جست‌وجوی استاد و پژوهشگر

Ute Susanne Kadenمشاهده پروفایل

پژوهشگر

• Ecology
• Environmental Science
• Conservation Biology
• +۵ مورد دیگر

Dr. Ute Susanne Kaden is a researcher at the Helmholtz Centre for Environmental Research (UFZ) , Department of Conservation Biology and Social-Ecological Systems, where she has worked since 2017. She completed her PhD at the University of Natural Resources and Life Sciences, Vienna in 2024, following an M.Sc. and B.Sc. in Geoecology from TU Bergakademie Freiberg (2016, 2014). Her research focuses on nitrogen dynamics in floodplains , denitrification quantification , and restoration of wetland complexes for climate adaptation and biodiversity conservation. Her work addresses critical projects such as: AZOTE : Nitrate retention in riparian zones ElBiota : Biodiversity in Elbe floodplains BioClim-Wetlands : Climate adaptation through wetlands Restore4Life : Wetland restoration in Danube Basin SpongeBoost : Green infrastructure for freshwater ecosystems Her peer-reviewed publications (2018–2024) demonstrate expertise in: Nitrogen cycling in groundwater-river interaction zones Quantifying denitrification potentials Hydromorphological controls on floodplain ecosystems Software tools for inundation mapping (QGIS plugin) Transboundary wetland restoration strategies

• Robotics
• Artificial Intelligence
• Autonomous Systems
• +۵ مورد دیگر

Prof. Ferrein is a Professor at the Institute for Mobile Autonomous Systems and Robotics (MASKOR) within the Faculty of Electrical Engineering and Information Technology at Aachen University of Applied Sciences. He has taught core courses including Fundamentals of Computer Science, Robotics, Artificial Intelligence, and Robot Programming with ROS for Electrical Engineering students from 2014 to 2016, establishing himself as a key educator in applied robotics. His research spans Robotics , Artificial Intelligence , and Industrial Automation , with concentrated expertise in logistics systems, mining automation, and smart manufacturing. As leader of the Carologistics RoboCup Logistics Team, he leverages competitive robotics to develop real-world factory automation solutions, focusing on multi-robot coordination, sensor fusion, and ROS-based implementations for industrial challenges. Analysis of his 2023-2025 publications reveals a decisive shift toward solving industry-specific problems: anomaly detection in metal-textile production, autonomous vehicle control in mining, and AI-supported work system design. His work consistently bridges academic research and industrial deployment through practical ROS integrations, emphasizing human-robot collaboration and cognitive load reduction in quality control workflows. MASKOR institute serves as the operational hub for Prof. Ferrein's activities, driving initiatives like the ROS Summer School and RoboCup Logistics League participation. This environment fosters industry-academia collaboration through educational programs and competitive benchmarks that validate cyber-physical systems for factory automation, with significant contributions to mining robotics and rescue operations.

• Regional climate and environmental modelling
• Climate impact research
• Geoinformatics and remote sensing
• +۶ مورد دیگر

Prof. Dr. Jürgen Böhner is a Full Professor at the University of Hamburg's Department of Earth System Sciences, where he leads research on climate-environment interactions. His office is located in the Geomatikum building (Room 814), with regular office hours on Thursdays from 3-4 PM. He coordinates the Climate, Climate Change and Society (CliCCS) Cluster of Excellence and directs the SAGA-GIS development initiative. His research focuses on: Regional climate modelling with emphasis on high-mountain and tropical systems Climate impact assessments for Himalayan treelines and Amazonian ecosystems Advanced geospatial methods through Geoinformatics and remote sensing SAGA-GIS software development for environmental applications Process-based analysis of land degradation and soil conservation His publications demonstrate interdisciplinary approaches combining climate modelling, geoinformatics, and field validation. Recent work emphasizes: High-resolution climate projections for forest inventories Glacial lake mapping using CubeSat technology Social-ecological tipping points in the Amazon Novel validation methods using social media data Irrigation impacts on regional climate systems He leads an extensive research portfolio including: TREELINE-II (DFG, 2023-2026): Himalayan treeline sensitivity to warming PRODIGY 2/BMBF (2023-2025): Amazon tipping point management CliCCS/DFG Excellence Cluster (2019-2025): Climate-society interactions LANDMATE (2018-2023): Human land surface modification feedbacks CHELSA (ongoing): High-resolution global climatologies He advises 17 researchers and students working on climate modelling, remote sensing, and environmental process analysis. His team collaborates with institutions across Europe, South Asia, and South America.

Thomas Mehnerمشاهده پروفایل

پژوهشگر

• Fish Ecology
• Limnology
• Evolutionary Ecology
• +۶ مورد دیگر

Dr. Thomas Mehner is a Senior Researcher and Working Group Leader at the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) in Berlin, where he leads the Research Group for Food Web Ecology and Fish Communities. He previously served as Vice-Director of IGB (2020-2024) and Deputy Director (2016-2019). Since 2012, he has held the position of Private Docent (PD) for Fish Ecology at Humboldt University zu Berlin, formally establishing his academic faculty status. His research expertise spans fish ecology and limnology, evolutionary ecology and population genetics, and behavioral ecology. His work focuses on freshwater ecosystems, particularly fish community structure, pond ecology, and the impacts of environmental changes on aquatic biodiversity. He is actively involved in multiple significant research projects including FishDistribution (modeling fish distributions under climate change), ODER~SO (investigating the Oder River environmental disaster), and PONDERFUL (as project lead, developing methods for maximizing the use of ponds in climate change adaptation). Analysis of his recent publications reveals a strong focus on freshwater biodiversity conservation, particularly regarding pond ecosystems and their role in supporting amphibian and fish species. His work increasingly addresses climate change impacts on freshwater systems, nature-based solutions for water management, and the integration of ecological research with policy implementation across European contexts. His research combines field studies with modeling approaches to understand complex ecological relationships in freshwater environments. Mehner has been actively publishing since the 1990s, with recent work appearing in high-impact journals including Hydrobiologia, Ecology of Freshwater Fish, and Biodiversity and Conservation. His collaborative approach is evident in the extensive international co-authorship networks across his publications. His career progression shows consistent advancement in research leadership, beginning with PhD studies at Rostock University (1989-1992), followed by postdoctoral positions at University of Technology Dresden and Innsbruck University, then joining IGB in 1997 as Deputy Head of Department where he has remained throughout his career.

• Engineering Geodesy
• Laser Scanning
• LiDAR
• +۴ مورد دیگر

Yihui Yang is a postdoctoral researcher at the Chair of Engineering Geodesy, Technical University of Munich (TUM), since August 2023. He holds a Ph.D. in Geodesy and Survey Engineering from the University of Stuttgart (2019-2023) and completed his M.Sc. and B.Sc. at Wuhan University and China University of Geosciences respectively. Education: Ph.D., Engineering Geodesy (2023), University of Stuttgart M.Sc., Geodesy and Survey Engineering, Wuhan University B.Sc., Surveying and Mapping Engineering, China University of Geosciences His research focuses on engineering geodesy , with expertise in laser scanning, LiDAR, point cloud processing , and deformation analysis . Key themes include georeferencing, uncertainty reduction, change detection , and 4D monitoring of geodetic data. Recent publications highlight advancements in Piecewise-ICP registration algorithms , patch-based deformation analysis , and targetless point cloud alignment . His work addresses systematic error mitigation in landslide monitoring and precision improvements for terrestrial laser scanning applications. In teaching, he supervises theses on point cloud registration and low-cost geospatial systems while contributing to courses in geodetic monitoring , engineering geodesy , and 3D digitalization .

• Climatology
• Landscape Ecology
• Alpine Shrub Ecology
• +۵ مورد دیگر

Dr. Svenja Dobbert is a Researcher and Lecturer at the Department of Geography, University of Bonn. Her primary affiliation is with the Löffler Research Group (RG Löffler). She specializes in Climatology and Landscape Ecology with a focus on Alpine shrub physiology, ecological modeling, and remote sensing applications. Her work investigates growth mechanisms of Mediterranean-alpine shrubs and arctic-alpine dwarf shrubs under environmental variability, particularly in Norway's LTAER-NO sites and Spain's Sierra Nevada. Research interests include dendroecology, habitat analysis, and the impacts of climate change on plant communities. She has conducted extensive fieldwork across Europe's alpine regions, producing datasets on shrub growth patterns and environmental controls. Her interdisciplinary approach combines ecological modeling with geospatial techniques, addressing questions about species distribution dynamics and ecosystem responses to global changes. Notable contributions include studies on bimodal growth patterns in alpine shrubs, environmental drivers of plant growth in contrasting biomes, and long-term datasets from Norway and Spain. Her research has advanced understanding of microclimate interactions and shrub encroachment processes in Arctic and Mediterranean ecosystems. Collaborations involve international teams studying alpine ecosystems, spiders' body size responses to snowmelt, and lynx habitat connectivity across Europe. She maintains active research profiles on ResearchGate and Google Scholar, with publications in journals like Erdkunde , Journal of Ecology , and Communications Biology .

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Jian Chen is a prolific academic researcher with affiliations across multiple institutions worldwide, primarily in China but also including international universities. The DBLP disambiguation page lists 47 distinct individuals with this name, working in various departments including Computer Science, Electrical Engineering, Telecommunications Engineering, and other technical fields at prestigious institutions such as Zhejiang University, Xidian University, Northeastern University, and Ohio State University. Research interests span a wide range of technical domains including artificial intelligence, machine learning, computer vision, network security, medical imaging, and electrical engineering. Recent publications indicate active research in deep learning applications, image processing, trajectory analysis, and biomedical informatics. The publication output is substantial, with numerous papers appearing in IEEE Access, Pattern Recognition, and other high-impact journals. The research trends show a strong focus on practical AI applications across multiple domains, with particular emphasis on medical imaging, network security, and intelligent control systems. Many publications involve collaborative work with researchers across different institutions, indicating an active research network. While specific awards are not listed in the provided DBLP information, the volume and quality of publications suggest recognition within the academic community. The research output demonstrates expertise in both theoretical development and practical implementation of computational methods. Jian Chen appears to be actively mentoring students, as evidenced by the numerous collaborative publications, though specific student names aren't detailed in the DBLP records. The research spans both fundamental algorithm development and applied problem-solving across various engineering and computer science domains.

Andreas Martin Lisewskiمشاهده پروفایل

مدرس ارشد

• Computational Life Sciences
• Synthetic Biology
• Biodigital Convergence
• +۷ مورد دیگر

Dr. Andreas Martin Lisewski is a Senior Lecturer of Science at the School of Science, Constructor University. His research focuses on computational life sciences and synthetic biology, particularly analyzing genomic information in infectious agents and exploring the biodigital convergence between molecular biology and digital communication theory. He investigates synthetic biology's societal impacts, including ethical implications and risks associated with bioengineering advancements. Dr. Lisewski holds an MSc from the University of Hamburg and a PhD from the Technical University of Munich. He has received prestigious awards such as the Sigma Xi (2025) and DAAD Rückgewinnung Stipendium (2015-2016). His teaching portfolio includes courses on synthetic biology, global health, and ethics in science and technology. His research outputs include groundbreaking studies on SARS-CoV-2 genomic analysis, vaccine effectiveness, and computational methods for protein function prediction. He serves as a Guest Editor for International Journal of Molecular Sciences and Academic Editor for Microbial Cell . Key research themes include: Decoding genomic information using digital signal processing principles Development of computational tools to distinguish synthetic from natural genomes Thermostability design in proteins for bioengineering applications Evaluation of synthetic biology's societal and environmental risks

Andreas Schmidtمشاهده پروفایل

پژوهشگر

• Computer Science
• Artificial Intelligence
• Business Management
• +۳ مورد دیگر

Andreas Schmidt is affiliated with the University of Kassel, Germany, and holds a PhD in Computer Science from the University of Koblenz and Landau (2022). His research spans interdisciplinary domains including artificial intelligence, business management, and neuroscience. He has published extensively in journals like NeuroImage, SIAM Journal on Scientific Computing, and Remote Sensing of Environment, demonstrating expertise in computational methods, neural networks, and applied data science. Key research themes include propagation network modeling, home-office productivity analysis, and autonomous driving safety frameworks. His work integrates technical innovation with practical applications, such as optimizing district heating systems and advancing neurovascular coupling studies in primates. While no formal awards are listed, his contributions reflect impactful interdisciplinary collaboration across computer science, environmental science, and medical imaging.

• Permafrost Science
• Environmental Monitoring
• Soil Science
• +۳ مورد دیگر

Niko Bornemann is a Researcher in Permafrost Research at the Alfred Wegener Institute (AWI) in Potsdam, Germany. His primary role involves managing long-term observations of meteorological, soil, and hydrological conditions at key Arctic sites, including Samoylov Island (Lena Delta, Siberia) and Bayelva (Svalbard). He oversees a soil and sensor testing laboratory, supporting field deployments and data collection for projects such as SWESOS and MOMENT. His work focuses on understanding permafrost dynamics, climate interactions, and environmental change in Arctic regions. Bornemann contributes to international collaborations, publishing datasets on permafrost thickness, thermal regimes, and snow-water equivalents. His research supports initiatives like the SIOS observatory network, emphasizing real-time monitoring and data quality standards. Affiliations: AWI Permafrost Research Team, Polar Terrestrial Environmental Systems Section Key Projects: SWESOS (Snow Water Equivalent System), MOMENT Arctic Expeditions, Yedoma Landscape Studies Research Interests: Long-term environmental monitoring, permafrost-ecosystem interactions, Arctic cryosphere dynamics, and sensor technology applications. His fieldwork integrates geophysical and meteorological data to assess climate impacts on permafrost stability and associated feedbacks.

• Software Engineering
• Data Management
• Interoperability Systems
• +۳ مورد دیگر

Dr. Roland Koppe is Head of Software Engineering at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI), leading the Architecture and Portal Solutions team within the 'From Observations to Archives and Analysis (O2A)' group. His work focuses on software engineering, interoperability of heterogeneous systems, and data modeling to enable seamless data flow from sensor observations to archival and publication systems. He is a key contributor to initiatives such as the Data Portal German Marine Research, the O2A framework, and the CRITTERBASE biodiversity data warehouse. Research Interests: Software Engineering methodologies for scientific data systems, engineering management of large-scale projects, interoperability standards for marine and climate data, and FAIR data principles implementation in collaborative frameworks. His work addresses challenges in managing heterogeneous sensors, automating quality control, and integrating data across marine, polar, and climate research domains. Developed the O2A framework to streamline sensor data workflows Core contributor to the Data Portal German Marine Research Architect of CRITTERBASE for polar marine biodiversity data Lead software engineering for MOSAiC Arctic expedition data management Contributed to Digital Earth initiatives and MOSES flood modeling projects Publications focus on sensor data management systems, FAIR implementation, and interdisciplinary data integration challenges in polar and marine research. His work emphasizes scalable solutions for heterogeneous data sources and collaborative scientific workflows.

• Wireless Communications
• 5G/6G Channel Modeling
• UAV Communications
• +۵ مورد دیگر

Ziwei Huang is an Assistant Professor at Southeast University's School of Information Science and Engineering, Department of Communication Engineering. With a prolific publication record from 2019-2025, Huang has established expertise in wireless communications, particularly in 5G/6G channel modeling, UAV communications, and vehicular networks. Recent work demonstrates a strategic expansion into AI/ML applications, computer vision, and large language models, showing interdisciplinary research growth. Huang's research interests focus on wireless communications channel modeling with particular emphasis on non-stationary characteristics, spatial consistency, and trajectory modeling for next-generation communication systems. Key areas include UAV communications , vehicular networks , intelligent sensing-communication integration , and multi-modal data fusion . Recent work has expanded into fundus image processing , text-to-image synthesis , and hallucination mitigation in vision-language models , demonstrating a strategic expansion into AI applications while maintaining core expertise in communications. The publication trends reveal a clear evolution from traditional wireless channel modeling (2019-2021) toward more integrated sensing-communication systems (2022-2023), with a significant pivot toward AI/ML applications in 2024-2025. Huang's work spans both theoretical channel modeling and practical implementation, with increasing emphasis on cross-disciplinary applications. The research shows strong collaboration patterns with Xiang Cheng and Lu Bai, suggesting membership in a well-established research group at Southeast University. Huang has contributed to numerous high-impact publications in IEEE journals including IEEE Transactions on Wireless Communications, IEEE Transactions on Vehicular Technology, and IEEE Communications Surveys & Tutorials, as well as top conferences like AAAI and ACL. The research demonstrates consistent funding support through collaborative projects focused on next-generation wireless communication systems. The work spans multiple laboratories and research teams, including wireless communications research groups at Southeast University, collaborations with medical imaging researchers for fundus analysis, and partnerships with AI research teams working on vision-language models. Recent publications suggest active participation in interdisciplinary research initiatives bridging communications engineering with artificial intelligence.

• Machine Learning
• Computer Vision
• Data Science
• +۳ مورد دیگر

Xiaoxue Li is a computer science researcher with a robust publication record spanning 2008-2025. She contributes to diverse fields including Machine Learning , Computer Vision , and Data Science , with recent 2025 work on neural rendering (MoNeRF) and light field image quality assessment. 2024 publications focus on stress reduction databases , asymmetric distribution learning for museum applications, and LIDAR data integration for biomass estimation. Her 2023-2021 output includes fault identification via deep belief networks and micro-expression recognition systems. Earlier works (2008-2020) address polynomial discriminant systems , graph attention networks , and network embedding for heterogeneous systems. Her research trends show increasing specialization in generative AI and graph-based learning applied to both computer vision and physical systems .

• Remote Sensing using Unmanned Aerial Systems
• Multispectral Image Analysis
• Object-based Image Analysis (OBIA)
• +۲ مورد دیگر

Dr. Jan RK Lehmann is a Postdoc researcher at the University of Münster, affiliated with the Institute of Landscape Ecology. He holds dual roles in the Applied Landscape Ecology and Ecological Planning Group and the Remote Sensing and Spatial Modelling Research Group. His work focuses on advancing remote sensing techniques using Unmanned Aerial Systems (UAS) for ecological and environmental applications. Lehmann completed his PhD in Landscape Ecology at the University of Münster in 2017, with a thesis titled *"Unmanned Aerial Systems in Landscape Ecology - New Remote Sensing Capabilities and Perspectives for Scalable Environmental Applications and Analyzes."* His research integrates multispectral image analysis, object-based classification, and studies on invasive species in Brazilian and Mediterranean ecosystems. He has contributed to projects like BIOBRAS (DAAD-funded research in Brazilian ecosystems) and INSPECTED.NET (EU Marie Curie-funded invasive species network). His recent publications emphasize drone-based monitoring of plant species, peatland moisture dynamics, and cryptogamic soil cover in polar regions. Lehmann teaches courses on remote sensing methods, field techniques, and environmental planning. He supervises student projects and offers field equipment rentals through the Institute’s platform. His work bridges ecological research, spatial modeling, and practical applications in conservation and environmental management.

• Computer Vision
• Machine Learning
• Image Processing
• +۵ مورد دیگر

Dr. Xin Lin is a Professor at the School of Computer Science and Technology, University of Science and Technology of China in Hefei. With an extensive publication record spanning computer vision, machine learning, and artificial intelligence, Dr. Lin leads a research group focused on solving challenging problems in image processing, robotics, and wireless communications. His work bridges theoretical advancements with practical applications across healthcare, autonomous systems, and industrial manufacturing. Dr. Lin's research interests encompass computer vision, machine learning, image processing, and artificial intelligence, with particular expertise in image restoration, 3D object detection, and human pose estimation. His laboratory develops innovative approaches to handle multiple image degradations simultaneously and create lightweight, efficient vision systems suitable for real-world deployment. The research demonstrates strong interdisciplinary connections, applying computer vision techniques to medical imaging, satellite communications, and industrial IoT applications. Analysis of Dr. Lin's recent publications reveals a strong focus on multi-task learning approaches that address multiple image degradation problems simultaneously. His work shows increasing sophistication in handling complex real-world scenarios, from low-light conditions to rain interference, while maintaining computational efficiency. The research trajectory demonstrates a clear path from fundamental image processing techniques to practical applications in autonomous driving, healthcare, and industrial systems. Dr. Lin has received recognition for his contributions to the field through numerous publications in top-tier venues including CVPR, IEEE Transactions, and ACL. His work on image restoration, particularly the Dual Degradation Representation framework, has gained significant attention in the computer vision community. Dr. Lin actively supervises graduate students and collaborates with researchers worldwide. His laboratory works on cutting-edge projects involving digital twins for manufacturing, satellite communications, and medical imaging applications. Current research directions include developing more robust and efficient models for real-world deployment scenarios, with particular attention to resource-constrained environments.