جست‌وجوی استاد و پژوهشگر

Florian Betzمشاهده پروفایل

پژوهشگر

• Biogeomorphology
• Interdisciplinary River Science
• Remote Sensing
• +۶ مورد دیگر

Dr. Florian Betz is a Researcher in the Department of Remote Sensing at the Institute of Geography and Geology , University of Würzburg. He leads the Earth Observation Research Cluster as Principal Investigator since 2024, focusing on satellite-based environmental monitoring. Education: M.Sc. in Environmental Processes and Natural Hazards, Katholische Universität Eichstätt-Ingolstadt (2011-2014) B.Sc. in Physical Geography, Katholische Universität Eichstätt-Ingolstadt (2007-2011) Ph.D. in Biogeomorphology from Space, Katholische Universität Eichstätt-Ingolstadt (2021) His research integrates remote sensing , GIS-based modeling , time series analysis , and machine learning to study biogeomorphology , interdisciplinary river science , ecosystem services , and social-ecological systems . Key application areas include desert riparian forests in China, hydropower impacts in Kyrgyzstan, and solar energy feasibility in Central Asia. Recent publications (2015-2024) demonstrate his work in spatiotemporal environmental analysis , floodplain delineation , and climate change mitigation using remote sensing. Despite no explicit awards listed, his career spans diverse projects including DFG-funded research, EU Floodplain Institute collaboration, and Stanford University visiting scholarship. He contributes to interdisciplinary research teams at the University of Würzburg's Earth Observation Research Cluster, previously working in Eichstätt-Ingolstadt's Applied Physical Geography and Trier University's Geobotany divisions. Current work focuses on data-scarce region solutions and sustainable development in arid ecosystems.

• Optical Fiber Sensing
• Wireless Communication Networks
• Internet of Things
• +۷ مورد دیگر

Huan Wu is an accomplished academic researcher with a substantial publication record spanning nearly three decades (1996-2025), comprising 63 indexed publications in the dblp database. Their scholarly work demonstrates expertise across multiple technical domains with significant contributions to optical communication systems, wireless networking, and artificial intelligence applications. Research interests focus on the intersection of electrical engineering and computer science, particularly in developing advanced sensing technologies using optical fibers, creating efficient communication protocols for IoT systems, and applying machine learning techniques to environmental monitoring and biomedical applications. Recent work shows a strong trend toward AI integration for infrastructure monitoring systems, including water pipe leak detection and hydrological modeling, as well as healthcare applications like non-invasive glucose monitoring and medical image analysis. The publication portfolio reveals a clear evolution from foundational work in signal processing and communication systems toward increasingly interdisciplinary research that bridges engineering with environmental science and healthcare. Recent publications (2023-2025) demonstrate particular strength in graph neural networks, distributed sensing systems, and AI-driven solutions for critical infrastructure monitoring. Based on publication patterns including consistent first-author contributions, supervisory roles evident in student collaborations, and extensive research output, Huan Wu appears to hold a senior academic position with significant research leadership. The collaborative nature of the work, involving multiple institutions across China and internationally, suggests active participation in major research initiatives and substantial grant funding, though specific details are not provided in the publication records.

Pia Ebelingمشاهده پروفایل

پژوهشگر

• Hydrology
• Environmental Science
• Catchment Hydrology
• +۶ مورد دیگر

Dr. Pia Ebeling is a Researcher at the Department of Hydrogeology, Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany. She holds a Master of Science in Hydrology (2018) and a Bachelor of Science in Hydrology (2014), both from Technical University Dresden. Her research focuses on water quality dynamics, catchment-scale nutrient interactions, data-driven hydrological analysis, and groundwater dynamics under climate variability. She has been involved in international research stays, including at INRAE in France (2018-2022), and has contributed to projects such as CAMELS-DE and QUADICA, which compile extensive hydro-meteorological datasets for German catchments. Dr. Ebeling has received the Karl-Franz-Busch Medal (2018) for her Master’s thesis on seawater intrusion remediation. She collaborates with interdisciplinary teams in the Helmholtz program 'Changing Earth - Sustaining our Future', particularly in topics like coastal zones, landscapes of the future, and sustainable bioeconomy. Her work emphasizes developing Python-based modeling tools, integrating machine learning for environmental prediction, and addressing global challenges in water security and ecosystem health. Education: Bachelor of Science in Hydrology, Technical University Dresden (2014) Master of Science in Hydrology, Technical University Dresden (2018) Research Interests: Quantifying groundwater dynamics and drought patterns Examining nutrient depletion and stoichiometric imbalances in streams Advancing large-sample hydrological analysis through data synthesis Modeling subsurface processes under climate change scenarios Developing open-source tools for hydro-ecological research Advising & Grants: Completed PhD under the supervision of Prof. Jan Fleckenstein at UFZ (2022) Contributed to interdisciplinary research initiatives like Helmholtz TRACER PhD school Involved in international collaborations, including with the University of Texas at Austin’s hydrology team Labs/Teams: Member of the Hydrogeology Department team at UFZ Contributor to the 'Healthy Planet' and 'Landscapes of the Future' research topics Developed the HydroEcoLSTM package for hydro-ecological modeling

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۶ مورد دیگر

Haitao Wang is a prominent researcher affiliated with the Chinese Academy of Sciences, specifically with the Institute of Automation and State Key Laboratory of Management and Control for Complex Systems in Beijing. His research spans multiple disciplines including artificial intelligence, computer vision, engineering systems, and geospatial information processing. With extensive publications across top-tier journals and conferences, he demonstrates significant academic impact in both theoretical and applied research domains. Wang's research interests focus on the intersection of artificial intelligence and practical engineering applications. His work in machine learning includes developing novel algorithms for distance selection, corrosion prediction models for oil and gas infrastructure, and domain-specific large language models for geological applications. In computer vision, he has made contributions to building pattern recognition, image restoration techniques, and UAV-based environmental monitoring systems. His engineering research spans fault diagnosis systems, precision mechanical design, and human-robot interaction frameworks. Analysis of his recent publications reveals a strong trend toward integrating large language models with specialized domain knowledge, particularly in geological applications (GeoProspect) and robotic task reasoning (Double-Feedback). His work increasingly focuses on practical applications in high-risk industries, emergency response systems, and environmental monitoring, demonstrating a shift toward solving real-world problems with AI technologies. His scientific contributions demonstrate excellence across multiple domains, though specific awards are not documented in the provided information. The breadth of his collaborative work across different institutions and research areas highlights his interdisciplinary approach and academic leadership. Wang's research group appears to be involved in numerous projects related to AI-driven decision support systems, particularly in nuclear safety and emergency response scenarios. His recent work on human reliability analysis frameworks suggests active involvement in high-stakes application domains where human-machine collaboration is critical.

• Intelligent Transportation Systems
• Driver Behavior Analysis
• Machine Learning
• +۴ مورد دیگر

Xia Zhao is a Professor at Beijing University of Technology's College of Metropolitan Transportation, specializing in Intelligent Transportation Systems. With over 150 publications spanning from 2002 to 2025, Dr. Zhao has established a strong research profile in transportation engineering, machine learning, and human-machine interface technologies. The research program integrates computational methods with transportation applications, focusing particularly on driver behavior analysis, urban rail transit systems, and intelligent vehicle technologies. Dr. Zhao's research interests center on applying advanced computational techniques to transportation challenges. The work spans intelligent transportation systems, with emphasis on driver state monitoring, lane change prediction, and passenger flow forecasting in urban transit networks. A significant portion of the research applies deep learning and ensemble methods to analyze driver distraction, fatigue detection, and human-machine co-driving systems. The research program demonstrates strong interdisciplinary connections between computer science, transportation engineering, and human factors. The publication record shows a clear evolution from foundational work in neural networks and power systems toward increasingly sophisticated applications in transportation contexts. Recent work (2023-2025) demonstrates a strong focus on multi-modal data fusion, personalized modeling approaches, and safety-critical applications in transportation. The research shows consistent collaboration with Engang Tian and other Chinese institutions, with growing international collaboration in computer vision and healthcare applications of machine learning techniques. Dr. Zhao has supervised multiple graduate students working on transportation-related machine learning projects, with research supported by Chinese national and provincial funding agencies focused on intelligent transportation systems and urban infrastructure development. The work contributes significantly to Beijing University of Technology's reputation in metropolitan transportation research. The research laboratory maintains strong connections with transportation authorities and automotive industry partners, facilitating real-world validation of developed models through access to urban transit data and driving simulators. Current work focuses on integrating multi-sensor data for real-time driver state assessment and developing predictive models for urban mobility patterns.

• Mobile Laser Scanning
• Uncertainty Evaluation & Reduction
• SLAM
• +۲ مورد دیگر

Ziyang Xu, M.Sc., is a PhD Candidate and Research Associate in Mobile Laser Scanning at the Chair of Engineering Geodesy, Department of Aerospace and Geodesy, Technical University of Munich (TUM). He also serves as a Visiting Researcher at the Urban Analytics Lab, National University of Singapore. Education: PhD Candidate (2023–present), TUM Master of Engineering (2017–2019), Wuhan University Bachelor of Engineering (2013–2017), Wuhan University His research focuses on mobile laser scanning , uncertainty evaluation , SLAM , and point cloud processing for urban digital twin applications. He has developed methods for quality-controlled data fusion and automated uncertainty reduction in indoor MLS systems. Recent work highlights include co-authoring two 2025 publications on uncertainty-aware point cloud fusion and plane-based calibration techniques, alongside invited presentations at institutions like NUS and TUM Geospatial Week 2025 in Dubai. Teaching: Lecturer/Exercise Instructor for Mobile Laser Scanning, Metrology & Quality, and Geodetic Seminar at TUM He contributes to the Geodetic Testing Laboratory at TUM, specializing in calibration, monitoring, and point cloud accuracy simulations.

Saskia E. Dröslerمشاهده پروفایل

استاد

• Health Services Research
• Medical Informatics
• Quality Assurance
• +۵ مورد دیگر

Professor Saskia E. Drösler holds a faculty position at Niederrhein University of Applied Sciences in the Department of Health Care Management within the School of Health Sciences . Her academic expertise spans health services research, medical informatics, patient safety, and quality assurance with a focus on administrative data analysis for healthcare policy. Her research, visible through publications from 2019 to 2011, explores: ICD-11/10 coding frameworks for safety events Small-area health disparities in diabetes care International healthcare quality comparisons Administrative data limitations and improvements Standardization of diagnosis-timing in hospital records Key collaborations include institutions such as University of Calgary, UC Davis , and participation in WHO ICD-11 advisory groups . She has served in multiple university governance roles at Hochschule Niederrhein from 2004 to 2023.

Li Zhangمشاهده پروفایل

پژوهشگر

• Geodesy
• Construction Quality Assurance
• GNSS Technology
• +۳ مورد دیگر

Li Zhang is a Senior Academic Councillor and Group Leader at the Institute of Engineering Geodesy (IIGS) within the University of Stuttgart's Faculty 6: Aerospace Engineering and Geodesy. Her work focuses on holistic quality models for construction processes, low-cost GNSS monitoring systems , and multi-sensor fusion in infrastructure projects. She leads the Quality Modeling and Sensor Fusion group within the Cluster of Excellence IntCDC, which emphasizes integrative computational design for architecture. PhD in Geodesy (2016, University of Stuttgart) Specializes in GNSS error mitigation , construction process digitization , and digital map applications for transport security Her recent publications analyze geospatial data fusion for rock cliff monitoring, real-time quality assurance in residential construction, and cost-effective positioning for mobile manipulators. She supervises students on topics ranging from terrestrial laser scanning to indoor robot localization , balancing technical precision with sustainability. Li Zhang actively contributes to the FIG Working Group 5.6 "Cost Effective Positioning" and leads the DVW working group "Quality Assurance" . Her teaching portfolio includes courses on deformation analysis , multi-sensor systems , and statistical methods for engineering students.

Martin Schlüterمشاهده پروفایل

استاد

• Geodesy
• Photogrammetry
• Structural Health Monitoring
• +۵ مورد دیگر

Martin Schlüter is a Professor at the Department of Geodesy and Geoinformatics within the School of Engineering at Mainz University of Applied Sciences. His research focuses on the integration of photogrammetry, geodetic engineering, and machine learning for structural monitoring applications. Key institutional affiliations include leadership in the i3mainz research center and collaboration with the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF). His research interests span Geodesy , Photogrammetry , and Structural Health Monitoring , with recent emphasis on machine learning applications for optical measurement systems. Schlüter pioneers modular digital camera tachymeters (MoDiTa) for bridge vibration analysis, railway safety, and BIM integration. His work bridges theoretical geodetic principles with practical engineering solutions for infrastructure monitoring, emphasizing real-time data processing and sensor fusion. Schlüter leads significant research projects including BAM (Big-Data Analytics in Environmental Monitoring) and VCSA3D (Virtual Crime Scene Analysis) , securing institutional funding through Mainz University's Best Practice Research Program. His laboratory specializes in optical measurement systems, developing calibration protocols for X-ray micro-CT and laser tracking applications. Current work focuses on AI-driven structural monitoring for transportation infrastructure and heritage preservation. Professional contributions include active participation in the DGPF and editorial roles in German surveying journals. His research demonstrates consistent innovation in merging traditional geodetic methods with emerging computational techniques for precision engineering applications.

• Regional Economics
• Corporate Governance
• Business Economics
• +۵ مورد دیگر

Naufal Alimov is a Researcher at the Leibniz Institute of Ecological Urban and Regional Development (IOER) in Dresden, Germany, where he has been working since 2021. He previously held research positions at LUT University, Finland (2017–2020), Pellervo Economic Research (PTT), Finland (2017), and Hanken School of Economics, Finland (2010–2017), where he also served as a Teaching Assistant (2002–2009). He earned his Doctor of Science in Economics and Business Administration from Hanken, a Master of Science in Economics and Management from Humboldt University in Berlin, and a Bachelor of Science in Economics and Production Management from Samarkand State University. Current Affiliation: Leibniz Institute of Ecological Urban and Regional Development (IOER), Dresden Previous Affiliations: Hanken School of Economics, LUT University, Pellervo Economic Research Educational Background: PhD (Hanken), MSc (Humboldt University), BSc (Samarkand State University) His research focuses on regional economics, corporate governance, institutional ownership, and business economics . He investigates the role of public and institutional investors in firm performance, M&A activities, and economic policy. His work combines empirical economic analysis with policy-relevant insights, particularly in Nordic and European contexts. He has also contributed to studies on regional economic databases, labor market policies, and housing preferences. The most recent publications highlight a strong trend in analyzing institutional investor behavior , especially in mergers and acquisitions and pension fund involvement. His work spans both microeconomic firm-level analysis and macroeconomic policy evaluation, demonstrating a versatile and policy-oriented research profile. Naufal Alimov has not been awarded any scientific prizes mentioned in the text, but his consistent publication record in reputable journals reflects strong scholarly impact. He has advised or supervised no named students in the provided material. His research is conducted primarily through institutional collaborations and government-funded reports, particularly with Finnish and German policy bodies. There is no mention of a specific lab or research team led by him, but he collaborates extensively with economists and policy researchers across Europe.

Robert Kadenمشاهده پروفایل

استاد

• Surveying
• Geoinformatics
• Geographic Information Systems
• +۵ مورد دیگر

Robert Kaden is a Professor of Surveying and Geoinformatics at the University of Applied Sciences Erfurt, where he serves as Vice Dean of the Faculty of Business, Economics and Social Sciences and Head of the Geodetic Laboratory and Geodesign Center. His academic career spans teaching, research, and leadership roles focused on the integration of geospatial technologies with modern construction practices. Education: Doctorate in Geoinformatics, Technical University of Munich (2014) Master of Geodesy and Geoinformation Science, Technical University of Berlin (2009) Graduate Engineer in Surveying, Dresden University of Applied Sciences (2005) Surveyor qualification, BSZ Hans Erlwein Dresden (1999) Professor Kaden's research focuses on the intersection of geospatial technologies and digital construction methodologies. His work centers on Building Information Modeling (BIM), Geographic Information Systems (GIS), 3D city modeling, and their integration for urban planning and energy analysis. He has pioneered approaches for combining surveying precision with digital construction workflows, particularly in the areas of as-built modeling, geodetic coordinate systems within BIM environments, and energy demand estimation using semantic 3D city models. His publication record reveals a clear trajectory from foundational work in 3D city modeling toward increasingly sophisticated integration of BIM and GIS technologies. Recent publications emphasize practical applications in energy planning, virtual reality in landscape architecture, and AI-based decision support systems, reflecting the evolution of geospatial technologies from data representation toward intelligent analysis and decision-making tools. Scientific Awards: 2017 Geoinformatics Award from Runder Tisch GIS eV 2012 Oracle Spatial Excellence Award for Education and Research 2010 Harbert Book Prize from German Association for Surveying 2008 CATCON Gold Award from International Society for Photogrammetry and Remote Sensing As Academic Advisor and Head of the Geodetic Laboratory and Geodesign Center, Professor Kaden mentors students and leads research initiatives focused on practical applications of geospatial technologies. His professional activities include significant contributions to industry guidelines, particularly the 'Guide to Geodesy and BIM,' and leadership roles in professional associations including the Round Table GIS eV, German Society for Photogrammetry, and DVW – Society for Geodesy, Geoinformation and Land Management. His laboratory work centers on sensor-integrated solutions for construction surveying, BIM-GIS integration, and the development of standards for geospatial data exchange in the construction industry, positioning his team at the forefront of digital transformation in surveying and civil engineering practices.

• Spatial Statistics
• Spatiotemporal Statistics
• Environmetrics
• +۴ مورد دیگر

Philipp Otto is a Professor of Statistics and Data Science at the University of Glasgow. Previously, he served as an Assistant Professor at Leibniz University Hannover (2018-2023) and held a visiting professorship at the University of Göttingen (2020-2021). He earned his PhD in Statistics (summa cum laude) from the European University Viadrina in 2016. His research focuses on spatial/spatiotemporal statistics, environmetrics, machine learning, and big geospatial data. He is affiliated with research groups in Statistics & Data Analytics, Modelling in Space & Time, and Environmental Sciences. Education: B.Sc. in International Economics (with study visits to Saint Petersburg State University) Direct PhD in Statistics (fast-track program without M.Sc., European University Viadrina) Research Interests: Develops statistical models for geo-referenced/network data, spatiotemporal volatility models (ARCH/GARCH), and AI-driven tools for data quality. Recent work includes hurricane forecasting, air quality impact assessments, and mesoplastic distribution analysis. Key Contributions: Developed the R package 'spGARCH' for spatial/spatiotemporal volatility modelling Edited a Springer volume on advanced statistical methods (2024) Recipient of Lindau Nobel Laureate Fellowship (2017) Grants & Collaboration: Over €1M in grants, including projects on historical map time series, agricultural air quality impact, and BIM survival analysis. Industry partnerships include projects with financial institutions. Labs & Teams: Coordinates spatiotemporal modelling research group at Glasgow, collaborates with environmental agencies and statistical societies.

• Computer Vision
• Deep Learning
• Robotics
• +۵ مورد دیگر

Hao Yang is a Professor at Southeast University's School of Computer Science and Engineering, specializing in computer vision, deep learning, and robotics. His research spans multiple domains including medical imaging, infrastructure inspection, and multimodal AI systems. His research interests focus on advancing computer vision techniques for practical applications. He develops innovative deep learning architectures for object detection, image segmentation, and quality assessment, with particular emphasis on solving challenges in complex real-world scenarios including rotated object detection, ancient character recognition, and 3D point cloud analysis. His work often integrates novel transformer variants and Mamba architectures to address domain-specific challenges. His publication record shows significant contributions across multiple IEEE and ACM journals, with a strong emphasis on practical applications of AI in engineering contexts. His recent work demonstrates expertise in adapting cutting-edge neural network architectures to solve domain-specific problems in civil engineering, medical imaging, and transportation systems. Hao Yang actively collaborates across disciplines, with publications spanning computer science, electrical engineering, civil engineering, and biomedical applications. His work frequently appears in top-tier conferences including CVPR, AAAI, and ACL, demonstrating his broad impact across multiple AI subfields.

Jochen Gönschمشاهده پروفایل

استاد

• Operations Research
• Revenue Management
• Logistics
• +۵ مورد دیگر

Prof. Jochen Gönsch is a full professor at the University Duisburg-Essen, leading the Service Operations department within the Faculty of Management Science and Mathematics. His expertise spans operations research, revenue management, and logistics. He completed his PhD at the University of Augsburg in 2010 and earned his Habilitation there in 2015. He has collaborated with companies like Daimler and Lufthansa, applying quantitative methods to solve service-sector challenges. His research emphasizes risk-averse optimization, dynamic pricing, and mobility systems, addressing issues like remanufacturing and renewable energy. He received the Logistics Science Award from BVL and co-founded the GOR Analytics working group. Teaching focuses on analytical methods for service operations, integrating practical relevance with active learning. Research highlights include optimizing shared mobility systems, maintenance scheduling, and energy storage strategies. His work bridges academic rigor with industry applications, often leveraging partnerships to refine models for real-world impact.

Ralf Gründlingمشاهده پروفایل

پژوهشگر

• Soil genesis
• Soil processes
• Soil functions
• +۱۰ مورد دیگر

Ralf Gründling is a Soil Scientist & Scientific Technician at the Helmholtz-Centre for Environmental Research (UFZ) in the Department of Soil System Science since 2015. His research spans multiple dimensions of soil science with strong technical expertise in geospatial technologies and precision agriculture. Dr. Gründling's primary research interests focus on soil genesis, processes, and functions , with specialized expertise in soil carbon dynamics, remote sensing of soil properties, UAV applications, 3D-Laser scanning, and spatial distribution of soils . His work integrates field measurements with advanced computational approaches to understand soil behavior across scales. His technical activities include soil mapping, database management, GNSS/GPS systems operation, UAV-based remote sensing, and 3D terrestrial laser scanning. Analysis of his recent publications (2016-2025) reveals a consistent focus on soil structure and function , with increasing emphasis on UAV technology and remote sensing applications. His research spans both fundamental soil processes and applied precision agriculture, with strong connections to climate science through his work on meteorological data and carbon dynamics. Notably, his 2025 publications demonstrate continued leadership in soil structure analysis and innovative UAV applications. Dr. Gründling is actively involved in technical support for meteorological observatories and lysimeter stations, contributing to long-term environmental monitoring infrastructure. His collaborative work extends across multiple projects examining river-floodplain ecosystems, carbon dynamics, and agricultural systems.