جست‌وجوی استاد و پژوهشگر

Thomas Brinkhoffمشاهده پروفایل

استاد

• Geoinformatics
• Geodatabase Systems
• Spatio-temporal Data Processing
• +۵ مورد دیگر

Prof. Dr. Thomas Brinkhoff is Chair of the Institute Board and Chair of Geoinformatics at Oldenburg University of Applied Sciences. He leads the Institute for Applied Photogrammetry and Geoinformatics (IAPG) and contributes to institutions like the Association for the Promotion of Geoinformatics in Northern Germany (GiN e.V.) and the Oldenburg Research and Development Institute for Computer Science (OFFIS e.V.). Education: Diploma in Computer Science (Informatik), Universität Bremen (1990) Doctorate in Computer Science (Dr. rer. nat.), Ludwig Maximilian University of Munich (1994) Brinkhoff's research spans geodatabase systems, spatiotemporal data processing, geosensor analytics, and location-based services. His work addresses Volunteered Geographic Information (VGI), web-based geospatial visualization, and mobile data integration, with applications in traffic management and forensic science. Recent projects include ProSaDi (Digital Provenance and Collection Research) and contributions to the Laboratory for optical 3D metrology . He has served on program committees for ACM SIGSPATIAL (2002-2019), AGILE conferences (2010-2025), and editorial boards of journals like GeoInformatica and TGIS. Notable Lectures: 2024: Forensic applications of tachograph data 2023: Geoinformatics in homicide investigations 2022: Spatiotemporal analysis for sustainability projects 2015: Open geodata standards at FOSSGIS 2014: Mobile sensor data processing

Xiaoxiang Zhuمشاهده پروفایل

استاد

• Remote sensing and Earth observation
• Signal processing
• Artificial intelligence and machine learning
• +۳ مورد دیگر

Xiaoxiang Zhu is a Professor for Data Science in Earth Observation at the Technical University of Munich (TUM) and serves as the Director of the International AI Future Lab - AI4EO. She is also on the Board of Directors of the Munich Data Science Institute (MDSI) and has held various leadership positions in research institutions including as Spokesperson for Helmholtz AI Research Field "Aeronautics, Space and Transport" (MASTr). Her educational background includes a doctorate (Dr.-Ing.) and habilitation from TUM. She has held positions as Private Dozent at TUM (2013-2015), TUM Junior Fellow (2013-2015), and Research Group Leader for "SparsEO" at Munich Aerospace (2011-2016). Professor Zhu's research focuses on the intersection of remote sensing, artificial intelligence, and data science. Her work primarily addresses global urban mapping, sustainable development goals, and climate change monitoring through Earth observation technologies. She develops advanced signal processing techniques and machine learning algorithms specifically tailored for satellite imagery and geospatial data analysis. Her research has significant applications in urban planning, environmental monitoring, and disaster management. Her team has pioneered approaches that combine synthetic aperture radar (SAR) with deep learning for improved Earth observation capabilities. Professor Zhu has received numerous prestigious awards including being named an IEEE Fellow (2021), receiving the Geodesy Award of the Nico Rüpke Foundation (2020), and being awarded an ERC Proof of Concept Grant (2020, 2022). She is also a Fellow of the Academia Europaea (2024) and AAIA Fellow (2024). Her publication record includes highly cited works such as "Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources" (2017). She leads a substantial research team comprising numerous PhD students and postdoctoral researchers working on various projects including Horizon Europe - ThinkingEarth, EarthCare, and AI4TWINNING. Her research group, the Chair of Data Science in Earth Observation, is actively involved in multiple large-scale European and German research initiatives focused on Earth observation and AI applications.

• Photogrammetry
• Geoinformation
• Remote Sensing
• +۷ مورد دیگر

Prof. Christian Heipke is a distinguished academic serving as Dean of the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover, Germany. He also holds the position of Executive Director at the Institute of Photogrammetry and GeoInformation (IPI), one of the leading research institutions in geospatial sciences within the faculty. His leadership extends across multiple committees including the Curriculum and Teaching Committee, Admissions and Examination Boards for Geodetic Science and Geoinformatics, and Navigation and Environmental Robotics. As a Professor at IPI, he maintains active research while overseeing significant academic and administrative responsibilities at the university. Professor Heipke's research spans multiple domains within geospatial sciences, with particular emphasis on: Advanced photogrammetric techniques and algorithms Remote sensing applications for environmental monitoring Computer vision approaches for geospatial data analysis Urban development monitoring using satellite imagery Machine learning applications in geoinformatics Disaster prediction and management systems His recent scholarly output reveals a strong focus on integrating cutting-edge computer vision and deep learning techniques with traditional photogrammetric methods. Analysis of his 15 most recent publications shows a clear trajectory toward more sophisticated AI-driven approaches for processing geospatial data, with particular attention to time-series analysis, uncertainty quantification, and multi-view systems. His work bridges theoretical advancements with practical applications in flood forecasting, deforestation monitoring, urban planning, and construction materials analysis. The geographic scope of his research has expanded significantly, with recent projects focusing on international case studies in the Philippines and tropical regions. Professor Heipke leads the Institute of Photogrammetry and GeoInformation, a major research hub that has celebrated 75 years of contributions to the field. His leadership extends to the Graduiertenkolleg 2159: "Integrity and Collaboration in Dynamic Sensor Networks," where he serves as a professor overseeing doctoral research. The institute maintains state-of-the-art facilities for processing satellite imagery, aerial photography, and developing novel algorithms for geospatial data analysis. Under his direction, the institute has strengthened its international collaborations and interdisciplinary research approaches, particularly in addressing Sustainable Development Goals through geospatial technologies.

• Epidemiology
• Public Health
• Migration and Health
• +۴ مورد دیگر

Professor Oliver Razum at Bielefeld University's Faculty of Health Sciences leads Working Group 3 in Epidemiology & International Public Health. He serves on multiple commissions including the Faculty Conference and Teaching Commission, and contributes to the Center for Statistics and Bielefeld Center for Data Science. His work focuses on Epidemiology of migration and health Refugee health inequalities Medical rehabilitation equity Othering processes in public health Gender-sensitive health research Recent articles explore 2026 medical rehabilitation for people with disabilities, 2025 refugee mental health frameworks, and 2024 pandemic equity research. Key scientific contributions include the 2025 Georg Forster Fellowship and leadership in diversity-sensitive healthcare initiatives. His research spans urban neighborhood health effects , digital rehabilitation , and structural racism in healthcare . Current projects address intersectional inequalities , post-migration stress , and gender as social determinant . Professor Razum maintains active collaborations with institutions like the German Research Foundation and DRV Westphalia.

• Earth Observation
• Data Science
• Remote Sensing
• +۳ مورد دیگر

Prof. Jonathan Bamber holds the Professorship in Data Science in Earth Observation at the TUM School of Engineering and Design within Technische Universität München (TUM). His role involves advancing interdisciplinary research at the intersection of data science and environmental monitoring. Research interests focus on leveraging advanced data analysis techniques for Earth observation, including climate change modeling, remote sensing applications, and geospatial data interpretation. His work bridges engineering and environmental science to address global challenges. No specific awards, grants, or student advisees are listed in the provided profile. Contact details are managed via TUMonline, but an explicit email address is not provided in the text.

Werner Langمشاهده پروفایل

استاد

• Sustainable Building
• Energy-Efficient Design
• Building with Positive Footprint
• +۷ مورد دیگر

Prof. Dr.-Ing. Werner Lang serves as Vice President for Sustainable Transformation and holds the Chair of Energy Efficient and Sustainable Design and Building (ENPB) at the Technical University of Munich (TUM), within the TUM School of Engineering and Design. Previously, he was Professor of Sustainable Building and Director of the Center for Sustainable Development at the University of Texas School of Architecture in Austin (2008-2010). Lang also directs the Oskar von Miller Forum and is a partner at Lang Hugger Rampp GmbH Architekten in Munich. Lang's research focuses on developing strategies for buildings with positive environmental footprints through regenerative energy systems, renewable materials, and closed material cycles. His work emphasizes comprehensive life cycle analysis considering ecological, economic, and social aspects. Current research areas include climate-resilient urban neighborhoods, circular economy in construction, and sustainable building materials. The ENPB institute conducts numerous research projects such as Building Climate-Municipal, CircularFTmehrRAUM, and Urban Green Infrastructure. Lang's publications reveal a strong trend toward life cycle assessment, multi-criteria decision-making, and computational approaches for sustainable building design. His recent work integrates machine learning with building performance analysis and focuses on practical implementation of circular economy principles in urban contexts, with increasing emphasis on quantifying environmental benefits of urban green infrastructure. TUM Sustainability Award 2022 Doce et Delecta (Second Prize for Best Teaching), 2019 Bayerischer Energiepreis 2014 International Building Skin Tech Award (2008) Promotionspreis der TUM (2000) Lang leads the Institute of Energy-Efficient and Sustainable Design and Building with numerous research grants including projects like Building.Lab+, NAWAREUM, and ECO+. His team includes researchers working on topics ranging from urban mining to life cycle assessment tools. The institute maintains several products and startups including MoMeBo, Bilanzlabor, and EnergyML that translate research into practical applications for the building industry.

• Machine Learning
• Data Mining
• High-Performance Computing
• +۵ مورد دیگر

Prof. Dr. Fabian Gieseke is a Professor and Chair of Machine Learning and Data Engineering at the University of Münster. He holds a PhD in Computer Science from Carl von Ossietzky University of Oldenburg and a dual degree in Mathematics and Computer Science from the University of Münster. His research focuses on Machine Learning, High-Performance Computing, and their applications in Geosciences, Smart Cities, and Astrophysics. Education: PhD in Computer Science (2012), Carl von Ossietzky University of Oldenburg University studies in Mathematics and Computer Science (2006–2011), University of Münster Research Interests: Data Mining and Machine Learning High-Performance Computing & Distributed Systems Deep Learning Applications in Environmental Science and Astrophysics Geospatial Data Analysis using Satellite Imagery Publications Trends: His recent work emphasizes large-scale environmental monitoring via deep learning, including canopy height estimation, forest biomass prediction, and national-scale tree counting. He also explores interactive systems for geospatial data retrieval and optimization of machine learning models for resource-constrained environments. Advising & Grants: Supervised over 30 theses on topics like satellite image analysis, deep learning on microcontrollers, and data marketplaces for smart grids. Active in securing grants for interdisciplinary projects combining AI with Earth observation. Labs/Teams: Leads the Machine Learning and Data Engineering group at the University of Münster, focusing on scalable AI solutions for real-world challenges in science and industry.

Werner Reinartzمشاهده پروفایل

استاد

• Retailing
• Customer Management
• Digital Transformation
• +۹ مورد دیگر

Werner Reinartz is a Professor of Marketing at the University of Cologne since 2007, where he holds the Chair for Retailing and Customer Management . He serves as Vice-Rector for Transfer (2023–ongoing) and Director of the Center for Research in Retailing (IFH e.V.) . His career includes a part-time Associate Professor role at INSEAD (2007–2010) and tenured/untenured positions there from 1999–2007. He earned a Ph.D. in Marketing from the University of Houston (1999) and an MBA from Henley Management College (1997). His research focuses on Retailing , Customer Management , Digital Transformation , and Marketing Strategy . He investigates how platformization , geospatial data , and authenticity in TV advertising influence consumer behavior and business outcomes. His work also addresses CRM efficacy , B2B hybrid offerings , and value creation in dynamic markets . Key trends in his recent publications include: Digital transformation in retail and branding Behavioral economics in marketing decisions Quantitative analysis of TV advertising effectiveness Geospatial data applications in international markets Platform business models for enduring customer relationships CRM and customer profitability in noncontractual settings Scientific honors include: Academic Fellow, Marketing Science Institute (MSI) (2023) EMAC Distinguished Marketing Scholar Award (2023) Shelby D. Hunt/Harold H. Maynard Award (2022) Jan Steenkamp Award for Long-Term Impact (2021) Outstanding Area Editor, Journal of Marketing (2016) Donald R. Lehmann Award (2001) and John A. Howard Dissertation Competition Winner (1999) He contributes to editorial boards and collaborative research initiatives like the Research Initiative 'Digital Transformation and Value Creation' and the Cluster of Excellence ECONtribute: Markets & Public Policy , which examines market challenges through interdisciplinary lenses.

• Artificial Intelligence in Healthcare
• Biomedical Engineering
• Traditional Chinese Medicine Informatics
• +۳ مورد دیگر

Dr. Jing Wang is a Professor in the Department of Bioinformatics at Southern Medical University's School of Medicine, with extensive research at the intersection of artificial intelligence and biomedical applications. Her work demonstrates strong cross-disciplinary collaboration across medical institutions, engineering departments, and computer science research groups. Her primary research interests include Artificial Intelligence in Healthcare , Biomedical Engineering , and Traditional Chinese Medicine Informatics , with recent publications showing particular expertise in medical imaging analysis, diagnostic assistance systems, and clinical decision support. Her work spans both theoretical algorithm development and practical clinical implementations. Analysis of her 15 most recent publications (2025-2026) reveals a strong trend toward clinically applicable AI systems, with approximately 60% of publications focused on medical diagnostics and treatment support systems. The remaining publications demonstrate expertise in industrial applications of computer vision and fundamental AI research. Her work shows consistent collaboration with both domestic Chinese institutions and international research groups. Notable scientific contributions include: Development of 'Tianyi', a traditional Chinese medicine language model for clinical practice Innovations in bionic soft robotics for rehabilitation assistance Novel approaches to medical image analysis for cancer diagnostics Her research program appears well-funded with consistent publication output across high-impact journals in biomedical engineering, AI, and medical informatics. Current work suggests strong emphasis on translating AI research into clinical practice, particularly in diagnostic support systems and rehabilitation technology.

• Data Science
• Computational Social Science
• Misinformation
• +۳ مورد دیگر

Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.

Christian Mayerمشاهده پروفایل

استاد

• Origin of life
• Nanocapsules
• NMR spectroscopy
• +۵ مورد دیگر

Prof. Dr. Christian Mayer is a Professor in Physical Chemistry at the Faculty of Chemistry, University of Duisburg-Essen. He serves as Head of the working group focusing on origin of life research, nanocapsules, and NMR spectroscopy techniques. His research group is located at Universitätsstraße 5, D-45141 Essen, Germany, with contact information including phone number +49 201 183-2570. Prof. Mayer's research interests primarily focus on the origin of life in deep tectonic fault zones of the first continental fragments, where he collaborates with Prof. Dr. Ulrich Schreiber from the Faculty of Biology and Prof. Dr. Oliver Schmitz from Applied Analytical Chemistry. His work investigates how vesicle formation occurs in tectonic fault systems through cyclic phase transitions of carbon dioxide, creating ideal conditions for molecular evolution. He specializes in pulsed field gradient NMR (PFG-NMR), high-resolution NMR, and solid-state NMR techniques to characterize nanoscale systems including nanocapsules, vesicles, and microemulsions. His recent publication trends reveal a strong interdisciplinary focus spanning physical chemistry, prebiotic chemistry, and astrobiology. The articles demonstrate increasing integration of computational methods with experimental approaches, particularly in analyzing molecular structures and dynamics. His research has evolved from fundamental studies of nanocapsule systems to broader investigations of protocell formation mechanisms under early Earth conditions, with recent work extending to astrobiological contexts including potential life formation on Titan. Prof. Mayer has established significant collaborations across multiple disciplines, particularly with geologists and biologists, to investigate the physical chemical processes that could have led to the emergence of life. His work bridges fundamental physical chemistry with practical applications in nanomedicine, particularly in developing artificial oxygen carriers based on nanocapsule technology. His laboratory utilizes high-pressure facilities to simulate early Earth crust conditions, with a particular focus on supercritical CO 2 environments. The working group combines experimental approaches with theoretical modeling to understand vesicle formation processes and their implications for the origin of cellular life.

• Hydrography
• Geodesy
• Navigation Systems
• +۷ مورد دیگر

Prof. Harald Sternberg is a distinguished academic at HafenCity University Hamburg, holding the position of University Professor for Hydrography and Geodesy. His affiliations include the Department of Geodesy and Geoinformatics, where he leads research in hydrographic education and advanced geomatics technologies. He previously served as Vice President for Teaching and Studies (2009-2022) and Acting President (2010) of HCU. Education: Ph.D. in Geodesy from University of the Bundeswehr Munich (1999), specializing in trajectory determination of land vehicles using hybrid systems. Early career included roles as scientist at Bundeswehr University (1991-2001) and academic leadership at HAW Hamburg (2005-2009). Research focuses on underwater mapping, navigation systems, and sensor integration. Key projects include: Level 5 Indoor Navigation (5G-based positioning), hydrothermal vent exploration using deep-towed multibeam systems, and low-cost mobile mapping solutions. He also investigates smartphone-based inertial navigation and autonomous underwater vehicles for infrastructure monitoring. Publications span underwater vision systems, satellite-derived bathymetry, and 3D point cloud analysis. Over 200 peer-reviewed articles and book chapters reflect expertise in geomatics applications. Current research emphasizes 5G-enabled indoor navigation and environmental sensor networks. Grants include BMWK-funded autonomous deep-sea monitoring and BGR exploration projects in the Indian Ocean. His lab develops innovative tools like the HOMESIDE sled for seafloor surveys. Supervises Ph.D. research on hydrothermal vent analysis and data-driven inertial localization.

Heiko Paulheimمشاهده پروفایل

استاد

• Knowledge Graphs
• Semantic Web
• Web Data Mining
• +۵ مورد دیگر

Prof. Dr. Heiko Paulheim is a Professor of Data Science and currently serves as University Vice President at the University of Mannheim. He leads the Data and Web Science Group (DWS), which focuses on Web Data Mining, Knowledge Graphs, and Semantic Web technologies. His research group contributes to open source knowledge graphs like DBpedia and develops new knowledge graphs such as WebIsALOD and DBkWik. As of October 1, 2024, he has limited teaching capacity due to his vice presidential duties. Prof. Paulheim's research interests span Knowledge Graphs, Semantic Web, Web Data Mining, Machine Learning, and Natural Language Processing. His work particularly focuses on knowledge graph refinement, embedding techniques (notably RDF2vec), and applications in various domains including news recommendation, biomedical informatics, and environmental monitoring. His group develops practical tools like the RapidMiner Linked Open Data Extension and RDF2vec for knowledge graph applications. His recent publications demonstrate a strong focus on knowledge graph embeddings, with particular attention to RDF2vec variants, applications in news recommendation systems, biomedical data integration, and spatio-temporal knowledge graphs for environmental monitoring. His work bridges theoretical advances in knowledge representation with practical applications across multiple domains. Among his notable achievements are a nomination for the Best Paper Award at CAiSE 2025 and securing an Open Science Grant for the SpatialBenchRAG project. His research has significant impact in both academic and industrial contexts, with multiple papers accepted at top conferences like ISWC and ESWC. Prof. Paulheim has supervised numerous PhD students including Alexander Brinkmann and Michael Schlechtinger, and has led several research projects including the DFG Project Mine@LOD, State of BW Project SyKoW², and BMBF Project DS4DM. His group maintains strong industry connections with partners like SAP AG, Daimler AG, and IDS.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۱۲ مورد دیگر

Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols

Ihab Hijaziمشاهده پروفایل

پژوهشگر

• Geoinformatics
• 3D Geospatial Modeling
• BIM-GIS Integration
• +۴ مورد دیگر

Dr. Ihab Hijazi is a researcher at the Chair of Geoinformatics , Technical University of Munich, specializing in 3D geospatial modeling and BIM-GIS integration . He teaches courses such as CAFM - Facility Management and GIS and contributes to advancing CityGML standards and urban digital twins . Research Focus : Hijazi’s work bridges Building Information Modeling (BIM) and Geographic Information Systems (GIS) , emphasizing interoperability , semantic transformation , and smart city infrastructures . His projects include the Smart Sustainable Districts (SSD) and Smart District Data Infrastructure , focusing on urban energy systems and data harmonization. Publications highlight his contributions to CityGML utility networks , urban growth simulations , and semantic modeling frameworks. He actively develops 3DCityDB and participates in standardization initiatives like the DIN Spec 91607 Digital Twin for Cities .