جست‌وجوی استاد و پژوهشگر

Azelle Courtialمشاهده پروفایل

پژوهشگر

• Map generalization
• Deep learning
• Multi-scale maps
• +۳ مورد دیگر

Azelle Courtial is a Researcher at Institut Géographique National (IGN) , specializing in deep learning applications for cartographic generalization within the MEIG research team (Modélisation des Environnements Inhabités et Généralisation). Her work focuses on multi-scale mapping automation and spatial data representations , with recent contributions in GAN-based mountain road generalization and deep learning benchmark development . Education: PhD in Geographic Information Sciences (Université Paris-Est, 2023), Master's in GIS (2019), Licence in Math & Informatics (2017) Projects: ERC Consolidator Grant recipient (2021-2026) Software: Developer of CartAGen4Py and DeepMapGen libraries Her publications demonstrate a research trajectory combining deep learning for map generalization multi-scale cartography principles constraint-based evaluation frameworks spatial relation modeling in GIS Scientific recognition includes: Best short paper award at AGILE Conference 2022 Teaching contributions span spatial databases , webmapping , and GIS programming , with responsibilities in course material development and student project supervision.

• Algorithms
• Databases
• Programming Languages
• +۳ مورد دیگر

Louis Jachiet is an Assistant Professor in Computer Science at Télécom Paris, affiliated with the Data, Intelligence and Graphs (DIG) team within the Information Processing and Communication Laboratory (LTCI) and the Computer Sciences and Networks (Infres) department. His research focuses on algorithms, databases, programming languages, and logic. His work spans query optimization distributed SPARQL evaluation graph algorithms formal language theory program synthesis data provenance with a strong emphasis on bridging theoretical and applied research. Analysis of his publications reveals expertise in database systems graph processing automata theory query enumeration SPARQL optimization probabilistic databases across both theoretical and practical applications.

Bastien CAZAUXمشاهده پروفایل

مدرس

• Bioinformatics
• Algorithms
• Genome Assembly
• +۳ مورد دیگر

Bastien CAZAUX is a Lecturer affiliated with ESPRIT, located at Office S3.49 in Scientific City. His research focuses on bioinformatics algorithms, computational genomics, and data compression techniques. Role: Lecturer at ESPRIT Location: Office S3.49, Scientific City Team: Bonsai His research interests span bioinformatics, algorithms, genome assembly, and graph-theoretic approaches to genomic data processing. Key contributions include work on de Bruijn graphs, k-mer indexing, and efficient compression methods for biological sequences. Recent publications highlight trends in pan-genomic analysis , motif discovery , sequence alignment , and graph-based genome representation . These works often combine algorithmic innovation with practical applications in genomic data processing. He is associated with the Bonsai research team, contributing to foundational work in computational biology and algorithm design.

• Network Science
• Data Science
• Complex Networks
• +۳ مورد دیگر

Artur Ziviani is a Senior Researcher at the Data Extreme Lab (DEXL) of the National Laboratory for Scientific Computing (LNCC), a research unit under Brazil's Ministry of Science, Technology, and Innovation (MCTI). He coordinates LNCC's Multidisciplinary Graduate Program in Computational Modeling (M.Sc./Ph.D.). With a Ph.D. from Université Pierre et Marie Curie (Paris 6) and a B.Sc./M.Sc. from Federal University of Rio de Janeiro (UFRJ), he has held visiting positions at INRIA (France) and taught at Paris 6. His research focuses on network science, data science, and interdisciplinary applications, including complex networks, machine learning, and mobility analysis. He is a Senior Member of IEEE and ACM, and an Affiliated Member of the Brazilian Academy of Sciences (2013-2017). His work spans algorithmic information theory, network centrality, and human-aware communication strategies. Recent projects include mobility modeling, protein interaction networks, and biodiversity informatics. Education: Ph.D. (Paris 6, 2003), M.Sc. (UFRJ, 1999), B.Sc. (UFRJ, 1998). Research interests include network characterization, data-driven modeling, and applications in healthcare and biodiversity. His publications analyze network structures, centrality metrics, and spatiotemporal data, with contributions to IEEE, ACM, and Elsevier journals. He also leads initiatives on SDN architectures for high-performance computing and collaborative learning platforms. Awards include recognition as an IEEE Impact Creator and senior fellowships. His team works on projects like SafeCityMap (mobility during pandemics) and MobHet (heterogeneous mobility prediction). He serves on editorial boards for Computer Networks and IEEE Communications Surveys.

• Computer Vision
• Image Processing
• Autonomous Systems
• +۶ مورد دیگر

Antoine Manzanera is a Professor HDR at ENSTA Paris, affiliated with the Unité d'Informatique et d'Ingénierie des Systèmes (U2IS) and the Systèmes autonomes et Robotique (SAR) department. His research focuses on Computer Vision, Image Processing, and Autonomous Systems, with notable contributions to medical imaging applications and robotics. He holds a Habilitation Degree from Université Pierre et Marie Curie (2012) and has taught at ENSTA, Université Paris Sorbonne, and international institutions. BSc Mathematics, Université Claude Bernard Lyon 1 (1991) MSc Theoretical Computer Science, Université Claude Bernard Lyon 1 (1993) PhD Signal and Image Processing, Télécom ParisTech (2000) Research interests span Computer Vision fundamentals, including Video Analysis, Scene Reconstruction, and Autonomous Visual Learning. His work bridges theory and practice, addressing challenges in medical diagnostics (e.g., colonoscopy analysis, Parkinson's biomarkers) and robotics (e.g., drone navigation, autonomous driving datasets). Recent articles highlight advancements in multimodal data fusion, deep learning optimization, and medical imaging tools. Collaborations include projects on synthetic databases for colon depth estimation and Riemannian geometry for oculomotor analysis. His contributions to open-source frameworks like Video++ and educational initiatives (e.g., DroMOOC on drones) further his impact.

• Cloud Computing
• Distributed Systems
• High-Performance Computing
• +۵ مورد دیگر

Raja Appuswamy is an Assistant Professor in the Department of Data Science at EURECOM, focusing on data management on modern hardware and molecular information storage. His research integrates cutting-edge storage technologies with bioinformatics, particularly in DNA-based data storage. He teaches courses in cloud computing, distributed systems, and databases. His work spans Optimizing storage hierarchies with DNA archival systems Hardware-conscious algorithms for GPUs and heterogeneous architectures Error correction in molecular storage Long-term database preservation strategies Key research interests include Cross-architecture data joins (e.g., OneJoin, XJoin) Cold storage innovation with CMOSS and OligoArchive Integration of bio-inspired storage with traditional computing and explores interdisciplinary challenges at the intersection of computer science and molecular biology. Publications emphasize scalable solutions for DNA storage density, error tolerance, and archival systems. Awards highlight contributions to DNA-based image encoding, storage reliability, and page cache checksums. Current projects focus on enzymatic DNA ligations for storage density improvement, motif-based error correction, and hardware acceleration for knowledge graphs. His work targets both theoretical advancements and practical implementations in storage systems.

• Database Systems
• Semantic Web
• Graph Databases
• +۳ مورد دیگر

Jacques CHABIN serves as a Lecturer at the University of Orleans, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. His academic role centers on advancing database theory and semantic technologies within the institution's computer science research ecosystem. His research spans database systems with emphasis on semantic web architectures, graph database evolution, and formal language applications. Key investigations include RDF/S schema management, null value handling in incomplete databases, and context-driven querying systems for urban graph analysis. Recent work addresses privacy-preserving techniques in semantic networks and consistency maintenance during database evolution. Analysis of his 15 most recent publications reveals persistent focus on graph-based data management systems. The 2020-2024 output demonstrates progression from foundational XML schema evolution toward contemporary property graph challenges, with consistent attention to data consistency, constraint enforcement, and practical tool development like DataFix for database repair. As an active member of LIFO, Chabin contributes to the laboratory's mission in fundamental computer science research. His collaborative work with researchers including Mirian Halfeld-Ferrari and Nicolas Hiot reflects integration within the laboratory's database systems research group focused on theoretical and applied data management challenges.

• Ontology-Based Databases
• Semantic Web
• Data Integration
• +۴ مورد دیگر

Guy Pierra serves as a Full Professor in Data Engineering at ISAE-ENSMA, affiliated with the Laboratory of Applied Informatics and Systems (LIAS). His work bridges academic research and industrial standardization through the LIAS laboratory's dual locations at ENSIP in Poitiers and ISAE-ENSMA in Chasseneuil. His research focuses on ontology-based database systems , semantic data integration , and industrial automation standards . Key contributions include the development of OntoQL (an ontology query language), the OntoDB database system, and foundational work on ISO 13584 standards for parts libraries. His research demonstrates consistent evolution from geometric modeling in the 1990s toward semantic web technologies and ontology-driven data integration in the 2000s. His publication trends reveal deep specialization in Ontology-embedded database architectures Standardized product data exchange (PLIB/ISO 13584) Semantic query processing for heterogeneous systems Industrial applications of ontology reasoning with significant contributions to both theoretical foundations and practical implementations. Pierra has led numerous research collaborations through the LIAS laboratory, particularly in ontology-based data integration projects. His work has directly contributed to international standards including multiple parts of ISO 13584 for industrial automation systems and parts libraries. He maintains active research through the Data Engineering team at ISAE-ENSMA, focusing on next-generation semantic data management systems that address challenges in engineering databases, electronic commerce, and industrial automation.

Badran Raddaouiمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Argumentation Frameworks
• +۷ مورد دیگر

Badran Raddaoui is a Senior Lecturer (Maître de Conférences) at Telecom SudParis, where he conducts extensive research in artificial intelligence with a focus on knowledge representation, reasoning under uncertainty, and data mining. His work bridges theoretical foundations with practical applications in knowledge management and pattern discovery. His primary research interests include: Argumentation frameworks and logical reasoning systems Measurement and quantification of inconsistencies in knowledge bases SAT-based approaches for data mining problems Ontology reasoning under uncertainty Community detection in complex networks High utility itemset mining from transaction databases Dr. Raddaoui's recent publications demonstrate a strong emphasis on symbolic AI techniques for pattern extraction and robust reasoning with imperfect information. His work on inconsistency measurement through minimal inconsistent subsets and prime implicates has established him as a notable contributor to the field. He frequently applies constraint programming and SAT solving to problems in data mining and knowledge representation. His publication record shows consistent output from 2010 through 2024, with numerous papers in top-tier AI venues including IEEE Intelligent Systems, IJCAI, ECAI, and AAMAS. His research often involves collaborations with Saïd Jabbour and other researchers across multiple institutions. As an academic, Dr. Raddaoui contributes to the scholarly community through conference reviewing and participation in research projects focused on AI theory and applications. His current work continues to advance frameworks for knowledge base repair and conceptual clustering through pattern mining techniques.

Fabrice MUHLENBACHمشاهده پروفایل

مدرس

• Artificial Intelligence
• Data Mining
• Text Mining
• +۴ مورد دیگر

Fabrice MUHLENBACH is a Lecturer in Computer Science at Jean Monnet University of Saint-Étienne, where he has been working since September 2003. He conducts his research within the Hubert Curien Laboratory (UMR CNRS 5516), specifically as a member of the Connected Intelligence research team. His academic background is multidisciplinary, combining computer science with cognitive psychology and cognitive sciences. MUHLENBACH's research focuses on artificial intelligence, particularly in improving recommendation processes from intelligent systems. His work addresses ethical considerations in AI and explores ways to escape the "filter bubbles" commonly encountered in recommendation systems. He has developed innovative approaches to recommendation systems that consider complementary cultural forms when users become saturated with similar content. His research also investigates the potential usefulness of knowledge from scientific disciplines outside one's expertise. His publications demonstrate expertise in data mining, text mining, recommendation systems, and AI ethics. His work spans from theoretical foundations to practical applications, with notable contributions in clustering algorithms, semantic search, and health informatics. His research has practical implications, as evidenced by the industrial patent filed for a method of automatic multimedia content selection. Best Paper Award at ADMA 2017 conference Patent FR3046269 for "Method for automatic selection of multimedia content in a database" (2017) Contributions to Encyclopedia of Social Network Analysis and Mining (2014, 2017) MUHLENBACH has supervised doctoral theses in recommendation systems and data mining, and has been actively involved in teaching various computer science subjects including data analysis, machine learning, and computer ethics. His work bridges technical aspects of AI with human-centered considerations, reflecting his background in both computer science and cognitive psychology.

• Web Data Management
• Probabilistic Databases
• Provenance in Databases
• +۴ مورد دیگر

Pierre Senellart is a Professor in the Computer Science Department at École normale supérieure (ENS, Université PSL) and deputy director of the DI ENS laboratory, a joint CNRS/Inria/ENS unit. He leads the Valda team at Inria Paris and holds a chair in the PRAIRIE Paris School of AI. University: École normale supérieure (Université PSL) School: Department of Computer Science (DI ENS) Academic Rank: Professor Research Interests: His work bridges theoretical and practical aspects of web data management, including web crawling, archiving, information extraction, uncertainty management, and intensional data systems. He contributes to probabilistic XML data models, provenance tracking, and knowledge base construction. Recent Article Trends: His Google Scholar publications (2025–2023) focus on provability in probabilistic databases, theorem extraction from PDFs, and AI applications in uncertain data analysis. Topics span game theory, knowledge compilation, and multi-modal learning for scientific documents. Scientific Awards: Junior member of Institut Universitaire de France (2020–2025) Distinguished SIGMOD 2017 Program Committee Member ACM HyperText 2014 Douglas Engelbart Best Paper Award Adviser of Clément Genzmer (SIGMOD Programming Contest 2009 winner) Grants & Projects: He secures major funding (e.g., PRAIRIE Paris School of AI: €575k, DesCartes CNRS@CREATE: SGD50M), with roles in research projects like Dissemin (open science platforms) and ARCOMEM (social web archiving).

Julien Basteمشاهده پروفایل

دانشیار

• Algorithms
• Graph Theory
• Parameterized Complexity
• +۵ مورد دیگر

Julien Baste is an Associate Professor at the University of Lille , teaching computer science at IUT de Lille . He conducts research within the ORKAD team at CRIStAL (CNRS UMR 9189), focusing on theoretical and applied aspects of algorithms and graph theory. Research Interests : Algorithms, graph theory, parameterized complexity, multi-objective optimization, and computational diversity. Recent Publications : His work spans FPT algorithms for subtree problems, reload cost optimization, diversity in hitting sets, and edge modification problems, appearing in venues like SIAM Journal on Computing , Discrete Mathematics , and Artificial Intelligence . Julien Baste collaborates with researchers across Europe and contributes to international conferences such as CEC , ICGT , and SIDMA . His academic role is full-time and ongoing since 2020.

• Data Mining
• Pattern Mining
• Database Systems
• +۳ مورد دیگر

Arnaud Giacometti is a Professor at the University of Tours, affiliated with the Faculty of Science and Technology and the Computer Science Department. He is also the President of the University and leads research at the Tours Fundamental and Applied Computer Science Laboratory (LIFAT). Research Interests: His work focuses on data mining, pattern discovery, and knowledge graph analysis. He has developed innovative methods for frequent pattern mining, sequential pattern sampling, and constraint-based clustering, emphasizing efficiency and scalability in large datasets. Recent Publications: His research trends include trie-based sampling, reservoir pattern sampling in data streams, and semantic web-driven comparison tables. Key subfields span distributed databases, knowledge graph representativeness, and memory-aware parallelization for big data. Scientific Roles: As President of the University of Tours, he oversees institutional leadership while maintaining active research contributions. His laboratory (LIFAT) serves as a hub for fundamental and applied computer science research.

Talel Abdessalemمشاهده پروفایل

استاد

• Database Systems
• Data Mining
• Machine Learning
• +۴ مورد دیگر

Talel Abdessalem is a Professor at Télécom Paris , where he has held leadership roles including Director of LTCI Research Laboratory (since 2017) and Dean of Research (since 2018). He currently serves as Deputy Vice-President for Research at Institut Polytechnique de Paris . PhD in Computer Science from Paris-Dauphine University Habilitation (HDR) from UPMC-Sorbonne University His research spans large-scale data management , recommender systems , social network analysis , and uncertain data modeling . He has participated in numerous national (ANR, FEDER) and European (FP7) research projects, with recent work focusing on stream-based learning , graph analytics , and privacy-preserving systems . His publications include diverse contributions to directed graph centrality algorithms (2021), stream recommender frameworks (River, Scikit-Multiflow), and geospatial recommendation models (ALGeoSPF). He has supervised 14 PhD students and collaborates with researchers in France, Brazil, and Indonesia. Co-leads DIG (Data, Intelligence and Graphs) research team Director of LTCI (Information Processing and Communication) laboratory

• Machine Learning Systems
• Data Science
• Big Data Computing
• +۵ مورد دیگر

Professor Vassilis Christophides is a Full Professor at the École Nationale Supérieure d'Électronique et de ses Applications (ENSEA), part of the University of Cergy-Pontoise. His research focuses on Machine Learning Systems, Data Science & Big Data Computing, Databases and Semantic Web Systems, and Digital Libraries. He has published over 155 articles in top-tier journals/conferences like ACM SIGMOD and IEEE ICDE. Teaching includes courses such as Machine Learning, Big Data Computing, and Transparency in AI at the undergraduate and master's levels. He actively serves in conference roles (e.g., Tutorial Chair at WISE 2022), steering committees (EDBT Association), and as a reviewer for ERC grants. His work emphasizes ethical AI, explainable systems, and scalable data management across domains like Cultural Heritage and Environmental Sciences. Notable contributions include the BDA 2021 Best Paper Award and leadership in projects like the NOON research group. His work bridges theoretical advancements with real-world applications in data-driven decision-making and system optimization.