جستجوی استاد راهنمای دانشگاهی

Yannick Benezethمشاهده پروفایل

استاد

• video health monitoring
• video analytics
• remote photoplethysmography
• +۵ مورد دیگر

Yannick Benezeth is a Professor of Computer Science at Université de Bourgogne Franche-Comté in Dijon, France, where he teaches courses on databases, optimization, and image/video processing at the IUT de Dijon. He conducts research at the ImViA research laboratory (EA7535), focusing on video health monitoring and video analytics applications. His academic journey includes serving as an Associate Professor from 2011-2024 and earning his Habilitation à Diriger des Recherches (HDR) in 2019. Dr. Benezeth's research interests center on video-based health monitoring systems, particularly remote photoplethysmography (rPPG) for non-contact vital sign measurement. His work spans computer vision , video analytics , and physiological signal processing , with applications in stress detection, abnormal event recognition, and health monitoring. He has developed several publicly available datasets including UBFC-Phys, UBFC-RPPG, and IMVIA-NIR that have become valuable resources for researchers in affective computing and remote physiological monitoring. His publication record demonstrates consistent contributions to top computer vision venues including CVPR, ICPR, and IEEE Transactions. Recent work shows a clear trend toward multimodal approaches combining video analysis with physiological signal processing, particularly in psychophysiological stress studies. The UBFC-Phys dataset published in 2021 represents a significant contribution to affective computing research with over 50 participants and comprehensive physiological measurements. As a research supervisor with HDR qualification, Dr. Benezeth leads projects in the ImViA laboratory focusing on video analytics for healthcare applications. His team has developed innovative methods for background subtraction, abnormal event detection, and skin tissue segmentation that have been adopted by other researchers through his publicly shared code and datasets. Current work appears focused on improving the robustness of video-based physiological measurement under realistic conditions.

Vassilis Christophidesمشاهده پروفایل

استاد

• Databases
• Web Information Systems
• Big Data Processing
• +۹ مورد دیگر

Vassilis Christophides is a Professor of Computer Science at the University of Crete and holds an advanced research position at Inria Paris, where he leads work in the MiMove team. His research spans databases, web information systems, big data processing, and IoT analytics, with a strong emphasis on entity resolution, data integration, and scalable data mining. He has supervised numerous research projects funded by the European Union and the Greek State, and has published over 130 articles in top-tier conferences and journals. Research Interests: His primary research areas include Databases, Web Information Systems, Big Data Processing and Analytics, and Information Systems for the Internet of Things. He also focuses on entity resolution, knowledge graphs, streaming data, and explainable AI, particularly in the context of anomaly detection and fairness-aware data systems. His recent work explores hybrid attention models for entity alignment and causal analysis in time series classification. Recent Research Trends: Analysis of his recent publications (2021–2025) reveals a strong focus on entity resolution with fairness constraints, explainable anomaly detection, and adaptive scheduling in IoT edge analytics. He also investigates deepfake detection, crop type mapping using satellite data, and structural bias in knowledge graphs, demonstrating a broad and impactful research portfolio at the intersection of data management and machine learning. Scientific Awards: 2004 SIGMOD Test of Time Award Best Paper Award, ISWC 2003 Best Paper Award, ISWC 2007 Advising and Grants: While specific student names are not listed in the provided texts, Christophides has co-authored numerous papers with researchers such as Vasilis Efthymiou, Ioannis Tsamardinos, and Nikolaos Myrtakis, suggesting active mentorship. He has been the scientific coordinator of multiple EU and national research projects, indicating substantial grant leadership and project management experience. Labs and Teams: He is affiliated with the MiMove team at Inria Paris, a research group focused on mobility and data-intensive systems. His work bridges academic and applied research, leveraging Inria’s infrastructure for large-scale data experimentation and innovation in IoT and edge computing environments.

Nicolas Lachicheمشاهده پروفایل

استاد

• Data Science
• Relational Data Mining
• Sequential/Temporal Data Mining
• +۴ مورد دیگر

Nicolas Lachiche is a Professor in Computer Science at the University of Strasbourg, where he serves as Deputy Head of the SDC (Data Science and Knowledge) team within the Engineering, Computer and Imaging Sciences Laboratory (ICube - UMR 7357). His research focuses on machine learning and data/knowledge management across various application domains. Professor Lachiche's primary research interests include data science, relational data mining, sequential and temporal data mining, anomaly detection, and NoSQL databases. His work spans healthcare applications (ICU with Vincent Castelain), high-performance computing (with Florina Ciorba), materials science (with Yves-André Chapuis), and smart factories (with Technology & Strategy). His research trajectory shows evolution from early foundational work in scope classification (94-97) and the PRIMUS family of systems (97-04) to current cutting-edge projects addressing modern data challenges across multiple sectors. Professor Lachiche maintains an extensive service record in the scientific community, having organized numerous workshops and conferences including the MODA series (2020-2024), PFIA 2023 (as Local chair), and various international workshops on data mining and machine learning. He has served on program committees for prestigious conferences such as AAAI (2018, 2020-2024), ECAI, ICDM, and SDM. Additionally, he has reviewed for top journals including Artificial Intelligence Journal, Machine Learning Journal, and IEEE Transactions. His teaching portfolio includes data science, data mining, and machine learning; big data technologies, business intelligence and data warehouses; advanced databases (OLAP, NoSQL, GIS); and foundational courses on databases, data persistence, web programming, and user interfaces.

Michel Deshaiesمشاهده پروفایل

استاد

• Resource Exploitation
• Environmental Impact
• Landscape Changes
• +۴ مورد دیگر

Michel Deshaies is a Professor at the University of Lorraine, serving as director of CERPA since 2006 and LOTERR since 2012. His academic career began with an aggregation in geography in 1989 and doctorate in 1994 from the University of Nancy 2, where he was a lecturer until 2006 before attaining full professorship. His educational trajectory includes higher education at the University of Le Mans, aggregation of geography (1989), doctoral research on limestone plateau meanders in Eastern France (1994), and HDR accreditation on mining landscapes (2005). These qualifications established his expertise in physical and human geography. Deshaies' research centers on resource exploitation impacts, environmental transformations, and rapid landscape changes with regional specialization in Germany and Central Europe. His work integrates geographical analysis of energy transitions, mining landscape metamorphosis, and socio-ecological conflicts, emphasizing spatial dimensions of human-environment interactions. Key contributions examine territorial anchoring of energy projects and post-mining landscape rehabilitation. Analysis of his 2016-2020 publications reveals consistent focus on energy transition dynamics, mining landscape transformations in Germany, and socio-spatial dimensions of resource conflicts. The corpus demonstrates interdisciplinary methodology combining field observations, spatial analysis, and qualitative approaches to address territorial challenges in European contexts. He currently supervises three doctoral students: Jimmy GRIMAULT (energy transition conflicts), Manuel ISNARDON (radioactive waste storage projects), and Arsène KOUADIO (rural electrification in Côte d'Ivoire). His research funding includes ANR COLLENER (2012-2016) on German renewable energy, the Land Transport Infrastructure Twinning project (2012-2015), and the Gazhouille project on coal gas exploitation. As LOTERR director, he leads research across three thematic axes: Water and Environments, Landscape and Territorial Transitions, and Transitions, Actors and Planning. The laboratory operates advanced technical platforms for hydrometry, topography, sedimentology, and drone-based remote sensing, including specialized tools like IKARE for karst phenomena inventory and a 30,000-image aerial photograph database.

• 20th Century French Music
• Harmonic Analysis
• Music and Cinema Studies
• +۳ مورد دیگر

Philippe Cathé is a Professor at Sorbonne University, specializing in 20th-century French music (1870-1950), harmonic analysis via Nicolas Meeùs' theory of harmonic vectors, and the interplay of sound and image in cinema. He teaches seminars on harmonic vectors and courses on tonality, analytical methods, and music-cinema relationships at both Sorbonne and New York University. His research integrates theoretical innovation with extensive repertoire analysis, spanning from Renaissance to pop music. His publications focus on Charles Koechlin and Claude Terrasse, with notable works including co-edited volumes like Charles Koechlin, Composer and Humanist (Vrin, 2010) and monographs such as 500 Years of Harmonic Music (HDR thesis, 2012). He co-developed the Charles software for harmonic vector analysis. Scientific Awards: Manuscript Prize of the General Council of the Rhône (2003) Conference Organization: Co-organized multiple Petites Biennales de Musique Française and international symposia on musicology, including topics like harmonic vectors and film music. Radio Work: Regular contributor to France-Musique programs on French operetta and composers like Terrasse and Koechlin.

• History of Photography
• Colonial History
• British History (19th-20th Century)
• +۳ مورد دیگر

Daniel Foliard is a Researcher at the Université de Paris within the Department of Arts and Visual Culture - History and Civilization . His work focuses on the history of photography, British colonial history (19th-20th centuries), and the intersection of colonialism with knowledge production and visual culture. Research interests include the politics of colonial archives, the role of photography in imperial projects, and the ethical dimensions of representing historical trauma. He has contributed to debates on digital humanities methods for analyzing colonial visual materials, particularly through computational approaches to image databases like the EyCon Dataset. Recent publications emphasize contested colonial narratives, the materiality of photographic archives, and the interplay between violence and visual representation. Key themes include the violence of colonial photography , the memory of conflict , and the reconstruction of silenced histories . Foliard collaborates with institutions like the LARCA (Laboratoire d'Analyse et de Récits des Cultures et des Arts) and maintains an active presence in academic publishing, including special issue curation and critical reviews of historical scholarship on imperialism and visual culture.

Hanan Sametمشاهده پروفایل

استاد

• Spatial Data Structures
• Geographic Information Systems (GIS)
• Computer Vision
• +۳ مورد دیگر

Hanan Samet is a Distinguished University Professor in the Computer Science Department at the University of Maryland, College Park. He holds affiliations with the Center for Automation Research and the Institute for Advanced Computer Studies (UMIACS). His academic journey includes a PhD from Stanford University (1975) in Computer Science, following degrees in Engineering (UCLA) and Operations Research/Computer Science (Stanford). Affiliations: University of Maryland, College Park (since 1975) Roles: Professor, Founding Editor-in-Chief of ACM Transactions on Spatial Algorithms and Systems, Founder of ACM SIGSPATIAL Samet's research focuses on spatial data structures, spatial databases, GIS, computer vision, and information retrieval. His seminal work includes the Foundations of Multidimensional and Metric Data Structures , an award-winning book addressing spatial indexing and query optimization. He pioneered frameworks like NewsStand for map-based news exploration and Coronaviz for pandemic visualization. Key contributions span spatial synonyms for approximate search, SAND spatial browser for digital government, and trajectory analysis systems for aviation safety and urban mobility. His work bridges theory and practice, influencing databases, graphics, and geographic systems. Education: B.S. Engineering, UCLA M.S. Operations Research, Stanford M.S./Ph.D. Computer Science, Stanford Samet has advised numerous students and led NSF-funded projects on spatio-textual data, similarity search, and spreadsheet analysis. His honors include the ACM Paris Kanellakis Award (2011), IEEE Wallace McDowell Award (2014), and UCGIS Research Award (2009). His labs and teams focus on spatial algorithms, visualization, and GIS applications. Notable projects include VASCO (spatial index demo), MARCO (image databases), and CHOLERA (disease tracking).

Mathieu Brédifمشاهده پروفایل

پژوهشگر

• Lidar Processing
• 3D Reconstruction
• Geovisualization
• +۵ مورد دیگر

Mathieu Brédif is a Permanent Researcher at LASTIG, Gustave Eiffel University, affiliated with the National School of Geographic Sciences (ENSG) and IGN. He serves as co-chair of ISPRS Working Group II/3 on Point Cloud Processing (2016-2020) and chaired ISPRS Working Group III/5 on Graphics and Remote Sensing (2012-2016). His academic appointments include Assistant Professor at École Polytechnique teaching Image Analysis and Computer Vision (INF573) and 3D Computer Graphics (INF443) since 2019-2020. Telecom ParisTech PhD (2005-2010) Stanford University Master in Computer Science (2004) École Polytechnique Engineering Degree (2000-2005) Brédif's research focuses on Lidar processing, 3D reconstruction, and geovisualization , with significant contributions to point cloud analysis, urban scene modeling, and historical image integration. His work bridges computer vision, photogrammetry, and geographic information systems, emphasizing practical applications in urban planning and cultural heritage. He has developed novel algorithms for point cloud inpainting, visibility estimation, and distributed 3D reconstruction. His publications reveal consistent focus on urban modeling through point cloud processing (58% of works), image-based rendering techniques (22%), and geovisualization systems (15%). The research trajectory shows increasing emphasis on deep learning applications for LiDAR data since 2016, alongside continued development of geometric algorithms for photogrammetric processing. ANR project leadership in geospatial data valorization (structurAtion et vaLorisation du patrimoinE géoGraphique - 9) iSpace&Time 4D web GIS development (5) European project participation in high-volume point cloud analysis (8) Brédif actively mentors doctoral candidates, currently supervising Melvin Hersent, Alexane Nghien, and Florent Geniet, with 8 completed PhDs including Pierre Biasutti and Murat Yirci. His laboratory work centers on the GEOVIS research team , developing the iTowns open-source framework for 3D geospatial visualization, which powers the Géoportail's 3D data engine and supports multiple ANR projects in cultural heritage visualization.

Romain Raveauxمشاهده پروفایل

دانشیار

• Image Analysis
• Machine Learning
• Structural Pattern Recognition
• +۵ مورد دیگر

Romain Raveaux is an Associate Professor at the LIFAT Computer Science Laboratory, University of Tours, affiliated with Polytech Tours. His research focuses on Image Analysis, Machine Learning, Structural Pattern Recognition, Graph Matching, Graph Neural Networks, Discrete Optimization, Reinforcement Learning, and Transfer Learning . Email: romain.raveaux@gmail.com , romain.raveaux@laposte.net Address: 64 av. Jean Portalis, Tours, France, 37200 Phone: +33 (0)2 47 36 14 27 Research Interests Graph Matching and Neural Networks Discrete Optimization for Pattern Recognition Transfer Learning in Graph-Based Models Historical Document Analysis Scientific Trends His recent work bridges Graph Neural Networks with Mixed-Integer Programming , focusing on Image Semantic Segmentation and Graph Cycle Detection . Earlier studies emphasize Genetic Algorithms for graph classification and Graph Edit Distance optimization in pattern recognition.

• Deep Learning
• Machine Learning
• Computer Vision
• +۹ مورد دیگر

Ahed Alboody is a Professor and Researcher at HESAM University Group, specifically affiliated with CESI and the Digital Innovation Laboratory for Businesses and Learning to Support Territorial Competitiveness (LINEACT) in Nice, France. He holds a specialized doctorate in computer science from the University of Toulouse 3 Paul Sabatier and has extensive experience in deep learning, computer vision, and remote sensing applications. His work bridges academic research with practical applications in environmental monitoring, human-computer interaction, and spatial reasoning systems. Education: Specialized Doctorate in Computer Science, University of Toulouse 3 Paul Sabatier (IRIT), 2011 Master 2 Research in Electronics, Automation and Systems Engineering, National Polytechnic Institute of Toulouse (INPT-ENSEEIHT), National School of Civil Aviation (ENAC), ISAE-SUPAERO, and University of Toulouse III, 2006 Engineering Diploma in Electronics and Telecommunications, University of Tishreen (Techrine), Lattakia, Syria, 2002-2003 Undergraduate studies in Electronics and Telecommunications, University of Tishreen (Techrine), Lattakia, Syria, 2002 Alboody's research focuses on advanced applications of deep learning and computer vision, particularly in the areas of 3D hand gesture recognition, hyperspectral and multispectral image processing, and semantic segmentation. His work combines theoretical advancements in mixture-of-experts architectures with practical applications in remote sensing and environmental monitoring. He has pioneered approaches in frugal learning and zero-shot learning for image segmentation tasks, with applications in digital twins and collaborative robot environments. His publication record demonstrates a clear evolution from foundational work in spatial reasoning systems (2008-2012) to current cutting-edge research in deep learning architectures for 3D gesture recognition and hyperspectral image analysis. Recent publications (2022-2024) show a strong focus on mixture-of-experts transformers, parallel architectures for efficient computation, and applications in environmental monitoring with drones and satellite imagery. Alboody actively supervises Master's level research projects (two M2 level projects mentioned) and serves as a reviewer for prestigious journals including IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Geoscience and Remote Sensing. He has also been a member of the Technical Program Committee for international conferences on databases and knowledge applications. His laboratory work centers around the Digital Innovation Laboratory for Businesses and Learning to Support Territorial Competitiveness (LINEACT), where he leads research in engineering and digital tools. Current projects include developing graph neural networks for 3D hand gesture recognition using depth and skeleton data, and implementing frugal learning approaches for semantic image segmentation in collaborative robot environments.

Thomas Bourgeronمشاهده پروفایل

استاد

• Genetics
• Genomics
• Autism Spectrum Disorders
• +۱۲ مورد دیگر

Thomas Bourgeron is a Professor and head of the Human Genetics and Cognitive Functions Unit at the Institut Pasteur in Paris, France. His research integrates psychiatry, neuroscience, and genetics to investigate the causes of autism spectrum disorders (ASD) and improve clinical management. He leads a multidisciplinary team and numerous international research initiatives. Institut Pasteur, Paris Human Genetics and Cognitive Functions Unit Principal Investigator, EU-AIMS, PARIS Study, Phelan-McDermid Syndrome Project His research focuses on identifying genetic mutations linked to ASD, particularly in synaptic genes (NLGN, SHANK, CNTN) and the melatonin pathway affecting sleep. His team employs high-throughput genomics, brain imaging (MRI, EEG), mouse models, and human iPSCs to study functional impacts and develop therapeutic strategies. He emphasizes data sharing and open science through tools like GeneTrek and GRAVITY. His recent publications reveal genetic heterogeneity in autism, brain connectivity patterns, and phenotypic stratification using electrophysiology and behavioral analysis. His work bridges molecular mechanisms with clinical manifestations, aiming to identify common pathways for targeted interventions. Phenotypic effects of genetic variants in autism Genetic correlates of phenotypic heterogeneity Stratification of autistic phenotypes using EEG Functional impact of SHANK3 mutations Proteomic analysis of the pineal gland in autism Thomas Bourgeron mentors numerous PhD students, postdoctoral researchers, and engineers. He collaborates with leading institutions and clinicians, including Professor Christopher Gillberg at the University of Gothenburg. His lab is actively recruiting in human genetics and neuroimaging. He contributes to public education through lectures, open-access tools, and outreach initiatives like the CléPsy platform and educational videos for families of autistic children.

Florent Ginhouxمشاهده پروفایل

پژوهشگر

• Myeloid Cell Biology
• Cancer Immunology
• Tumor Microenvironment
• +۴ مورد دیگر

Florent Ginhoux is a Researcher at Gustave Roussy 's Tumor Immunology and Cancer Immunotherapy Program (U1015). He leads the Florent Ginhoux Laboratory (ginhouxlab.com), focusing on myeloid cell biology in cancer progression and immunotherapy development. His team integrates spatial transcriptomics (Visium HD, Xenium), single-cell RNA sequencing , and MACSIMA spatial proteomics to dissect tumor-immune interactions. His research spans pediatric oncology (brain tumors, neuroblastomas, sarcomas) and adult cancers (pancreatic, head/neck carcinomas). Key projects include Developing iPSC-derived macrophages for patient-specific tumor modeling Characterizing TREM2+ multinucleated macrophages as prognostic indicators Creating MoMac-VERSE and DC-VERSE compendiums for cross-tissue myeloid cell analysis His lab collaborates with pediatric oncologists and bioinformaticians on projects funded by 2024 Fight-Kid Cancer grant ( MiMiC-Kids ) 2024 ANR Excellence Chair ( MATCH ) 2024 Horizon HIT-GLIO grant 2021 INCa PEDIAHRG High-Risk/High-Gain program

Claire Delusمشاهده پروفایل

مدرس

• Continental Hydrology
• Water Cycle Dynamics
• Watershed Hydrology
• +۷ مورد دیگر

Claire Delus is a Lecturer in Geography at the University of Lorraine, specializing in continental hydrology at the intersection of engineering and geographical approaches. Her work emphasizes statistical and modeling methods, supported by naturalist observations of flow processes in watersheds influenced by physiographic contexts. Education: Doctorate in Geography (2007), Paul-Verlaine University of Metz, under Jeannine Corbonnois and Emmanuel Gille Research Activities: Water cycle dynamics Hydrosystems analysis Drought and low water level modeling Rainfall-runoff hydrological forecasting Multidisciplinary historical reconstructions Key Projects: ANR CIPRHES (2021-2024): Integrated hydrometeorological forecasting for droughts PESTINTER (2021-2023): Pesticide impacts on La Cleurie (Vosges) aquatic ecosystems RECRET (2017-2021): Moselle flood and drought history via sediment cores Technical Expertise: Hydrometry and sedimentology GIS applications in hydrology Field instrumentation Remote sensing and drone imaging

Marc Chaumontمشاهده پروفایل

دانشیار

• Visual Data Analysis
• Remote Sensing
• Steganography
• +۱۰ مورد دیگر

Marc Chaumont is an Associate Professor at the University of Nîmes since 2005 and a senior researcher at LIRMM Montpellier. He holds an HDR (Habilitation à Diriger des Recherches) and is an IEEE Senior Member. Since October 2024, he has been an associate collaborator at IRISA laboratory in Vannes, France. His career spans academic research, teaching, and interdisciplinary collaborations with institutions like MARBEC, CIRAD, and CNRS. PhD in Computer Science from IRISA Rennes (2003) Engineer Diploma from INSA Rennes (1999) His research focuses on visual data analysis , remote sensing , and digital forensics , particularly in steganography and steganalysis . He has contributed to creating large-scale databases like LSSD , a 2 million JPEG image dataset for deep-learning steganalysis. His work extends to AI applications in marine ecology, medical imaging, and poverty estimation from satellite data. Recent publications highlight collaborations across disciplines, including transformer models for socioeconomic indicator prediction, 3D fish tracking in coral reefs, and self-supervised encoder pretraining for chronic wound segmentation. He has supervised interns like Ayman El Mannouy (2024) and Abir Zahi (2023) in projects related to weakly supervised segmentation and marine biodiversity. IEEE Senior Member (2020) Top-3% Best Reviewer at IEEE ICIP 2020 Marc has taught courses ranging from signal processing to image compression since 1999. His technical contributions include software tools for image databases and steganalysis algorithms. He actively participates in European projects like DFUC'2024 and ConvEntion for astronomical data classification.

• Machine Learning
• Computer Vision
• Image Analysis
• +۳ مورد دیگر

Prof. Jean-Yves RAMEL is a faculty member at Savoie Mont Blanc University's Department of Computer Science (LISTIC laboratory). Previously, he served as a Professor at Polytech’Tours (2007–2023) and held roles at INSA Lyon (1997–2002). His research focuses on machine learning on graphs, medical and document image analysis, and interactive learning systems. He has published extensively in journals like Pattern Recognition and Medical Image Analysis . His work spans medical imaging segmentation, graph-based algorithms, and historical document processing. Notably, he contributed to projects like BVH (Bibliothèques Virtuelles Humanistes) and developed methods for graph edit distance and dynamic classification. He maintains active collaborations in digital humanities and has supervised numerous projects in computer science education. Education: PhD in Computer Science (1996), INSA Lyon: 'Interpretation automatique de documents graphiques' Maitre de conférences positions at INSA Lyon (1997–2002) and Polytech’Tours (2002–2023) Research Interests: Machine Learning on Graphs: Graph neural networks, graph embedding, and graph matching algorithms. Image Analysis: Medical imaging segmentation, historical document processing, and 3D texture analysis. Interactive Learning Systems: Dynamic classification, anytime algorithms, and one-class classifiers. Publications: Over 140 publications, focusing on graph-based methods, medical imaging, and document analysis. Recent work includes advancements in CNN/GNN pipelines for brain imaging and grammar-reinforcement learning for graphs (2025). Grants & Projects: Led the LIFAT lab (2018–2021) and contributed to the BVH digital humanities program. Active in collaborative projects involving medical imaging segmentation and historical document digitization. Labs/Teams: LISTIC laboratory (Savoie Mont Blanc University) and prior roles in LIFAT (Tours) and INSA Lyon's Interaction Collaborative lab.