جست‌وجوی استاد و پژوهشگر

• Generative AI
• Geographic Information Systems
• Natural Language Processing
• +۳ مورد دیگر

Diogo Cosme is a Researcher and Research Assistant at ISTAR-Iscte (Research Center in Information Sciences, Technologies and Architecture) within the School of Technology and Architecture at ISCTE - University Institute of Lisbon. He is currently pursuing doctoral studies in Information Sciences and Technologies while actively contributing to research projects and teaching. His academic qualifications include: PhD in Information Sciences and Technologies (2024-2028) at ISCTE Master's in Computer Engineering (2022-2024) at ISCTE Master's in Intelligent Systems and Applications (2023-2024) at Université Gustave Eiffel Bachelor's in Computer Engineering (2019-2022) at ISCTE Diogo's research centers on applying Generative AI to Geographic Information Systems (GIS), with emphasis on developing natural language interfaces that enable non-specialists to interact with geographic data through conversational AI. His work bridges artificial intelligence, spatial analysis, and human-computer interaction, focusing on making complex GIS tools accessible through intuitive language-based interfaces. This research has direct applications in smart tourism, urban planning, and location-based services. His recent publications demonstrate a clear trajectory in AI-driven information systems, particularly in LLM-based classification systems and smart tourism observatories. The 2024 conference paper examines content classification challenges in information retrieval, while the 2023 report establishes foundational work for the European Smart Tourism Tools Observatory, both reflecting his expertise in applying AI to domain-specific information systems. No scientific awards were documented in the provided materials. Diogo contributes to the EUROSTIT project (continuing his master's work from the RESETTING project) and teaches Algorithm Design and Analysis for Computer Engineering students (2025/2026 academic year). He actively pursues professional development through initiatives like the Lisbon Machine Learning School (LxMLS 25), demonstrating commitment to advancing his technical capabilities in machine learning applications. He operates within ISTAR-Iscte's Software Systems Engineering group, collaborating on interdisciplinary projects that integrate AI methodologies with real-world spatial data challenges, particularly in tourism technology and geographic information processing.

Laetitia Chapelمشاهده پروفایل

استاد

• Machine Learning
• Remote Sensing
• Time Series Analysis
• +۳ مورد دیگر

Laetitia Chapel is a full professor in computer science at Institut Agro Rennes-Angers , conducting research within the OBELIX team at IRISA , a mixed research unit in computer science, signal/image processing, and robotics. Her work focuses on machine learning for structured data (time series, graphs, hierarchical representations) with applications in remote sensing, and she obtained her HDR in 2022. Research Interests center on advancing optimal transport theory for structured datasets, including time series alignment, hyperbolic embeddings, and Riemannian neural networks. She actively participates in international workshops and conferences, delivering invited talks at venues like NeurIPS and IEEE MLSP. Ambroise Odonnat (2024, Huawei-funded PhD) Léo Buecher (2024, supervised with Nicolas Courty) Sergio Osson (2025, supervised with Charlotte Pelletier) Guillaume Mahey (2024, with Gilles Gasso) Manal Hamzaoui (2023, with Sébastien Lefèvre and Minh-Tan Pham) Titouan Vayer (2020, now at INRIA) Pierre Gloaguen (2017, now assistant professor at UBS) Scientific Awards include the Best paper award at MajecSTIC 2005 and Best paper award at ICPRAI 2022 . Her research projects span from theoretical foundations (e.g., hyperbolic metric learning) to applied remote sensing tasks (e.g., cropland mapping in the Brazilian Amazon).

Tao Chenمشاهده پروفایل

استاد

• Image Processing
• Data Mining and Deep Learning
• Geo-hazards
• +۲ مورد دیگر

Prof. Tao Chen is a faculty member at the School of Geophysics and Geomatics , China University of Geosciences , specializing in advanced remote sensing and data analysis techniques. His research bridges Image Processing , Machine Learning , and Geo-environmental Monitoring . Focus on Geo-hazards (e.g., flood mapping, landslide susceptibility) Develops hybrid methods for texture enhancement and spatiotemporal change detection Active in IEEE GRSS professional networks Recent publications highlight multi-source data fusion (e.g., SAR + social media) and model interpretability in geospatial risk assessment. Collaborates with experts like Jun Li and Antonio Plaza .

Pedram Ghamisiمشاهده پروفایل

استاد مهمان

• Artificial Intelligence
• Remote Sensing
• Deep Learning
• +۷ مورد دیگر

Prof. Dr. Pedram Ghamisi is a leading academic in artificial intelligence and Earth observation, currently serving as the head of the Machine Learning Group at Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany, and holding a Visiting Professor position at Lancaster University , UK. His work bridges machine learning with remote sensing, focusing on satellite and aerial imagery analysis. Education: M.Sc. (hons.) in Remote Sensing, K. N. Toosi University of Technology (2012) Ph.D. in Electrical and Computer Engineering, University of Iceland (2015) His research explores advanced machine learning techniques to enhance remote sensing capabilities, including hyperspectral imaging, multisensor data fusion, and efficient labeling for large-scale image segmentation. Recent publications highlight applications of diffusion models, compact convolutional networks, and foundation models in geospatial analysis. Prof. Ghamisi leads the Machine Learning Group at HZDR, a hub for innovative approaches in resource ecology and Earth observation. He collaborates extensively with researchers like Bing Lu and Danfeng Hong, contributing to multidisciplinary projects in geospatial AI.

Stefan Petersمشاهده پروفایل

مدرس ارشد

• Remote Sensing
• Geographic Information Systems (GIS)
• Environmental Science
• +۵ مورد دیگر

Dr. Stefan Peters is a Senior Lecturer in Surveying and GIS at the University of South Australia (UniSA) within the UniSA STEM school. His research focuses on applied remote sensing and GIS to address environmental and geospatial challenges, including fire detection, habitat modeling, and 3D reconstructions. Lecturer in Surveying and GIS, UniSA STEM Research Degree Supervisor Stefan's research spans geospatial data fusion, environmental monitoring, and participatory mapping. He has contributed to frameworks for 3D point cloud fusion in forestry, AI-driven fire smoke detection, and urban catchment analysis using LiDAR and multispectral data. His recent publications highlight advancements in hyperspectral satellite AI emulation, channel morphology monitoring, and cross-sensor fire detection algorithms. Key themes include remote sensing innovation, environmental data integration, and urban-ecosystem interactions. Scientific contributions include: Best Paper Award (2012) Stefan coordinates courses in Web Cartography and Environmental Remote Sensing, emphasizing practical geospatial applications. He collaborates with institutions like Geoscience Australia and the FWPA on projects spanning forestry, fire management, and heritage preservation.

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Can Wang is a Senior Lecturer in Computer Science at Griffith University's School of Information and Communication Technology on the Gold Coast Campus, Australia. She has held this position since January 2020, having previously served as a Lecturer at the same institution from May 2017. Dr. Wang is a member of the Institute for Integrated and Intelligent Systems (2018-2024) and has extensive academic experience including a Postdoctoral Fellowship at Data61, CSIRO (2014-2016) and a Research Associate position at the University of Technology Sydney (2013). PhD in Computer Science from University of Technology Sydney, Australia (2009-2013) Master of Science from Wuhan University, China (2007-2009) Bachelor of Science from Wuhan University, China (2003-2007) Dr. Wang's research focuses on designing models and algorithms for heterogeneous and interdependent behavioral data to make predictions and recommendations. Her work aims to propose new frameworks and theories in data mining and machine learning while applying AI and big data analytics to multidisciplinary domains including social media, tourism, drug discovery, hospital admission, and medical mining. She has published over 80 high-quality publications with real applications and deployments. Her recent publications (2023-2025) demonstrate a strong focus on heterogeneous network analysis, recommendation systems (particularly point-of-interest recommendations), fake news detection, and the integration of Large Language Models with traditional recommendation approaches. Her work spans both theoretical advancements in machine learning algorithms and practical applications across healthcare, social media, and tourism domains, showing her commitment to bridging the gap between academic research and real-world problems. Dr. Wang actively supervises doctoral students, serving as Principal Supervisor for projects related to Large Language Models analysis, multi-media data summarization, and point-of-interest recommendation systems. She also serves as PROGRAM DIRECTOR for the Graduate Certificate in Data Science, Master of Data Science, and Master of Information Technology programs, and as PROGRAM ADVISOR for multiple undergraduate and graduate programs including various Bachelor of Science combinations and Cyber Security certificates. Her funded research includes significant projects such as the NSF-CSIRO collaboration on 'Towards Interpretable and Responsible Graph Modeling for Dynamic Systems' (2024-2027), multiple Griffith Sciences Advancing Women's Research Success Grants (2021-2024), and projects focused on patient readmission prediction, social media analysis during pandemics, and tourism market analysis in Queensland.

Kaicheng Yangمشاهده پروفایل

پژوهشگر

• Computational Social Science
• Social Bots
• Misinformation
• +۴ مورد دیگر

Kaicheng Yang is a postdoctoral researcher at Northeastern University , with an upcoming transition to Assistant Professor in the Department of Computer Science at Binghamton University in Fall 2025. His work bridges computational social science, AI ethics, and platform governance. Education: Ph.D. in Informatics from Luddy School of Informatics, Computing, and Engineering , Indiana University Bloomington His research investigates malicious information manipulation through social bots, algorithmic biases, and generative AI misuse, developing countermeasures for trustworthy online ecosystems. Recent publications explore domain-sharing patterns , news source credibility , and AI-generated social media fraud across journals like Scientific Data , PNAS , and Journal of Online Trust and Safety . Awarded Best Paper - Honorable Mention at the International AAAI Conference on Web and Social Media, Yang also contributes to open-source tools such as the LLM Domain Classification dashboard and Botometer X. His work has received media coverage from Wired , Forbes , and MIT Technology Review .

Dikran Meliksetianمشاهده پروفایل

استاد مدعو

• Software Engineering
• Cloud Computing
• Machine Learning
• +۵ مورد دیگر

Dr. Dikran Meliksetian serves as Adjunct Faculty in the Computer Engineering and Computer Science Department at the University of New Haven's Tagliatela College of Engineering. An accomplished Senior IT Professional with extensive US and international experience, he leverages industry expertise from his tenure as an IBM Senior Technical Staff Member to enrich academic instruction. His educational foundation includes a Ph.D. in Computer Engineering from Syracuse University. Dr. Meliksetian's research spans critical domains including software engineering, cloud computing, machine learning, and artificial intelligence, with significant contributions to geospatial processing, enterprise IT services, grid computing, and quantum technology. His work bridges theoretical frameworks with practical industry applications, particularly in developing scalable architectures and service delivery models. Current involvement in initiatives like the QuantumUp! Challenge demonstrates his commitment to emerging technologies. His publication history reveals an evolution from foundational grid computing research (2004-2006) toward contemporary work in geospatial processing and cloud service transformation (2016-2019), reflecting industry shifts toward distributed systems and AI-driven solutions. Notable recognitions include: IBM Master Inventor status Senior Membership in the ACM Life Membership in IEEE and IEEE Computer Society Portfolio of over 50 patents While specific student names aren't documented, Dr. Meliksetian actively mentors through projects like the QuantumUp! Challenge where students develop quantum technology skills. His extensive patent portfolio and industry-academia transition exemplify significant research impact beyond traditional grant funding structures. As part of the Computer Engineering and Computer Science Department, he contributes to the academic ecosystem by integrating real-world IBM experience with theoretical instruction, particularly in emerging fields like quantum computing where student teams compete in national challenges.

Panos Louvierisمشاهده پروفایل

استاد

• Cybersecurity
• Data and Information Fusion
• Defence Analytics
• +۷ مورد دیگر

Panos Louvieris serves as Professor of Information Systems at Brunel University London within the Department of Computer Science, College of Engineering, Design and Physical Sciences. He leads the Defence & Cyber Security (DCS) research group and holds co-directorship roles at the Brunel Intelligent Digital Economy and Society (IDEAS) Research Centre and the Trusted Open Models Institute (TOMI) at the Hartree Centre. His academic profile includes committee membership in the EPSRC Digital Personhood Network, EPSRC ITaaU+ Network, and NEMODE+ Network, reflecting his influence in UK research infrastructure. His research spans cybersecurity, data fusion, and computational finance with emphasis on cyber situational awareness, explainable AI, and distributed ledger technologies. Recent work integrates agent-based simulation for pandemic modeling, Markov models for threat prediction, and social media analysis for security awareness. This interdisciplinary approach bridges computer science, operations research, and social sciences to address defense, finance, and public health challenges through advanced computational models. Analysis of his publication trajectory reveals evolving focus from foundational information fusion (2005-2010) to cyber-physical systems (2010-2015), with current emphasis on digital personhood markets (2020-present). His most recent publications demonstrate methodological innovation in blockchain applications for tax compliance and geospatial pandemic modeling, while maintaining core expertise in cyber defense analytics. Louvieris actively supervises research and collaborates with institutions including the Hartree Centre and EPSRC networks. His grant portfolio features the EPSRC-funded Digital Personhood project (2013-2016), Innovate UK's Contact Tracing project (2020-2021), and current leadership of the EPSRC Making Tax Smart initiative (2022-2023) exploring distributed ledger technology for central bank digital currency compliance. He directs the Defence & Cyber Security research group and co-leads the IDEAS Research Centre, fostering cross-disciplinary work on digital economy challenges. Current projects involve HSBC sandbox testing of Open Banking prototypes and development of futures markets for digital personhood data, positioning his team at the intersection of cybersecurity, FinTech, and societal digital transformation.

• Artificial Intelligence
• Evolutionary Algorithms
• Neural Networks
• +۶ مورد دیگر

Patricia María Compañ Rosique is a full Professor in the Department of Computer Science and Artificial Intelligence at the University of Alicante , where she has served since 2008. She holds a PhD in Computer Science (University of Alicante, 2004) and a Computer Science Engineer degree (1994). Her career includes leadership roles such as Deputy Director of the Computer Science Degree Program (2009-2012) and Coordinating Deputy Director of the Higher Polytechnic School (2012-2013). Her research focuses on applying AI techniques across diverse domains: evolutionary algorithms for mathematical problems, neural networks in coastal engineering, and digital technologies in education. She leads the Research Group on Smart Learning Technologies and participates in the Santander-UA Chair of Digital Transformation. Recent publications highlight her work on: AI-driven concrete segregation analysis (2021) Evolutionary algorithms for matrix diagonalization (2019) Neural networks in coastal modeling (2018) Educational game development (2022) Impact of e-learning investments (2019) Scientific awards include: 3 triennial periods (sexenios) for research activity Member of AENUI (Spanish Association of University Computer Science Teachers) Contributions to educational innovation projects under EHEA She has directed 28 undergraduate/master's theses in the last 5 years and participated in 11 public research projects over the last 5 years, including grants from the Spanish Ministry of Economy and the Valencian Government.

Julie Bourbeillonمشاهده پروفایل

مدرس

• Bioinformatics
• Systems Biology
• Artificial Intelligence
• +۹ مورد دیگر

Julie Bourbeillon is a Lecturer in Computer Science at Institut Agro Rennes-Angers , where she co-directs the Department of Statistics and Computer Science. Her work bridges Bioinformatics , Computer Vision , and Data Integration with applications in Plant Biology and Horticulture . She has developed educational frameworks for integrating digital skills into horticultural training using the PiX framework . Education : PhD in Computer Science (2007, Université Grenoble 1), Master of Science and Engineering for Health and Medicine (2004), Engineer (2002, INAPG) Her research focuses on semantic distance , heterogeneous data integration , and data visualization for biological datasets. She leads the DIVIS project for handling complex phenotypic data and co-developed ELVIS and QuaDS software tools. Key projects include: DIGITOM (2024-2027): Digital twins for climate-resilient agriculture ERICA (2025-2026): Robotic climate mapping in greenhouses GRIOTE (2014-2018): Bioinformatics collaboration

Henrietta Tománمشاهده پروفایل

استادیار

• Medical image processing
• Fusion systems
• Quasigroups
• +۳ مورد دیگر

Dr. Henrietta Tomán serves as an Assistant Professor in the Department of Data Science and Visualization at the University of Debrecen's Faculty of Informatics, where she bridges advanced mathematical theory with practical medical imaging applications. Her work integrates abstract algebraic structures with cutting-edge AI systems to solve critical healthcare challenges. Her research portfolio spans three interconnected domains: Medical image processing (particularly ensemble-based segmentation and quality assessment for ophthalmic diagnostics) Geometric structures (quasigroups, loops, and differentiable manifolds) Stochastic optimization for resource-constrained AI systems Analysis of her publication trajectory reveals evolving expertise: early work (2010-2014) established foundations in geometric loop theory applied to image processing, while recent research (2020-2024) pioneers stochastic fusion techniques for medical image ensembles under computational constraints. Her most significant contributions involve translating mathematical abstractions into robust clinical decision-support tools, particularly in diabetic retinopathy detection and epidemic modeling. Current work demonstrates increasing focus on real-time AI systems that maintain accuracy under hardware limitations, reflecting urgent needs in telemedicine and mobile health applications. Dr. Tomán maintains active collaboration within the Doctoral School of Informatics and contributes to Hungary's national research initiatives in medical AI, with consistent publication output in top-tier venues spanning computer vision, medical imaging, and mathematical computing.

Angel Ruizمشاهده پروفایل

استاد

• Healthcare Operations
• Emergency Medical Services
• Mathematical Modeling and Optimization
• +۳ مورد دیگر

Angel Ruiz is a Full Professor at Laval University’s Faculty of Medicine, specializing in healthcare operations and optimization. He co-directs CIRRELT’s Laboratory on Health Service Networks and contributes to CERVO’s Clinical and Cognitive Neuroscience research axis. Laval University, Faculty of Medicine CIRRELT’s Laboratory on Health Service Networks (Co-Director) CERVO Brain Research Centre (Researcher) His research focuses on mathematical modeling, logistics optimization, and simulation frameworks to improve healthcare systems, including pandemic planning, emergency medical services, and biomedical sample transportation. He has developed systematic review tools and prioritization frameworks for rehabilitation services. Recent publications emphasize location-allocation models for vaccination facilities, stochastic programming for food bank networks, and real-time patient transport systems. While no awards or student lists are explicitly mentioned in accessible content, his work bridges healthcare management with operational research.

Feilin Laiمشاهده پروفایل

استادیار

• Geospatial Science
• Urban Remote Sensing
• Environmental Inequality
• +۲ مورد دیگر

Feilin Lai is an Assistant Professor at Lamar University , specializing in geospatial data science and urban remote sensing. Their research focuses on environmental inequality, AI-driven urban landscape analysis, and UAS applications for environmental monitoring. Ph.D. in Geography - Geospatial Data Science, Florida State University M.S. in Geographic Information System, Florida State University B.S. in Remote Sensing Science and Technology, Wuhan University Research spans machine learning for land use classification , high-resolution imagery analysis , and UAS-based environmental data collection , with notable contributions to transfer learning frameworks and urban landscape mapping. Recent publications emphasize cross-regional model adaptability , deep learning architectures , and technical challenges in drone applications for urban environments.

• Hydraulic Engineering
• Flood Risk Management
• Hydrology
• +۳ مورد دیگر

Susanna Dazzi is a Fixed-term Researcher at the Department of Engineering and Architecture, University of Parma, Italy. Her work focuses on hydraulic engineering, flood risk management, and computational modeling, leveraging advanced techniques like physics-informed neural networks and GPU acceleration for real-time flood prediction. Key Research Areas: Flood hazard estimation, levee breach dynamics, dam-break scenarios, 2D hydraulic modeling, and sediment transport analysis. Teaching: Delivers courses on Hydraulic Infrastructures and Hydraulic Protection of the Territory for Master's Degree programs in Civil Engineering. Publications: Develops data-driven models (e.g., FloodSformer) for real-time flood forecasting and investigates hydrodynamic interactions with infrastructure. Contact: Email: susanna.dazzi@unipr.it ; Office: Science Area Park, 181/A, 43124 Parma, Italy.