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Bettina SpeckmannView profile

Professor

• Computational Geometry
• Geometric Algorithms
• Spatial Data Analysis
• +12 more

Bettina Speckmann is a full Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU Eindhoven), where she leads the Applied Geometric Algorithms group. She holds additional appointments as EAISI Health Professor and EAISI Foundational Professor, and is affiliated with the Data Science Center Eindhoven. Her research bridges theoretical algorithm design with practical applications in spatial computing. Her research interests lie primarily in computational geometry and geometric algorithms, with strong applications in GIScience, Smart Mobility (including moving object analysis and automated cartography), geo-visualization, visual analytics, and e-Humanities. She focuses on developing efficient algorithms and data structures for spatial data, combining rigorous theoretical methods with practical engineering for real-world impact. Her recent publications (2025) show a strong trend in geometric data processing, particularly in polycube segmentations, dual loop algorithms, density estimation for moving groups, and topological analysis using merge trees and Fréchet distances. These works reflect her interdisciplinary focus on computational geometry, visualization, and data structures. Scientific awards received include: Netherlands Prize for ICT Research (2011) NWO Vici Award (2012) PEriTiA Prize (2020) Bettina Speckmann has advised numerous students and researchers through her group and has secured major grants, including the NWO Vici. She has served in leadership roles such as PC co-chair for Graph Drawing (GD 2011), PC chair for ICALP Track A (2015), and PC co-chair for SoCG (2018). She teaches courses such as Data Structures and Heuristic Algorithms. She leads the Applied Geometric Algorithms group, which actively collaborates with industry partners like HERE Global B.V., Fugro NL Land B.V., and OCLC B.V., and contributes to UN Sustainable Development Goals in areas related to data and mobility.

Tarique AnwarView profile

Lecturer

• Data Science
• Machine Learning
• Artificial Intelligence
• +3 more

Tarique Anwar is a Lecturer in the Department of Computer Science at the University of York, UK, since 2021. He holds a PhD in Data Science from Swinburne University of Technology, Australia. As Programme Leader for both MSc and BSc Data Science programs, he oversees admissions and curriculum development. His academic roles include Data Science Admissions Tutor and membership in the Artificial Intelligence Group. Research interests span Data Science , Machine Learning , Artificial Intelligence , and Social Media Analysis , with a focus on solving real-world problems using structured/unstructured data. Notable areas include mental health detection via social media, network analysis, and explainable AI. His work addresses challenges in: Depression severity assessment using attention mechanisms Rumor source detection in social networks Eating disorder classification through multimodal learning Geographic-social-temporal pattern mining He supervises MS/PhD students in Data Science and Analytics, emphasizing interdisciplinary research. His publications (2020–2025) demonstrate expertise in AI applications for health, social networks, and optimization. Labs/Teams: Artificial Intelligence Group at the University of York, collaborating with industry and academic partners globally.

Erkut AkdagView profile

Researcher

• Anomaly Detection
• Artificial Intelligence
• Smart City Surveillance
• +4 more

Erkut Akdag is a postdoctoral researcher at the Department of Electrical Engineering, Eindhoven University of Technology, specializing in artificial intelligence for smart city surveillance and transportation systems. He contributes to projects focused on anomaly detection, urban mobility optimization, and multi-modal sensing. Academic Rank: Researcher Contact: e.akdag@tue.nl His research addresses UN Sustainable Development Goals related to sustainable cities (SDG 11) and infrastructure (SDG 9). Key interests include Anomaly Detection , Smart City Surveillance , and Intelligent Transportation Systems . Recent publications emphasize anomaly detection in surveillance videos, distracted driver action recognition, and geo-spatial traffic analysis. His work leverages deep learning , temporal attention mechanisms , and spatio-temporal embeddings . Scientific recognition includes the Award for Exceptional Excellence (ITEA 2024) for collaborative research projects. He actively presents at international conferences, including IEEE VCIP and ICMV.

• Cloud Computing
• Big Data
• Smart Cities
• +4 more

Gregory Van Seghbroeck is a postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology (EA05). His work focuses on cloud computing, big data systems, and smart city infrastructure. Affiliation: Ghent University, IMEC research group Research Trends: Data placement optimization, workflow management, low-latency querying, cloud-native architectures He has supervised doctoral projects including Thomas Vanhove (2018), Leandro Ordonez Ante (2022), and currently advises Stan De Groeve (2025-2026). Key publication themes include: Interactive querying for latency-sensitive applications Geo-distributed data/cloud resource allocation Workflow orchestration frameworks (BRAHMA, SpeCH) Smart city data infrastructure

Volker MarklView profile

Professor

• Database Systems
• Big Data Analytics
• Stream Processing
• +5 more

Prof. Dr. Volker Markl is a leading researcher in database systems and information management, currently heading the Databases and Information Systems Group (DIMA) at the Technical University of Berlin and holding the Chair of Database Systems and Information Management at the Technical University of Dresden. He also leads the research group "Intelligent Analysis of Big Data – Smart Data" at the German Research Center for Artificial Intelligence (DFKI). Previously, Dr. Markl led research groups at FORWISS in Munich and at IBM Almaden Research Center in San Jose, CA. His research focuses on database systems, big data analytics, stream processing, and distributed systems, with particular emphasis on hardware-accelerated data processing and cloud-edge continuum processing. Markl's work bridges theoretical foundations with practical implementations, as evidenced by systems like NebulaStream for stream processing across the cloud-edge continuum. Markl's recent publications reveal a strong focus on stream processing challenges in distributed environments, data transfer optimization across heterogeneous systems, and leveraging machine learning for system optimization. His work on incremental query deployment and placement addresses critical challenges in volatile infrastructures where nodes frequently join or leave the system. Best Paper Award at ICDE 2025 for work on stream query placement General Chair of SIGMOD/PODS 2025 in Berlin Recipient of multiple research grants and industry collaborations As an advisor, Markl guides doctoral students like Xenofon Chatziliadis (working on geo-distributed stream processing) and Adrian Michalke (working on data extraction process analysis). His research group has secured 14 Software Campus grants since 2014, highlighting their strong industry connections and practical research impact. Markl's team actively contributes to major database conferences including SIGMOD, VLDB, and ICDE, with recent work spanning from foundational query processing techniques to practical applications in areas like mobility data processing and medical AI.

• Machine Learning
• Natural Language Processing
• Climate Modeling
• +6 more

Professor Andreas Hotho leads the Data Science Chair at the University of Würzburg's Faculty of Mathematics and Computer Science, while serving as founding spokesman of the Center for Artificial Intelligence and Data Science (CAIDAS). His academic journey includes senior researcher roles at the University of Kassel and research positions at the AIFB Institute (University of Karlsruhe) and L3S Research Center (Hannover). Research Focus: Machine Learning, Large Language Models, Climate Modeling, Semantic Web, and Knowledge Graphs Methodologies: ConvMOS architecture, DenseLoss approach, LanZ-DML, and BibSonomy system Applications: Environmental monitoring, Digital Humanities, Smart Beehive Analysis, and Social Media Analytics With over 15 recent publications, his work spans climate model output statistics, zero-shot metric learning, imbalanced regression techniques, and German language model development. He serves as editor-in-chief for the Transactions on Graph Data and Knowledge and maintains active roles in academic governance through PC memberships at ECML-PKDD, WWW, and other major conferences. Scientific awards include: NAACL 2024 Best Paper Honorable Mention ICDM 2023 Best Paper Award NeurIPS 2020 Best ML Innovation Award ISWC 2018 SWSA Ten-Year Award WWW 2015 Best Paper Award FAIML 2020 Best Student Paper His research group includes doctoral researchers like Jan Pfister, Julia Wunderle, and Albin Zehe. Key projects include LitBERT for literary analysis, BigData@Geo series for climate modeling, and BeeConnected for ecosystem monitoring.

Emmanuel MELINView profile

Lecturer

• Parallel Computing
• Virtual Reality
• Scientific Visualization
• +5 more

Emmanuel MELIN is a Lecturer at the University of Orléans, affiliated with the PaMDA team at LIFO (Laboratory of Fundamental Computer Science of Orléans). He teaches primarily at the IUT (University Institute of Technology) of Computer Science in Orléans. His research focuses on parallel computing, virtual reality, and scientific visualization using PC clusters and distributed architectures. Research Interests: His work spans parallel algorithms for geospatial processing, high-performance computing for environmental simulations, and distributed virtual reality systems. He has contributed to large-scale VR frameworks and open-source software like FlowVR, NetJuggler, and SoftGenLock. Publications: Dr. MELIN has authored 31 HAL publications, with 15 most recent covering GPU-accelerated finite element methods, tele-immersive grids, distributed application mapping, and geospatial data processing. His work intersects computer science, environmental modeling, and 3D rendering. Labs & Collaborations: He collaborates with INRIA and has participated in projects like RNTL Geobench, ANR DALIA, and Extengis. His software tools are available as open-source projects.

Gim Hee LeeView profile

Associate Professor

• Computer Vision
• Robotic Perception
• Machine Learning
• +3 more

Gim Hee Lee is an Associate Professor at the Department of Computer Science, National University of Singapore (NUS) School of Computing. He heads the Computer Vision and Robotic Perception (CVRP) Laboratory and holds affiliations with the NUS Graduate School for Integrative Sciences and Engineering (NGS) and the NUS Institute of Data Science (IDS). Previously, he was a researcher at Mitsubishi Electric Research Laboratories (MERL) in the USA and worked at DSO National Laboratories in Singapore. Education Dr.sc. in Computer Science from ETH Zurich M.Eng. in Mechanical Engineering from National University of Singapore B.Eng. in Mechanical Engineering (1st Class Honors) from National University of Singapore Research Interests Professor Lee's research focuses on Computer Vision , Robotic Perception , and Machine Learning , with specialization in dynamic 3D scene reconstruction, event camera applications, and cross-view geo-localization. His work bridges theoretical advances with real-world applications in autonomous systems and augmented reality, particularly through neural scene representations and geometric computer vision techniques. Scientific Awards Faculty Teaching Excellence Award (AY 2018/19 and AY 2017/18) CVPR 2014 Doctoral Consortium Travel Award Finalist for IROS 2012 Best Paper Award Finalist for IROS 2012 Best Video Award Advising and Service Professor Lee serves as Associate Editor for IJCV and has held Area Chair positions for major conferences including CVPR, ICCV, ECCV, and NeurIPS. He chaired Program Committees for 3DV 2022 and served as Demo Chair for CVPR 2023. His research on the Cross-View Matching Network (CVM-Net) demonstrated real-time ground-to-aerial geo-localization in moving vehicles, establishing a benchmark for subsequent research in this field. Laboratories He leads the Computer Vision and Robotic Perception (CVRP) Laboratory at NUS, which develops cutting-edge solutions for 3D scene understanding, event-based vision, and robotic navigation. The lab's work includes acquiring high-quality datasets for dynamic scene reconstruction and advancing neural representations for real-world applications.

• AI for Networking
• Edge Computing
• Federated Learning
• +4 more

Prof. Xiaoming Fu is a Professor at the Institute of Computer Science, University of Göttingen, and Head of the Computer Networks Group. He also holds a secondary membership in the Center for Statistics. His research focuses on AI-driven networking, edge computing, federated learning, and video analytics. He teaches courses such as 'AI-Empowered Networking and Mobile Communications' and oversees multiple seminars and internships on topics like smart cities and network optimization. His work spans technical innovations in multi-agent systems, low-latency services, and resource-efficient machine learning. Recent articles highlight contributions to neural-optimized video streaming, privacy-preserving MARL, and cross-modal social media analysis. He collaborates on interdisciplinary projects involving mobility data, socioeconomic analysis, and quantum networks. Prof. Fu’s research integrates reinforcement learning, network architecture design, and distributed systems to address challenges in 5G/6G, IoT, and smart cities. His lab develops solutions for edge-cloud orchestration, video quality adaptation, and resilient resource allocation, with applications in both academia and industry.

Arthur J. Lembo, Jr.View profile

Professor

• Geographic Information Systems (GIS)
• Geo-processing and spatial analysis
• Enterprise GIS implementation
• +7 more

Arthur J. Lembo, Jr. is Professor of Geography and Geoscience in the Henson School of Science and Technology at Salisbury University, where he has taught since 2007. He also serves as Technical Director of the Eastern Shore Regional GIS Cooperative, leading regional geospatial initiatives and enterprise GIS projects. Education B.S. Geography, State University of New York, College at Oneonta (1987) M.A. Geography, State University of New York at Buffalo (1989) Ph.D. Environmental Resource Engineering, State University of New York, College of Environmental Science and Forestry (1997) Research Interests Dr. Lembo’s research centers on the innovative application of advanced information technologies to geographic problem-solving. His work explores high-performance GIS through massively-parallel algorithms executed on consumer-grade gaming GPUs, enterprise-scale spatial database design, and the integration of smartphone sensors into real-time mapping workflows. Additional emphases include spatial decision support for post-disaster infrastructure assessment, crime analysis, and sustainable local food systems. Selected Research Sponsors National Science Foundation (multiple awards) W. K. Kellogg Foundation Maryland Governor’s Office of Crime Control and Prevention Maryland Department of Natural Resources USDA and NASA Scientific Awards 2018 State of Maryland Board of Regents Award for Teaching Excellence 2006 SUNY Chancellor’s Award for Excellence in Teaching 2005 NACTA Teacher of Merit 2003 CALS Faculty Innovation in Teaching Award, Cornell University 2002 & 2003 Master Teacher recognition, University of Mississippi & NASA Teaching & Outreach Dr. Lembo teaches core GIS programming and spatial-analysis courses (GEOG 204, 415, 419, 435, 619) and maintains an active extension program that provides GIS solutions for municipalities, first responders, and environmental NGOs such as The Nature Conservancy and the City of Havre de Grace. Lab & Teams He directs the Eastern Shore Regional GIS Cooperative, a university-based center that supports multi-agency geospatial data sharing and application development across Maryland’s Eastern Shore.

Tobias WernerView profile

Researcher

• Geospatial Data
• GIS
• Spatio-Temporal Data Analysis
• +3 more

Tobias Werner, M.Sc., is an active researcher at the IAPG Institute of the University of Oldenburg, specializing in geospatial data analysis and spatio-temporal systems. His work bridges theoretical research and practical applications in GIS, digital tachograph analysis, and 3D metrology, with significant contributions to projects funded by governmental bodies like the Federal Office for Cartography and Geodesy. Werner's research focuses on geospatial data management, spatio-temporal analytics, and privacy-aware route reconstruction. His expertise spans machine learning applications in sovereign cloud environments, participatory GIS development, and sustainable geospatial toolboxes for rural transformation. He collaborates extensively with institutions including Leibniz University Hannover on cutting-edge geospatial initiatives. Recent publications reveal a strong trajectory toward cloud-based geospatial processing, real-time data stream management for autonomous systems, and privacy-preserving methodologies in transportation data. His work consistently leverages open-source tools like OpenStreetMap and SpatiaLite while addressing critical challenges in data sovereignty and temporal analysis. Scientific Awards: No scientific awards listed in available documentation Werner contributes to multiple funded projects including "Evaluation of driving data from digital tachographs" (Central Criminal Investigation Department), "The temporal change of geodata" (Federal Office for Cartography and Geodesy), and "Northwest Lower Saxony Sustainable New (4N): Geo-Toolbox" (Lower Saxony Advance). No thesis supervision activities are documented. He operates within the Laboratory for optical 3D metrology, collaborating with senior researchers like Prof. Dr. Thomas Brinkhoff on geospatial data transformation frameworks. His technical leadership manifests in developing specialized applications for route reconstruction from tachograph data and creating the Geo-Toolbox platform for sustainable rural development, demonstrating applied research impact across law enforcement, cartography, and environmental planning domains.

Agustín Rafael Trujillo PinoView profile

Professor

• Geographic Information Systems (GIS)
• Big Data Processing
• Spatial Data Analysis
• +3 more

Agustín Rafael Trujillo Pino is a University Professor at the Department of Computer Science and Systems within the School of Cybernetics, Business and Society at the University of Las Palmas de Gran Canaria. His research focuses on geographic information systems, big data processing, and spatial data analysis, with a particular emphasis on virtual globes and geospatial visualization tools. Education: PhD in Sub-pixel Contour Detection in 2D/3D Images (2004), advised by Luis Miguel Álvarez León, Julio Esclarín Monreal, and Miguel Alemán Flores. As a member of the GIR IUCES research group, he has supervised doctoral theses on vector streaming for Big Geo Data, smart GIS applications, and mobile/web-based virtual globes. His work bridges engineering, computer science, and geospatial technologies. Research Activity: He specializes in geographic information systems, geospatial data visualization, and scalable processing of spatial datasets. His contributions include methodologies for virtual globes and real-time geospatial data streaming. Students: Pablo Fernández Moniz (2024), Sebastián Eleazar Ortega Trujillo (2021), José Miguel Santana Núñez (2017). Labs and Teams: Active in the GIR IUCES research group, focusing on image technologies and geospatial systems within the broader Institute of Cybernetics, Business, and Society.

• Internet of Things
• Smart Cities
• Digital Transformation
• +7 more

Bocchi Yann serves as an Ordinary Professor at HES-SO Valais-Wallis' School of Management within the Economy and Services faculty. His primary appointment resides in the Computer Science Institute where he leads research in Internet of Things, Smart Cities, and Digital Transformation. His work bridges technical innovation with practical urban applications through multiple EU and Swiss-funded projects. His research focuses on IoT architecture design, context-aware systems, and human-centered smart environments. Key contributions include nomadic sensing platforms for cost-effective smart city implementation, geo-aware security protocols for cross-border data exchange, and semantic middleware for heterogeneous technology integration. His work demonstrates particular expertise in translating theoretical IoT concepts into deployable urban solutions through the SmartSantander testbed and other large-scale validations. His publication portfolio reveals consistent focus on IoT interoperability (2014-2017), evolving toward human-centric applications (2015-2016) and energy-aware systems (2020). The research trajectory shows progression from foundational protocol development toward practical smart city implementations with emphasis on data valorization and user experience. As principal investigator, he has secured significant research funding including 820,415 CHF for the EU IoT6 project and 50,000 CHF for GeoTrust from the Hasler Foundation. His collaborative approach spans academic institutions, Swiss cantons, and industry partners including Nokia Research Labs and PostAuto Schweiz AG. He directs the Management Information Technology program while teaching Systems Modeling in the BSc HES-SO in Management Information Systems. His industry partnerships demonstrate strong technology transfer capability, particularly in tourism management systems and cultural heritage applications.

Hermann StrackView profile

Professor

• Network Security
• E-Government
• IT Security
• +5 more

Prof. Hermann Strack is a Professor of Network Management and Practical Computer Science at Harz University of Applied Sciences since 2000. He leads the Computer Science/E-Administration program and chairs the Competence Center at the university. His research focuses on cybersecurity, e-government, and digital transformation in education, with projects funded by the EU, German federal/state governments, and industry. Education: Doctorate in Engineering (2000), University of Karlsruhe State Examinations in Mathematics/Physics (1983-1985), University of Münster Diploma in Mathematics (1986), University of Münster His teaching spans Bachelor/Master programs in Computer Science, E-Administration, and Mobile Systems. Key research areas include security for distributed systems, eIDAS compliance, and secure e-government infrastructure. He collaborates on EU projects like StudIES+ and SHIELD, and leads initiatives such as the CyberSecurity Association in Saxony-Anhalt. Grants & Projects: EU CEF projects (StudIES+, TREATS) German Federal Ministry (BMBF) Education Platform project KAT-InnoLab SecInfPro-Geo (Competence Center) He is actively involved in academic governance, serving on ICT commissions and state-level university bodies. His work bridges technical security solutions with policy-driven digital transformation in public administration and education.

Dev Raj PaudyalView profile

Lecturer

• Spatial Data Infrastructure
• Land Administration
• Geospatial Engineering
• +13 more

Dr. Dev Raj Paudyal is a Lecturer in Surveying and Spatial Science at the School of Surveying and Built Environment, University of Southern Queensland (UniSQ), where he has been a full-time faculty member since 2010. He also serves as a Visiting Professor at Nepal Open University and the Institute of Engineering (WRC), Tribhuvan University. His academic journey includes significant contributions to curriculum development for Master's programs in Geoinformatics and Geospatial Engineering in Nepal. His educational background includes a BSc from Tribhuvan University (1995), an MSc in Land Administration from ITC, Netherlands (2005), and a PhD from University of Southern Queensland (2013). He has held prestigious positions including Research Scholar at Harvard University's Graduate School of Design (2015-2016) and Visiting Professor at the University of Maine. Dr. Paudyal's research spans Spatial Data Infrastructure, Land Administration, Digital Twins, and Climate Resilience with applications in Hydrology, Urban Planning, and Natural Resource Management. His expertise in mixed methods research has informed his work on Pro-poor Land Management and Informal Settlements Regularisation, particularly in developing country contexts like Nepal. His recent publications demonstrate a strong focus on integrating AI and advanced spatial analysis techniques for environmental monitoring and resource management. His scholarly contributions include serving as Associate Editor for the ISPRS International Journal of Geo-Information and as President of the International Geospatial Society (2013-2015). He has received New Colombo Plan Mobility Grants in 2021 and 2024 for international research collaboration. With over 25 years of professional experience including 10 years in Nepal's Survey Department and 15 years in academia, Dr. Paudyal has supervised 25 successful postgraduate students and currently mentors 7 PhD and 4 honours students. His research has been supported by various grants, and he has contributed to developing spatial information sharing strategies across natural resource management communities. His leadership extends to roles as Co-Chair of ISPRS working groups and Board Director of the Global Spatial Data Infrastructure Association.