Karine Zeitouni
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A Propos de moi
Karine Zeitouni is a Professor in Computer Science at the Technical Institute of Technology of Vélizy – UVSQ – Université Paris-Saclay. She received her Ph.D. in Computer Science from the University of Pierre et Marie Curie (Paris VI), and her HDR (qualification for supervising research) from the University of Versailles. She is Deputy Director of the Graduate School of Computer Science since March 2021. She is heading the Ambient Data Access and Mining (ADAM) group at DAVID laboratory. Her main research interest lies in databases, big data and data mining, with a focus on spatial and/or temporal data. She mainly applies her research in the fields of transportation, environment, universe science, and health. She has supervised 16 graduate Ph.D. students. She has co-authored over 120 peer-reviewed journal and conference papers. Her research is funded by national grants and multi-partner projects, bilateral research collaboration programs, and the European Union’s Horizon 2020 research and innovation program. She regularly serves as a PC member in international conferences in the field of (spatial) databases and data mining, machine learning and as a reviewer for national and international journals in these domains. She has (co-)chaired several conferences and workshops national and international wise.
Lieu de travail:
Bureau 304 – Bâtiment Descartes, UFR de Sciences, UVSQ
Contact
email: Karine dot Zeitouni at uvsq dot fr
Numéro de tél professionnel : +33 (0)1 39 25 40 46
Compte Linkedin
Responsabilités :
Responsable de l’équipe
Directrice adjointe à la formation de la GS ISN, Université Paris-Sacaly
Machine Learning and Artificial Intelligence:
Developing novel algorithms and techniques for machine learning and artificial intelligence, such as deep learning, reinforcement learning, natural language processing, and computer vision.
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Social Networks and Data Mining :
Exploring patterns and dynamics in social networks, as well as developing algorithms for mining and analyzing social media data, recommendation systems, and sentiment analysis.
Deep Learning for Image Recognition
This project aims to develop advanced deep learning models and algorithms for image recognition tasks. The research focuses on improving the accuracy and efficiency of image classification, object detection, and semantic segmentation using convolutional neural networks (CNNs) and attention mechanisms.
Privacy-Preserving Machine Learning
This project focuses on developing privacy-preserving techniques for machine learning algorithms. The research involves exploring methods such as differential privacy, federated learning, and secure multiparty computation to enable collaborative data analysis while preserving data privacy and confidentiality.
Natural Language Processing for Sentiment Analysis
This project aims to enhance sentiment analysis techniques by leveraging natural language processing (NLP) algorithms. The research involves developing sentiment lexicons, sentiment classification models, and sentiment analysis tools to accurately interpret and analyze sentiments expressed in text data from social media platforms and online reviews.
Blockchain-Based Smart Contracts for Supply Chain Management
This project focuses on utilizing blockchain technology and smart contracts to enhance transparency, security, and efficiency in supply chain management. The research involves developing and implementing blockchain-based solutions to track and authenticate product provenance, automate contract execution, and enable secure and traceable transactions within the supply chain.
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Disciplines
Mots-clefs
Auteurs
Auteurs de la structure
Revues
Année de production
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Laboratoires
Départements
Équipes de recherche
Articles dans une revue33 documents
Communications dans un congrès82 documents
N°spécial de revue/special issue2 documents
Ouvrages (y compris édition critique et traduction)5 documents
Chapitres d'ouvrage4 documents
Autres publications7 documents
Rapports1 document
Thèses1 document
Habilitation à diriger des recherches1 document
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Nouvelles
Offre de poste de Maitre(sses) de Conférences
Description: L’université de Versailles St-Quentin recrute un poste de maître(sse) de conférences en informatique (section 27) lors de la session synchronisée du concours. Il sera rattaché
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Offre de poste de Maitre(sses) de Conférences
Description: L’université de Versailles St-Quentin recrute un poste de maître(sse) de conférences en informatique (section 27) lors de la session synchronisée du concours. Il sera rattaché à l’UFR des sciences, département informatique et au laboratoire DAVID en recherche. Profil recherche : La recherche du candidat devra s’inscrire dans les domaines de l’équipe ADAM (Ambient Data Access […]
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