جست‌وجوی استاد و پژوهشگر

• Water Resources Management
• GIS and Remote Sensing Applications
• Educational Programmes Quality Control
• +۳ مورد دیگر

Arno van Lieshout holds dual academic appointments as a Lecturer in the Department of Water Resources and the Digital Society Institute. His work focuses on water resources management, GIS applications, and remote sensing education. He holds an MSc in Land and Water Management from Wageningen Agricultural University. His research emphasizes quality control in educational programs, remote sensing for salinity assessment in irrigated areas, and capacity building in emerging economies. Key areas of expertise include UN Sustainable Development Goals, particularly in water management and environmental sustainability. His publications span remote sensing education platforms, rainfall runoff modeling, and spatio-temporal analysis. He collaborates internationally on projects involving ILRI-Alterra and ITC-partners in Iran and Bolivia.

Barend Köbbenمشاهده پروفایل

مدرس ارشد

• GIS
• Cartography
• Web Mapping
• +۵ مورد دیگر

Barend Köbben is a Senior Lecturer/Researcher at the University of Twente's Faculty of Geo-Information Science and Earth Observation (ITC), within the Department of Geo-Information Processing (GIP). He holds an MSc in Geography (Cartography specialization) from Utrecht University and has over 25 years of academic experience, including a move to ITC in 1997. His work combines cartographic visualization with web application development, focusing on automated mapping in services environments. Education: MSc in Geography (Cartography), Utrecht University PhD equivalent via research contributions (not explicitly stated) Research Interests: Automated mapping pipelines using Open Web clients (D3, Vega-Lite) Thematic mapping and educational resources (THEMPE project) Spatiotemporal analytics in SDIs (Spatial Data Infrastructures) Standardization in cartography via ICA Working Group Teaching: Cartographic Theory, WebCartography, Geo-Webservices Coordinates Erasmus Mundus MSc program in Cartography Contributions: Over 1,000 maps/infographics published in Geografie Map Editor for KNAG journal Consultancy across 9 countries (Netherlands, India, etc.) Awards: 2020 Kartografieprijs for cartographic excellence Activities: Chair, ICA Working Group on Cartographic Standardization Member, Nederlandse Taalunie's Geographic Names Commission Keynotes on topics like 'Critical Map Literacy' Labs/Teams: Research Theme STAMP (Spatio-Temporal Analytics, Maps, Processing) OSGeo Foundation member Core contributor to Open Source GIS projects

Manmohan Chandrakerمشاهده پروفایل

استاد

• Computer Vision
• 3D Reconstruction
• Scene Understanding
• +۵ مورد دیگر

Manmohan Chandraker is a Professor in the Department of Computer Science & Engineering at the University of California, San Diego (UCSD). His research focuses on computer vision, 3D reconstruction, and autonomous driving, with interdisciplinary links to robotics, machine learning, and computer graphics. He leads projects in 3D scene understanding, real-time visual odometry, and sensor fusion for autonomous systems. His work has been recognized with top academic awards and industry accolades. Education: B.Tech. in Electrical Engineering from IIT Bombay Ph.D. in Computer Science from UCSD Postdoctoral scholarship at UC Berkeley Research Interests: Chandraker’s work addresses challenges in 3D reconstruction under complex lighting and materials, autonomous driving systems, and physically-based inverse rendering. Key contributions include frameworks for non-convex optimization in geometric reconstruction and semantic-driven dense reconstruction techniques. His research bridges theory and practice, with applications in augmented reality, robotics navigation, and automotive safety systems. Key Awards: Best Paper Award at CVPR 2014 UCSD CSE Dissertation Award (2009) NEC Business Contribution Award (2015) Advising & Industry Collaboration: He has led teams at NEC Labs America, collaborating with automobile manufacturers to develop single-camera systems for real-time autonomous driving. His work on closed-loop traffic simulations and adversarial scenario generation has driven industry partnerships and patents. Current research explores multimodal LLM integration, data-centric AI tools (e.g., Ymir platform), and photorealistic scene synthesis. Labs & Projects: Chandraker’s group develops open frameworks like OpenRooms and TRoVE for 3D scene datasets, and contributes to benchmarks for domain adaptation in vision tasks. His team also works on safety-critical systems for autonomous vehicles through projects like SAFE-SIM and Drive-1-to-3.

• Materials Science
• Computational Physics
• Workflow Systems
• +۳ مورد دیگر

Dr. Marian Bruns is a Postdoctoral Researcher at the Max Planck Institute for Sustainable Materials in Düsseldorf, affiliated with the Department of Computational Materials Design. His research focuses on atomistic simulations of metallic glasses, thermo-mechanical behavior analysis, and developing workflows for materials science. He contributes to digital infrastructure projects like NFDI-MatWerk and Platform Material Digital, specializing in integrating authentication systems, triplestores for semantic data, and computing services across cloud and HPC platforms. Education: PhD in Physics (2022), Ruhr-Universität Bochum: Thesis on simulating thermal cycling in metallic glasses under Prof. Steinbach and Prof. Varnik. M.Sc. in Physics (2017), Technische Universität Dortmund: Thesis on boundary-integral methods for active swimming capsules. B.Sc. in Physics (2015), Technische Universität Dortmund: Thesis on active Lennard-Jones particle simulations. His research interests bridge computational materials design, workflow development, and digital infrastructure for data-driven science. He actively collaborates on projects enhancing materials informatics through advanced computational frameworks.

• Geocollaboration
• Big Geospatial Data Analytics
• Geospatial Web Services
• +۷ مورد دیگر

Dr. Songnian Li is a Professor and Associate Chair (Graduate Studies) in the Department of Civil Engineering at Toronto Metropolitan University. He holds a B.Eng. from Wuhan Technical University of Surveying and Mapping (now Wuhan University) and a PhD from the University of New Brunswick. His research focuses on geocollaboration systems, big geospatial data analytics, human mobility patterns, and digital twins for smart cities. He leads the GIS and GeoCollaboration Lab and has contributed to over 50 peer-reviewed publications. Dr. Li has received numerous accolades including the ISPRS Fellow (2020), 2023 Career Achievement Award, and multiple best paper awards. He serves as Editor-in-Chief of the Canadian Journal of Remote Sensing and holds editorial roles in several international journals. He actively supervises PhD and MASc students in geomatics engineering and related interdisciplinary programs. His professional memberships include leadership roles in the International Society for Photogrammetry and Remote Sensing (ISPRS), Canadian Society for Remote Sensing (CSRS), and the Canadian Institute of Geomatics. He has delivered keynote lectures globally and pioneered research in geosocial media analytics and spatio-temporal dynamics.

• Transportation and Freight Services
• Artificial Intelligence and Image Processing
• Automotive Engineering
• +۳ مورد دیگر

Felix Feng is a Research Fellow and Manager of the Intelligent Infrastructure and Transport Systems (IITS) Laboratory at Imperial College London's Department of Civil and Environmental Engineering. His work focuses on developing configurable automotive system models for autonomous vehicle safety validation and simulating interactions with road users. He holds a PhD in Mechanical Engineering (University of Bath, 2019), an MSc in Mechatronics (University of Bath, 2014), and a BEng in Mechatronics (Tongji University, 2013). Education: PhD in Mechanical Engineering, University of Bath (2015–2019) MSc in Mechatronics Engineering, University of Bath (2013–2014) BEng in Mechatronics Engineering, Tongji University (2009–2013) His research interests span autonomous vehicles , artificial intelligence , transportation systems , and control systems . Notable projects include driver model development using machine learning and safety validation frameworks for autonomous systems. His recent work explores multimodal traffic prediction, digital twin simulations, and reinforcement learning applications in urban logistics. Publications highlight advancements in traffic risk modeling, GNSS fault detection, and smart infrastructure solutions. Felix’s contributions address challenges in autonomous vehicle interaction safety, urban mobility optimization, and resilient air traffic networks. His IITS Lab at Imperial focuses on translating cutting-edge research into practical tools for safer, smarter transportation ecosystems.

• Digital Transformation
• Knowledge Management
• Data Analysis
• +۷ مورد دیگر

Maria Sokhn is a Professor at the Haute Ecole Arc - Gestion within the University of Applied Sciences and Arts Western Switzerland (HES-SO). She leads the School of Economics and Services and specializes in digital transformation, knowledge management, and data-driven solutions. Her work bridges academia and industry through innovative projects addressing organizational digital maturity, sustainable supply chains, and accessibility technologies. Key Affiliations: HES-SO Master (Lausanne), Haute Ecole Arc (Neuchâtel) Research Focus: Digital transformation frameworks, blockchain applications, VR in tourism, and social innovation Research Interests: Sokhn's work spans digital maturity models for SMEs, blockchain-based supply chain transparency, and leveraging VR for destination branding. Her projects often integrate gamification and crowdsourcing to enhance data quality and user engagement. Notable collaborations include SwissRenov (circular economy in construction) and City-Stories (spatio-temporal data visualization). Grants & Projects: Over 20+ funded projects totaling CHF 2.5M+, including roles as Main Applicant for Digital Arc Hub (CHF 98K) and City-Stories (CHF 392K). Recent initiatives include SUDAC-2 (CHF 575K) expanding Switzerland's academic partnerships in the Global South. Labs/Teams: Active in HES-SO's interdisciplinary teams focusing on digital governance, inclusive technology, and smart manufacturing. Co-leads the Knowledge Discovery and Parapheur Electronique research groups.

Chen Yingمشاهده پروفایل

دانشیار

• Forecasting
• Non-stationary Time Series Analysis
• High Frequency Data
• +۱۵ مورد دیگر

Dr. Chen Ying is an Associate Professor in the Department of Mathematics at the National University of Singapore (NUS), with joint appointments in the Risk Management Institute, Department of Statistics and Applied Probability, and courtesy appointments in the Department of Economics. She serves as Academic Director of the Digital FinTech PhD program at the Asian Institute of Digital Finance and is a Faculty member in NUS Graduate School for Integrative Sciences and Engineering. Her research spans financial statistics, data science, and applied mathematics, focusing on non-stationary time series, high-frequency data, functional data analysis, and quantum computing applications in finance. Education: B.Sc. Economics (Renmin University of China, 1998), M.A. Economics and Management Science (Humboldt University, 2002), M.Sc. Statistics (Humboldt & Freie Universität Berlin, 2005), and Ph.D. in Statistics (Humboldt University, 2007, Summa Cum Laude). Research interests include AI forecasting, quantum computing for finance, non-stationary time series analysis, energy data analytics, and citation metrics. She has published extensively on topics like Bitcoin blockchain dynamics, natural gas demand modeling, and credit rating fairness. Dr. Chen serves on editorial boards of Statistica Sinica , Statistics and Its Interface , and Digital Finance , and holds leadership roles in the International Statistical Institute and International Association for Statistical Computing. Notable achievements include ISI Elected Membership (2016), Council Member of the International Statistical Institute (2023–2027), and roles in global initiatives like the EU FIN-TECH project. Her work bridges statistical theory with practical applications in finance, energy, and healthcare.

Eric Horsterمشاهده پروفایل

استاد

• Digital Tourism Marketing
• Customer Experience Management
• Smart Destinations
• +۳ مورد دیگر

Prof. Dr. Eric Horster is a Professor in the International Tourism Management (ITM) program at the West Coast University of Applied Sciences in Heide, Germany. He holds a prominent role as a board member of the German Institute for Tourism Research, contributing to cutting-edge research in digital tourism marketing, customer experience management, and smart destinations. His work integrates technology-driven solutions with strategic tourism challenges, focusing on visitor management systems, innovation methodologies, and sustainable destination development. Research Interests: Horster’s expertise spans digital tourism marketing, customer experience design, and the application of innovative technologies in tourism. His recent work explores visitor spatio-temporal behavior analysis, intervention frameworks for overcrowding, and the integration of recommendation systems in destination management. He emphasizes leveraging data-driven strategies to enhance tourism experiences while addressing industry challenges like resource management and sustainability. Articles & Trends: His publications highlight trends in smart destinations, digital tools for customer experience optimization, and innovation frameworks for tourism businesses. Notable works include frameworks for overcrowding mitigation (INPReS) and methodological guides for innovation in customer experience management. Awards & Grants: No specific awards or grants are listed, though his academic contributions are recognized through his active research and institutional roles. Advising & Labs: While no formal students are listed, Horster guides research initiatives at the German Institute for Tourism Research and collaborates with institutes like regioMAR and WinHR. His work often involves cross-disciplinary teams addressing tourism’s digital transformation and policy challenges.

• Big Data Systems
• Distributed Systems
• Networking Systems
• +۳ مورد دیگر

Dr. Damiano Carra serves as an Associate Professor in the Department of Computer Science at the University of Verona, with a future promotion to Full Professor scheduled for October 1, 2025. His academic appointment falls under the IINF-05/A Information Processing Systems sector, with research aligned with ERC classifications PE6_2 (Distributed systems, parallel computing, sensor networks) and PE7_8 (Communication networks, IoT). His research interests span two primary domains: Data Intensive Scalable Computing - focusing on analysis, evaluation and design of large-scale parallel systems with emphasis on Big Data processing and Cloud Computing Networking Systems - specializing in protocol design, performance evaluation, network measurement, and overlay networks Dr. Carra maintains active research groups including the Big Data Analytics team working with spatio-temporal datasets and the INdAM Research Unit at the University of Verona. His work has been published in prestigious journals including IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Computers, and IEEE Transactions on Cloud Computing, as well as presented at major conferences such as IEEE Infocom, IEEE Cloud, and IEEE BigData. He currently leads multiple research projects including: Securing Decentralized Finance and Remote Healthcare Systems - SHIELD (starting 10/22/24) Novel Methodologies and Tools for Next Generation Cyber Ranges - NOMEN (starting 5/21/24) PRIN PNRR 2022 - Joint Communication and Sensing: CSI-Based Sensing for Future Wireless Networks (starting 11/30/23) Administratively, Dr. Carra serves as Chair of Esaminatori ECDL, Referent for Third Mission in Computer Science, and holds membership on multiple university committees including the Faculty Board of PhD in Computer Science, Computer Science Teaching Committee, and Mathematics and Data Science Teaching Committee. He also serves as Responsabile del Test Center for the ECDL Test Centre.

Brian Hugh Neelonمشاهده پروفایل

استاد

• Bayesian Methods
• Longitudinal Data Analysis
• Spatial Data Analysis
• +۳ مورد دیگر

Brian Hugh Neelon is a Professor and Graduate Training Director for Biostatistics in the Department of Public Health Sciences at the Medical University of South Carolina (MUSC). He holds joint appointments at the Ralph H. Johnson VA Medical Center and the Biostatistics Shared Resource at Hollings Cancer Center. His research focuses on developing Bayesian hierarchical models for longitudinal and spatial data, with applications spanning infectious/chronic diseases, maternal/child health, environmental health, and health services research. Research Interests: Bayesian inference, longitudinal/spatial data analysis, zero-inflated models, latent growth modeling, and health disparities. His work integrates statistical innovation with real-world public health challenges, such as COVID-19 surveillance and social vulnerability assessments. Publications: Recent articles emphasize Bayesian methods in spatiotemporal modeling of pandemics (e.g., COVID-19), zero-inflated data frameworks, and health equity. Key themes include geospatial analysis of disease spread, policy impacts on public health, and computational advances in biostatistics. Awards: Recipient of the AJPM 2021 Paper of the Year Award for research on political affiliations and COVID-19 outcomes. Education & Advising: As Graduate Training Director, he oversees biostatistics students and collaborates with interdisciplinary teams. No specific grant or lab details are provided in the source text.

Maryam Rahnemoonfarمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Remote Sensing
• +۵ مورد دیگر

Dr. Maryam Rahnemoonfar is a Tenured Associate Professor of Computer Science and Engineering at Lehigh University’s P.C. Rossin College of Engineering and Applied Science, with a joint appointment in Civil and Environmental Engineering. She directs the Computer Vision and Remote Sensing Laboratory (Bina Lab) and leads interdisciplinary research in AI, sustainability, and environmental science. Previously, she held tenured positions at UMBC and Texas A&M University-Corpus Christi. Education: Ph.D. in Computer Science (University of Salford, UK, 2010), M.Sc. in Remote Sensing Engineering (University of Tehran, 2005), B.Sc. in Civil Engineering (University of Isfahan, 2002). Research focuses on AI-driven solutions for climate resilience, disaster management, and polar ice dynamics. Key areas include physics-informed machine learning, remote sensing algorithms for radar/sonar data, and UAV-based damage assessment. Her work has been funded by NSF, Amazon, Microsoft, and IBM grants. Scientific Awards: NSF HDR Institute Award, NSF BIGDATA Award, Amazon Academic Research Award Labs/Teams: Bina Lab (specializing in AI for environmental challenges) Grants: Over $1M from DOE Climate Resilience Center, NSF, and industry partnerships Recent projects include developing AI tools for ice sheet modeling, flood damage analysis using UAVs, and climate change monitoring via radar datasets. She actively collaborates with Antarctic research initiatives and advances AI for societal impact through workshops and interdisciplinary teams.

• Artificial Intelligence
• Mobility and Intelligent Transport
• Decision Support Systems
• +۳ مورد دیگر

EL KHALFI Zeineb is a Researcher-Lecturer at the CESI Bordeaux Campus, part of the Engineering and Numerical Tools research team. She holds a PhD from the University of Paul Sabatier (Toulouse) and the Higher Institute of Management of Tunis (2017), focusing on lexicographic refinements in possibilistic decision-making models. Her academic background includes a Master's in Data Mining and Knowledge Management (Polytech Nantes, 2014) and another in IT and Knowledge Management (Higher Institute of Management of Tunis, 2014). Education: PhD: University of Paul Sabatier and Higher Institute of Management of Tunis (2017) Master's: Polytech Nantes (Data Mining, 2014) Master's: Higher Institute of Management of Tunis (IT and Knowledge Management, 2014) Research Interests: Artificial Intelligence and Decision Support Systems Smart Mobility Solutions: Free-Floating and Shared Micro-Mobility Data Mining in Uncertain Environments Urban Transportation Optimization Her work bridges theoretical advancements in possibilistic decision models and practical applications in smart city technologies. Research Trends: Recent publications emphasize optimizing micro-mobility systems (e.g., dock-based rebalancing, spatio-temporal demand forecasting) and advancing sequential decision-making frameworks under uncertainty. Earlier work pioneered lexicographic refinements in possibilistic MDPs and decision trees, addressing complex decision scenarios in uncertain domains. Advising & Grants: No formal advisees listed, but collaborative projects focus on mobility innovation. Active participation in interdisciplinary research teams at CESI. Labs/Teams: Member of the Engineering and Numerical Tools research group at CESI, specializing in computational tools for urban and industrial challenges.

• Computer Vision
• Machine/Deep Learning
• Complex Networks
• +۱ مورد دیگر

MOURCHID Youssef is a Researcher-Lecturer at CESI (Campus Dijon), affiliated with the Engineering and Numerical Tools research team. He holds a PhD in Computer Science from Mohammed V University, Bourgogne University, and Osaka University (2014–2019), and completed a postdoctoral fellowship at Thales & IMS Lab, Bordeaux INP (2019–2020). His academic background includes a Master’s in Computer Science and Telecommunications from Mohammed V University (2012–2014). Roles: Researcher-Lecturer, Head of Building-Environment Chair, and reviewer for conferences like IEEE Image Processing and EGC. Teaching: Teaches computer science disciplines (Programming, AI, NLP) to engineering students and job applicants via courses, practical work, and active pedagogy. Research Interests: Focuses on Computer Vision, Machine/Deep Learning, Complex Networks, and Digital Healthcare . Key projects include applying graph neural networks for patient rehabilitation assessment, AI-driven impact fall detection, and satellite image colorization using GANs. His work bridges healthcare technology and multimedia analysis. Publications: Over 25+ peer-reviewed papers (6 journal articles, 13 conferences) spanning spatio-temporal graph networks, multilayer network models, and GAN applications. Recent work emphasizes healthcare applications like hypotension prediction and multimodal fall detection. Advising: Supervised master’s and PhD students on topics like GANs for image super-resolution, multilayer network analysis of movies, and AI for medical monitoring.

• Spatial Observation Data Analysis
• Sensor Integration
• Energy Consumption Analysis
• +۳ مورد دیگر

Sebastián Villarroya Fernández is an Assistant Professor at the University of Santiago de Compostela , affiliated with the Department of Electronics and Computing within the Higher Technical School of Engineering . He completed his PhD in 2018 with a thesis titled "Acquisition and declarative analytical processing of spatio-temporal observation data" , supervised by Dr. José Ramón Ríos Viqueira and Dr. José Manuel Cotos Yáñez. His research focuses on spatial observation data analysis, sensor data integration, and energy efficiency in maritime systems. Key projects include the development of the SODA framework for geospatial analytics and the design of a portable data acquisition system for optimizing energy consumption in fishing vessels. He collaborates with researchers in environmental, medical, and building monitoring domains, integrating protocols like HL7 and BACnet for heterogeneous sensor networks. His work addresses challenges in managing large-scale observation data, bridging gaps between entity-based and sampling-based data models. No formal awards are listed, but his contributions reflect innovative solutions in data management and energy efficiency.