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Di YangView profile

Assistant Professor

• Remote Sensing
• Geospatial Analytics
• Machine Learning
• +7 more

Di Yang is an Assistant Professor at the University of Florida, specializing in geospatial technologies and environmental monitoring. His research integrates machine learning, remote sensing, and citizen science to address land use changes, ecosystem dynamics, and human-environment interactions across global scales. He leads projects funded by NASA, BLM, and NCAR, focusing on topics like forest management, climate resilience, and geospatial cloud computing. Education: Ph.D. in Geography, University of Florida (2019) M.S. in Environmental Engineering, Texas A&M University-Kingsville (2013) B.S. in Environmental Science, Liaoning University of Petroleum and Chemical Technology (2011) Research Interests: Remote sensing applications, geospatial analytics, machine learning for environmental monitoring, citizen science, land change science, and sustainability. Grants & Projects: NASA: Forecasting Mosquito-Borne Disease Risk in a Changing Climate (PI) NASA Early-Career: Evaluating Long-term Impacts of Land-use Transformation on Fire Regimes BLM: Monitoring Yermo xanthocephalus using UAS and AI/ML His lab develops innovative frameworks like the Open Land-Use Map strategy and leverages platforms like Google Earth Engine for large-scale environmental analysis. Yang actively collaborates on NEON biodiversity surveys and contributes to ecological network design.

• Digital Photogrammetry
• Computer Vision
• Geospatial Imaging
• +10 more

Dr. Jackson David Cothren is a Professor in the Department of Geosciences at the University of Arkansas, where he also serves as the Leica Geosystems Chair in Geospatial Imaging. He holds dual leadership roles as Director of the Center for Advanced Spatial Technologies (CAST) and the Arkansas High Performance Computing Center (HPCC). His academic affiliations are deeply rooted in geospatial science, computer vision, and high-performance computing, bridging engineering and environmental applications. Ph.D. in Geodetic Science and Surveying, The Ohio State University M.S. in Geodetic Science and Surveying, The Ohio State University B.S. in Applied Mathematics, United States Air Force Academy Dr. Cothren's research spans digital photogrammetry, computer vision, UAV-based geospatial monitoring, and spatial archaeometry. He investigates non-traditional sensor modeling, feature extraction, surface generation, and integration with enterprise geospatial systems. His work increasingly incorporates deep learning, transformer models, and AI-driven analytics for applications in renewable energy, autonomous systems, and environmental sustainability. His recent publications highlight innovations in solar PV profiling, aerial image segmentation, and fairness-aware domain adaptation. The trends in his recent scholarly output reflect a strong shift toward machine learning and AI in geospatial analysis, particularly using transformer architectures for high-resolution imaging and cross-domain adaptation. His work integrates Lidar, GPS, and InSAR for deformation monitoring and leverages HPC for large-scale data processing. Applications span archaeology, agriculture, transportation, and energy infrastructure. Dr. Cothren has received numerous competitive grants from NSF, NEH, and USDA, supporting interdisciplinary research in geospatial analytics, smart transportation, and cultural heritage. His projects emphasize data-driven decision-making, community engagement, and workforce development in geospatial technologies. Principal Investigator, NSF E-RISE Rll: Arkansas Smart Transportation Research Incubator (2025–2029) Lead, RII Track-1: DART – Data Analytics that are Robust and Trusted (NSF, 2020–2025) Director, OPEN-GATE: Expanding Geospatial Education (NSF, 2016–2020) He mentors a broad interdisciplinary team and leads collaborative research initiatives involving computer vision, environmental science, and archaeology. His labs and research centers—CAST and HPCC—serve as hubs for innovation in spatial technologies, high-performance computing, and data-intensive research across the university and beyond. These centers support large-scale projects in archaeo-geophysics, UAV monitoring, and enterprise GIS integration.

Arka GhoshView profile

Researcher

• Heterogeneous data integration
• Ontology-based data access (OBDA)
• GeoBigdata (Spatial and Temporal Raster) integration and management
• +7 more

Arka Ghosh is a doctoral student and knowledge engineer at Umeå University , Department of Computing Science. He is affiliated with the AI for Data Management research group led by Prof. Dr. Diego Calvanese. Research focus: Virtual Knowledge Graphs (VKGs), heterogeneous data integration, and management of geospatial/raster data Email: arka.ghosh@umu.se Research Areas: Ontology-based data access (OBDA), semantic querying with SPARQL and NLP, Description Logic formalisms, raster/vector data processing, and temporal-spatial data modeling. Publications: Recent work presented at RuleML+RR 2024 and 2023 conferences includes extending VKG systems to handle large-scale satellite raster data and reformulating queries across heterogeneous data sources.

Michael MinockView profile

Associate Professor

• Natural Language Interfaces
• Knowledge Representation
• Databases
• +4 more

Michael Minock is an Associate Professor in Computing Science at Umeå University. His research focuses on the intersection of AI and relational databases, particularly in natural language interfaces, semantic tractability, and knowledge representation. He has led significant projects such as the VR grant (2015-04953) and EU STREP SpaceBook (2011–2014). Minock also co-founded C-Phrase Technologies AB and teaches courses on databases and large language models. B.S. in Computer Science (Honors), University of Michigan Ph.D. in Computer Science, UCLA His research explores the logical foundations of database query languages, emphasizing higher-order logic in natural language interfaces. Recent work integrates LLMs into database management systems. Key publication trends include query containment analysis, cooperative question answering, and context-aware navigation systems. Subfields span natural language processing, spatial databases, and semantic reasoning. Scientific Awards: Pell Grants Michigan Competitive Scholarship DARPA AASERT Award Minock's teaching includes courses on database management and LLM applications in IT. He actively contributes to open-access publications and maintains a strong interdisciplinary focus across AI, logic, and real-world database challenges.

• Rural Electrification
• Distribution Networks
• Energy Access Planning
• +7 more

Pedro Ciller Cutillas is a Lecturer at the Institute for Research in Technology (IIT), School of Engineering, Comillas Pontifical University, Madrid, Spain. His work is centered on rural electrification, distribution networks, and optimization-based planning tools for energy access in developing regions. He is actively involved in international energy planning projects and research. Industrial Engineering, Pontifical University of Comillas (2012) Mathematics, Complutense University of Madrid (2014) Ph.D. in Engineering, Pontifical University of Comillas (2021) Dr. Ciller's research focuses on rural electrification , energy access planning , and optimization modeling . His work integrates geospatial analysis , techno-economic modeling , and decentralized energy systems , with a strong emphasis on solving real-world energy challenges in underserved areas. He is a key contributor to the Reference Electrification Model (REM) , a decision-support tool for least-cost electrification planning. His recent publications reflect a consistent trend in energy access modeling , mini-grid optimization , and integrated electrification planning . These works combine computational intelligence , energy policy , and sustainable development to address global energy poverty. His research is published in high-impact journals such as Renewable & Sustainable Energy Reviews , Energies , and Proceedings of the IEEE . Scientific awards include: Honorary Distinction for the best Doctoral Thesis in Engineering, Comillas Pontifical University (2022) Recognition in national and international mathematics olympiads, including the International Mathematics Competition (IMC) Dr. Ciller has contributed to over 20 international consultancy projects funded by the World Bank , Inter-American Development Bank , MIT , International Energy Agency , and Shell Foundation . These projects involve national electrification strategies, geospatial planning, and mini-grid deployment in countries such as Rwanda, Uganda, Indonesia, and Paraguay. He collaborates with interdisciplinary teams and leads technical modeling components. His future work continues to advance tools for equitable and sustainable energy access. He is affiliated with research teams focused on energy systems modeling , smart grids , and decentralized energy solutions at IIT. His lab work involves synthetic grid modeling (e.g., Smart-DS project) and the development of decision-support platforms for energy planners.

Michelle MillerView profile

Research Professor

• Food Systems
• Sustainable Agriculture
• Regional Planning
• +5 more

Michelle Miller is a practicing economic anthropologist at the University of Wisconsin-Madison Center for Integrated Agricultural Systems, focusing on participatory action research in food systems. Her work bridges regional markets, climate adaptation, and agricultural logistics. PhD in landscape architecture with emphasis on regional planning and restorative ecology from UW-Madison MS in sustainable development from the Nelson Institute, UW-Madison Her research explores food systems , sustainable agriculture , and supply chain logistics , with recent articles analyzing network centrality in food distribution, climate resilience strategies, and geospatial knowledge graphs for smart foodsheds. Current projects include Tribal Food Sovereignty, smart foodsheds with AI, and Grassland 2.0 for agroecological transformation. Notable scientific awards include the Pesticide Environmental Stewardship Award (2005, 2003) and the 2011 Wisconsin Idea Award. She has guest lectured at Johns Hopkins Bloomberg School of Public Health and UW-Madison, covering systems thinking, food policy, and resilience.

• Time Series Analysis
• Machine Learning
• Solar Physics
• +3 more

Shah Hamdi serves as an Assistant Professor in the Computer Science Department at Utah State University's College of Engineering. His research bridges machine learning and space physics, with emphasis on time series analysis for solar phenomena prediction and explainable AI systems. His primary research focuses include Time Series Analysis for space weather forecasting, Solar Physics applications in flare and particle event prediction, Explainable AI through counterfactual methods, and Natural Language Processing for social media analysis. He develops novel frameworks for multivariate time series classification, data augmentation of imbalanced datasets, and interpretable model architectures that handle complex spatio-temporal patterns. Analysis of his recent publications reveals three dominant trends: (1) Application of graph neural networks and multimodal fusion to solar flare prediction using photospheric magnetic field data, (2) Development of shapelet-based and saliency-guided counterfactual explanation techniques for time series classification, and (3) Creation of generative models like ChronoGAN and AVATAR for synthetic time series data augmentation. His work consistently addresses challenges in imbalanced data and space weather forecasting accuracy. Dr. Hamdi leads collaborative research initiatives including the CAIG project for synthetic data generation in solar energetic particle events. His grant portfolio demonstrates expertise in securing funding for interdisciplinary space weather and machine learning projects, while his advising focuses on training graduate students in time series analysis and explainable AI methodologies for real-world applications.

Claudia Martins AntunesView profile

Associate Professor

• Data Science
• Temporal Data Mining
• Association Rule Mining
• +4 more

Claudia Martins Antunes is an Associate Professor at the Department of Computer Engineering, Instituto Superior Técnico, Universidade de Lisboa. Her work focuses on data mining, pattern discovery, and knowledge integration across diverse domains including healthcare, education, and bioacoustics. Primary Affiliation: Instituto Superior Técnico, Universidade de Lisboa Academic Role: Associate Professor Research Interests: Claudia specializes in data mining methodologies that incorporate domain knowledge, with particular emphasis on temporal data analysis and structured pattern mining. Her research spans healthcare analytics, educational data modeling, and multi-dimensional pattern discovery. Temporal data mining Constraint-based pattern discovery Knowledge-driven data analysis Healthcare data repositories Publication Trends: Claudia's work demonstrates consistent innovation in pattern mining techniques applied to healthcare and educational contexts. Recent publications focus on blockchain data analysis, urban planning applications, and advanced feature engineering methods, while earlier works established foundations in student modeling and sequential pattern mining. Teaching Activities: She teaches courses in Programming for Data Science, Data Science fundamentals, and Computer Engineering, alongside supervision of integrative projects in Industrial Engineering and Management.

Rune Hylsberg JacobsenView profile

Professor

• Energy Systems
• Drone Technology
• Blockchain Applications
• +12 more

Rune Hylsberg Jacobsen is a Professor at the Department of Electrical and Computer Engineering, Aarhus University. His work bridges energy systems, drone technology, and blockchain applications, with a focus on smart grids, autonomous systems, and decentralized infrastructure. Research interests include: Security frameworks for prosumer-driven energy systems using blockchain mmWave and LEO satellite communication protocols Homomorphic encryption for smart meter privacy Cooperative drone swarm navigation and infrastructure inspection Earth observation via CubeSats for climate research His recent publications highlight advancements in: Zero trust security models for renewable energy certificates Transport protocol optimization in satellite networks AI-driven charging window scheduling for drone fleets Decentralized identity management in Web3 infrastructure Key projects include: DISCO-2: Student CubeSat for Arctic climate monitoring Drones4Safety: Safety-critical inspection systems VPP4SGR: Virtual Power Plant networks

• Digital Representation
• Metadata
• Cultural Heritage
• +5 more

Lais Barbudo Carrasco is a Senior Lecturer in Archival and Information Science at Mid Sweden University's Department of Communication, Quality Technology and Information Systems (KKI). Her academic credentials include a 2019 PhD in Information Science and a 2024 Master's by Research in Media and Communication Science from MIUN. Bachelor's in Library and Information Science Master's in European Digital Media and Cultural Heritage Studies Her research focuses on digital representation , metadata , and cultural heritage with active participation in international initiatives like: Europeana Data Model (EDM) CIDOC CRM Interest Group International Council on Archives (ICA) RiC Model Current projects emphasize Indigenous data governance , exploring: CARE Principles (Collective Benefit, Authority to Control, Responsibility, Ethics) Local Contexts Labels for culturally appropriate data management Participatory approaches to Indigenous knowledge systems Parallel research examines AI ethics and algorithmic bias , addressing: Power dynamics in digital systems Justice-driven technological frameworks Human-centered information governance She applies social constructionist and DEI (Diversity, Equity, Inclusion) perspectives in: Strategic communication for cultural organizations Transformative meaning-making practices Digital preservation ethics

Ismail El MadafriView profile

Assistant Professor

• Applied Artificial Intelligence
• Sustainability
• Innovation in Higher Education
• +3 more

Ismail El Madafri is an Assistant Professor in the Graphic and Design Engineering Department at Universitat Politècnica de Catalunya (UPC), Spain. He holds a Mathematics degree from Université de Strasbourg (2020) and a Master in Didactics of Mathematics from Universitat Autònoma de Barcelona (2021). Currently pursuing a PhD in Sustainability at UPC, his academic work bridges engineering and AI-driven educational innovation. Education: B.Sc. Mathematics (Université de Strasbourg, 2020) M.Sc. Didactics of Mathematics (Universitat Autònoma de Barcelona, 2021) PhD in Sustainability (Universitat Politècnica de Catalunya, ongoing) Research Focus: Applying AI to sustainability challenges and innovating higher education through blended human-AI creative processes. His recent work explores wildfire detection algorithms, generative AI in education, and environmental simulation tools. Scientific Production: Focused on AI models for forest fire detection, educational technology integration, and environmental data analysis. Publications include lightweight CNN architectures, domain adaptation techniques, and open-source wildfire datasets.

İBRAHİM RIZA HALLAÇView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Parallel and Distributed Systems
• +3 more

İbrahim Rıza Hallaç is an Assistant Professor at RAFET KAYIŞ FACULTY OF ENGINEERING, Department of Computer Engineering. He holds a PhD (2021), Master's (2014), and Bachelor's (2011) in Computer Engineering from Firat University, with doctoral research on social media analysis using deep learning methods under advisor Galip Aydın. His research focuses on: Artificial Intelligence and language models Machine learning for distributed systems Big data analytics and cloud computing Natural language processing applications Social media sentiment analysis Radar-based object detection systems His publications (2015-2024) demonstrate consistent focus on machine learning applications across diverse domains including social network analysis, distributed computing, Turkish NLP, and sensor data processing. Recent works emphasize radar-based multi-target detection (2024), credit scoring models (2023), and wind speed estimation (2023), reflecting expertise in scaling AI solutions for real-world problems. He has led four national R&D projects: Deep Learning-Based Face Recognition Authorization System (2017-2019) DEĞİRMEN Big Data Analysis Platform for Turkish Armed Forces (2017-2021) KOSGEB Innovation Program for Face Recognition Systems (2017-2019) Cloud-Based Social Media Reputation Analysis System (2015-2017) No students, awards, or contact emails are documented in available records.

Harald GjermundrodView profile

Professor

• Distributed Computing Systems
• Computer Security
• Cyber Security
• +5 more

Harald Gjermundrod is a Professor of Computer Science in the Department of Computer Science at the University of Nicosia's School of Sciences and Engineering, where he has served since September 2008. Previously, he was a Post-Doctorate Associate (2006-2008) at the High-Performance Computing Systems Laboratory, University of Cyprus. Education: PhD in Computer Science, Washington State University (2006) MS in Computer Science, Washington State University (2001) BS in Computer Science, Washington State University (1999) Dipl.-Ing, Oslo Metropolitan University (1998) His research spans Distributed Computing Systems , Computer Security , Blockchain Technology , and Data Protection , with emphasis on practical implementations like GridStat middleware for power grids and privacyTracker for GDPR compliance. As co-director of the Informatics Security Laboratory, he leads research on active defense mechanisms and data protection frameworks. His 87+ publications since 2001 show evolving focus from grid computing (2006-2010) to blockchain applications (2016-present), with recent work emphasizing privacy engineering, secure e-voting, and AI-driven security analytics. Key trends include transitioning from infrastructure-focused research (GridStat, ICGrid) to policy-compliant systems (privacyTracker, HoneyCY). Scientific Recognition: Senior Member of ACM Eclipse Committer Status (Alumni) Chair of ACM Cyprus Chapter (2015-2018) He has secured funding from EU programs, US National Science Foundation, and National Institute of Technology, developing software products including GridStat (critical infrastructure middleware), ICGrid (medical data sharing), and ReProTool (Bologna Process compliance). His supervision has produced student-developed prototypes like NoteLocker and HoneyCY. Current projects include privacyTracker (GDPR compliance) and SmartTeV (blockchain voting). As co-director of the Informatics Security Laboratory, he oversees research on active defense systems, honeypot frameworks (HoneyCY), and verifiable data traceability, with applications in e-government and healthcare security.

Andreas DengelView profile

Professor

• Machine Learning
• Deep Learning
• Pattern Recognition
• +6 more

Andreas Dengel is a Professor of Computer Science at the Rhineland-Palatinate University of Technology (RPTU) and Managing Director of the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern. He leads the Smart Data & Knowledge Services research area and the DFKI Deep Learning Competence Center, with additional professorship rights at Osaka Metropolitan University since 2009. He earned his doctorate in Computer Science from the University of Stuttgart in 1989 following undergraduate studies in Computer Science with Economics at RPTU (then TU Kaiserslautern). His academic promotions progressed from C3 to W3 Professor at RPTU between 1993-2013. Professor Dengel's research centers on Machine Learning and Pattern Recognition with applications in Earth Observation, document analysis, and semantic technologies. His work bridges theoretical AI with industrial implementation, notably through NVIDIA-certified deep learning frameworks and multi-institutional Earth Observation projects. He pioneered quantified learning approaches for satellite data interpretation and human behavior modeling. Recent publications (2025) demonstrate his focus on diffusion models for geospatial data, multi-view learning for missing Earth Observation data, and gaze-based confidence detection systems. These works highlight cross-disciplinary integration of deep learning with environmental science and human-computer interaction. His extensive honors include: ICDAR Outstanding Achievement Award (2019) Order of the Rising Sun with Gold Rays (2021) Order of Merit of Rhineland-Palatinate (2022) IAPR Fellow distinction NVIDIA Pioneer Award He has secured over €100 million in third-party funding and supervised 500+ theses. His MIND graduate school supports 20 industry-sponsored students, while international exchange programs with Japanese institutions foster cross-border research. As FFPA Chairman and acatech member, he shapes national AI policy including Germany's "Lernende Systeme" platform. His leadership spans DFKI's Deep Learning Competence Center (NVIDIA Excellence Program awardee) and research groups developing Robust Machine Learning for defense systems, Ageing Smart environments, and Earth Digital Twin technologies through EU and national projects.

Tim McCarthyView profile

Professor

• Geospatial Data Science
• Earth Observation
• Remote Sensing
• +7 more

Tim McCarthy is a Professor in the Department of Computer Science at Maynooth University's Faculty of Science & Engineering. He serves as Principal Investigator at the National Centre for Geocomputation (NCG) and co-PI in over 32 externally funded projects totaling €28 million. His work focuses on Earth Observation, geospatial data science, and autonomous systems with applications in environmental monitoring, precision agriculture, and emergency management. Developed Terrain-AI for greenhouse gas emission monitoring Led CoPilot-AI for wildfire response systems Contributed to Copernicus Academy as Irish National Delegate Created U-Flyte drone research program His geospatial research integrates satellite, airborne, and terrestrial sensor data with machine learning for petabyte-scale environmental analytics. Applications span critical infrastructure, climate change mitigation, and marine coastal management through projects like MaCoBioS . He has spun out 2 university companies and supervises 4 current PhD/MRes students. Scientific Awards : 2023 SFI Defence Organisation Innovation Challenge 2022 US-Ireland Research Innovation Award 2021 AI Awards (Social Good) 2012 Copernicus Masters 2011 Maynooth Commercialisation Award Active in EU committees like the Copernicus Earth Observation program and Irish drone policy development through the Aerial and Drone Oversight Committee . His outreach includes media engagements with RTE, Sunday Times, and Silicon Republic on geospatial AI and drone technology.