Key themes include surface complexation modeling, redox processes, mineral-water interactions, and thermodynamic characterization under repository-relevant conditions. Agricola medal of the TU Bergakademie Freiberg for outstanding diploma work As an Honorary Professor at HTW Dresden, Dr. Brendler contributes to radiochemistry education while maintaining an active research program. His extensive publication record (over 100 papers) demonstrates significant contributions to the field of nuclear waste management. He serves on multiple advisory boards including the Technical Direction Team of the NEA Sorption Project and the Governing Board of EU framework projects. His research is supported by numerous national and international funding programs including EU R&D framework programs and German federal initiatives. Dr. Brendler leads research within the Thermodynamics of Actinides department at HZDR, collaborating with various research teams across multiple facilities including the Rossendorf Beamline at ESRF. His work integrates experimental, theoretical, and modeling approaches to address complex challenges in nuclear waste disposal safety assessment.

• Cloud Computing Economics
• Software Service Platforms
• IT Service Economics
• +۶ مورد دیگر

Jörn Altmann is a Professor in the Department of Computer Science and Engineering at Seoul National University's College of Engineering. With a publication history spanning over three decades from 1994 to 2025, he has established himself as a leading researcher in the economics of computing services, particularly focusing on cloud computing economics, software service platforms, and IT service ecosystems. He has served as editor for the GECON (Economics of Grids, Clouds, Systems, and Services) conference proceedings for multiple years, demonstrating his leadership in the field. Altmann's research interests center on the economic aspects of computing infrastructure, with particular expertise in cloud federation models, resource allocation mechanisms, and the business dynamics of software service ecosystems. His work bridges technical computing concepts with economic theory, examining how market mechanisms can be applied to computing resource allocation. Recent research has expanded into cybersecurity economics, AI adoption frameworks, and knowledge management in distributed systems environments. His publications consistently explore the intersection of technology adoption patterns and economic incentives within computing ecosystems. Analysis of his recent publications reveals a strong focus on cloud federation architectures, trust mechanisms in cybersecurity information sharing, and the economic modeling of value creation in distributed computing environments. His work demonstrates a methodological diversity, employing game theory, agent-based modeling, systematic literature reviews, and empirical analysis of industry dynamics, particularly in the Korean technology sector. Altmann has collaborated extensively with researchers worldwide, with notable long-term collaborations with José Ángel Bañares, Omer F. Rana, Kurt Vanmechelen, and Korean researchers including Kibae Kim. His work has appeared in prestigious venues including Future Generation Computer Systems, IEEE Transactions on Engineering Management, and the GECON conference series which he has helped shape through editorial leadership. His research has practical implications for cloud service providers, enterprise IT strategy, and policy development in digital infrastructure. The consistent publication record and editorial leadership suggest active supervision of graduate students and research teams, though specific students are not documented in the provided publication records.

Lisa Adamsمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence in Radiology
• Quantitative Imaging Biomarkers
• Oncological Imaging
• +۵ مورد دیگر

Dr. Lisa Adams is a Research Fellow at the Technical University of Munich (TUM) and an Attending Radiologist at TUM University Hospital Rechts der Isar, affiliated with the Diagnostic and Interventional Radiology department. She holds an Albrecht Struppler Clinician Scientist Fellowship (2024) and leads research in the Quantitative Imaging Biomarkers for Predictive Healthcare focus group. Adams earned her MD from Charité - Universitätsmedizin Berlin (2016), completed board certification in radiology (2021), and conducted postdoctoral research at Stanford University (2022-2023). She transferred her Habilitation in Experimental Radiology to TUM in 2024. Her research integrates AI with radiology diagnostics, specializing in: Developing machine learning methods for early disease detection and risk assessment Advancing quantitative imaging biomarkers across organ systems Body composition analysis and biological age estimation Multimodal analysis for personalized healthcare Ethical implementation of AI in clinical practice Adams' recent publications demonstrate strong focus on AI applications in radiology, spanning large language models for clinical reporting, deep learning for medical image interpretation, nanoparticle tracking in regenerative medicine, and radiomics for cancer diagnosis. Her work consistently bridges technical innovation with clinical translation. Awards & Recognition: Walter-Friedrich-Prize, Deutsche Röntgengesellschaft (2023) Alavi Mandell Award (2021) Invest in the Youth Stipend, ECR Vienna (2017) She actively mentors doctoral candidates, having supervised seven PhD students to completion since 2020 with two more nearing submission. Adams secures substantial research funding from DFG, EU, Wilhelm Sander Foundation, Bayern Innovativ, and Berlin Institute of Health. She serves as Scientific Editor for European Radiology and Trainee Editorial Board Member for Radiology: Artificial Intelligence , while also holding leadership positions in the German Radiological Society.

Ursula Gaedkeمشاهده پروفایل

استاد

• Ecological Theory
• Plankton Ecology
• Food Web Dynamics
• +۷ مورد دیگر

Professor Dr. Ursula Gaedke is a leading academic at the University of Potsdam, where she heads the research group Ecology and Ecosystem Modelling within the Institute of Biochemistry and Biology. Her work integrates field observations, laboratory experiments, and mathematical modeling to advance ecological theory and address pressing environmental issues such as biodiversity loss and ecosystem resilience. Her research focuses on plankton communities and food web dynamics, with a strong emphasis on trait-based approaches and seasonal patterns in natural systems like Lake Constance. She leads the DFG Priority Programme DynaTrait and is involved in the DFG Research Training Group BioMove, emphasizing individual-based and adaptive ecological modeling. The recent publications of her group reveal a consistent trend toward understanding how trait dynamics, trade-offs, and trophic interactions shape community stability and biodiversity. Her work increasingly bridges empirical data with theoretical models, especially in the context of seasonal shifts, self-organized pattern formation, and multitrophic interactions. The research spans from microbial plankton to complex food webs, often using long-term datasets and advanced analytical techniques. She supervises a dynamic team of PhD and master’s students, including Lilla Zsuzsanna Kiss, Louica Philipp, Catalina Rios Henriques, Julia Pawlak, and Edvin Löthgren. Former students like Dr. Elias Ehrlich have continued successful research careers. Her leadership in large collaborative projects underscores her central role in theoretical and quantitative ecology in Germany. Prof. Gaedke has been instrumental in building one of the largest theoretical ecology groups in Germany, combining empirical rigor with innovative modeling to explore how ecosystems respond to environmental change.