جستجوی استاد راهنمای دانشگاهی

Abderrahim Benslimane is a Full Professor of Computer Science at the University of Avignon, France, where he serves as Vice Dean of International Relations at the UFR STS (Unité de Formation et de Recherche en Sciences et Technologies). He is also Head of the master degree SICOM (Systèmes Informatiques Communicants: réseaux, services et sécurité) program at the university. His extensive academic career spans several decades with significant contributions to computer science, particularly in networking and security domains. Professor Benslimane holds a HDR (Title to supervise researches) from the University of Cergy-Pontoise, a Ph.D. from the University of Franche-Comté, along with M.S. and B.S. degrees in Computer Science from the same institution and the University of Nancy respectively. His research interests primarily focus on distributed computing, networking and communication protocols, with particular emphasis on modeling, describing and implementing secure communication protocols and multimedia applications in heterogeneous network architectures. He combines engineering and theoretical approaches using graphs, distributed algorithms, transition systems, and performance evaluation models. Benslimane's scholarly work demonstrates a strong trend toward addressing security and privacy challenges in emerging technologies. His recent publications focus on cybersecurity applications for wireless sensor networks, Internet of Things, blockchain implementations, UAV communications, and vehicular networks. He has pioneered research in energy attack mitigation, trust management systems, and secure group communications, often employing game theory and novel cryptographic approaches. His work bridges theoretical foundations with practical implementations in next-generation networking technologies. IEEE VTS Distinguished Lecturer (2020-2022) Best Paper award at IEEE ICC 2019 Multiple Prime d'Encadrement et de Recherche Doctorale awards (1998-2021) Prime d'Excellence Scientifique (2011-2015) IEEE Senior Member As an academic leader, Benslimane has served as Editor in Chief of Multimedia Intelligence and Security Inderscience Journal, Area Editor of IEEE Internet of Things Journal, and Associate Editor for multiple prestigious publications including IEEE Transactions on Multimedia and IEEE Wireless Communication Magazine. He has founded and led research centers including the Informatics Research center (CRI) at the French University in Egypt and the Multimedia and networking team (RAM) at the Laboratoire d'Informatique d'Avignon (LIA). His laboratory research focuses on security, communication protocols, graphs and distributed algorithms, with applications in ad hoc networks, sensor networks, vehicular networks, and IoT.

• Distributed Computing
• Networking
• Communication Protocols
• +۷ مورد دیگر

Pierre Marion is a Researcher at INRIA Paris, working within the Sierra research team since September 2025. His work focuses on the theoretical foundations of deep learning and he is beginning to explore applications of AI in mathematics. Marion has established collaborations across multiple institutions including EPFL, Sorbonne Université, and Google DeepMind. His educational background includes: Engineering degree from École polytechnique (2015-2018) with specialization in Applied Mathematics Master's degree from Sorbonne Université (2019-2020) PhD from Sorbonne Université (2020-2023) under the supervision of Gérard Biau and Jean-Philippe Vert Postdoctoral research at EPFL (2024) supervised by Lénaïc Chizat Marion's research interests primarily focus on the theory of deep learning, where he investigates the optimization and statistical properties of various neural network architectures. His work spans from shallow networks to complex generative models, with a particular emphasis on understanding the mathematical foundations that govern deep learning performance. Recently, he has begun exploring applications of AI in mathematical research, aiming to bridge the gap between theoretical machine learning and mathematical discovery. His research often combines rigorous theoretical analysis with practical implications for training deep neural networks. Analysis of Marion's recent publications reveals several key trends in his research. He has made significant contributions to understanding the role of large learning rates in optimization dynamics, demonstrating how they can accelerate convergence in logistic regression and prevent memorization in score-based generative models. His work on attention mechanisms has provided theoretical guarantees for their effectiveness in specific tasks like single-location regression and clustering. Additionally, Marion has extensively studied the connections between residual networks and neural ordinary differential equations , establishing generalization bounds and exploring scaling properties in the large-depth regime. His earlier work included contributions to natural language processing and quasi-Monte Carlo methods, reflecting a broad mathematical foundation that informs his current deep learning research. Marion has received several notable scientific awards: Runner-up PhD Award of AFIA (French Association for Artificial Intelligence) in 2024 Google PhD Fellowship in 2022 Ecole polytechnique Grand Prize of Research Internships in 2018 As an advisor, Marion currently supervises PhD student Yu-Han Wu (since 2024), with whom he has co-authored multiple publications on large learning rates and denoising score matching. Previously, he co-supervised several Master's students including Seorim Park, Yerkin Yesbay, and Nathan Doumèche. Marion has been actively involved in the machine learning community through conference organization (NeurIPS@Paris meetups), session chairing (ICSDS 2022), and extensive reviewing activities. He has served as a reviewer for top journals including JASA and Annals of Statistics, and conferences including NeurIPS and ICLR, where he was recognized as a top reviewer at NeurIPS 2023. Marion is a member of the Sierra research team at INRIA Paris, which focuses on machine learning theory and applications. He has also collaborated with researchers at CREST (Center for Research in Economics and Statistics), as evidenced by his participation in seminars organized by Anna Korba and Karim Lounici. His work often bridges theoretical computer science, statistics, and applied mathematics, reflecting the interdisciplinary nature of modern machine learning research.

• Deep Learning Theory
• Optimization of Neural Networks
• Generative Models
• +۲ مورد دیگر

Thierry Badard is an Associate Professor at the Department of Geomatics Sciences , Université Laval, where he also serves as Director of the Center for Research in Geospatial Data and Intelligence (CRDIG) . With over 28 years of experience in geospatial science, he leads research initiatives at the intersection of GeoAI , LiDAR processing , and smart city technologies . Director, CRDIG (2016-2022) Steering Committee Member, Big Data Research Centre (CRDM) Researcher, Institute for Intelligence and Data (IID) Research Expertise spans geospatial big data, GeoNLP, and IoT applications for digital twins. His work addresses flood risk modeling , 3D urban analytics , and environmental monitoring through AI-driven solutions. Recent publications focus on contrastive learning for LiDAR segmentation and geospatial ontologies for early warning systems. Grant Leadership includes collaborative projects on smart insurance analytics (2018-2025), Arctic bioaerosol research (2019-2025), and Quebec-Morocco digital twin partnerships (2022-2023). He has advised 15+ graduate students in geomatics and related fields.

• Geospatial Big Data
• GeoAI
• GeoNLP
• +۹ مورد دیگر

Paolo Papotti is an Associate Professor of Computer Science at EURECOM (France) since 2017, affiliated with the Data Science department. Previously, he was a senior scientist at QCRI (Qatar) and an assistant professor at Arizona State University (USA). He earned his PhD in Computer Science from the University of Roma Tre (Italy) in 2007, following an MEng in Computer Engineering from the same institution in 2003. His research focuses on scalable data management, data integration, data cleaning, and computational fact-checking. Notable contributions include work on knowledge graph rule discovery (Rudik), fact-checking frameworks (Scrutinizer), and data quality systems. His research has been supported by awards such as the 2020 Google Faculty Research Fellowship. Key publications include advancements in table representation learning, LLM-based data querying, and crowdsourced fact-checking validation. His work spans theoretical foundations and practical tools for improving data quality and information trustworthiness.

• Data Management
• Data Integration
• Data Cleaning
• +۳ مورد دیگر

Omar Rifki is an Associate Professor (Maître de Conférences) specializing in combinatorial optimization and artificial intelligence applications. His research bridges theoretical computer science with practical logistics challenges, focusing on routing problems, process mining, and machine learning integration for complex decision systems. His core research interests include phase transitions in NP-hard problems, vehicle routing optimization under time constraints, and healthcare process modeling. Rifki's work demonstrates a consistent pattern of integrating reinforcement learning with traditional optimization techniques to solve large-scale real-world problems in transportation and logistics, with particular emphasis on spatio-temporal data effects and collaborative systems. Analysis of his 15 publications (2019-2025) reveals three dominant research thrusts: (1) Fundamental studies of combinatorial problem hardness using phase transition frameworks, (2) Practical applications of deep reinforcement learning in vehicle routing and taxi assignment, and (3) Healthcare process optimization through advanced process mining techniques. His work consistently addresses scalability challenges in real-world implementations while maintaining theoretical rigor. No scientific awards were documented in the provided materials. His collaborative work with researchers like Christine Solnon and Thierry Garaix indicates active participation in European operations research communities, though specific grant details remain unreported. Rifki's research shows increasing integration of graph theory and machine learning in transportation applications, particularly evident in his Lyon City case studies on autonomous ride-sharing systems.

• Operations Research
• Combinatorial Optimization
• Artificial Intelligence
• +۳ مورد دیگر

Professor Petros Elia is a faculty member at EURECOM, holding the position of Professor within the Department of Communications systems. He specializes in Information Theory , Coding Theory , Caching , Distributed Computing , and Wireless Networks , with additional research in Biometrics . His work focuses on advancing theoretical foundations and practical applications in distributed systems and wireless communication efficiency. He received a prestigious ERC Consolidator Grant for his DUALITY project (2016) and a four-year Fulbright Scholarship (1993-1997). He is also a recipient of the Newcom++ Network of Excellence Distinguished Achievement Award (2008-2011) and the Best Student Paper Award at SPAWC 2011, awarded to his advisee Arun Singh. His research explores cutting-edge topics such as tessellated distributed computing , hypergraph decomposition , and topology-aware caching , with recent contributions presented at venues like the IEEE International Symposium on Information Theory (ISIT 2025). His work bridges theoretical advancements with real-world applications in wireless networks and distributed systems. Education: Supported by his Fulbright Scholarship, he pursued studies in the U.S. during 1993-1997. Grants: ERC Consolidator Grant (2016), and others. Advising: Mentor to Arun Singh , whose work earned a student paper award. He actively contributes to teaching Mobile Communications at EURECOM and remains a key figure in advancing the field through interdisciplinary collaborations and leadership in the Communication Systems department.

• Information Theory
• Coding Theory
• Caching
• +۳ مورد دیگر

Jon Crowcroft is the Marconi Professor of Communications Systems in the Department of Computer Science and Technology at the University of Cambridge, and serves as the Chair of the Programme Committee at the Alan Turing Institute. He is also a Fellow of Wolfson College, Cambridge, and a visiting professor at the Department of Computing at Imperial College London. With a career spanning over three decades in computer networking research, Professor Crowcroft has made seminal contributions to the development of the Internet and continues to be highly active in cutting-edge research areas. His educational background includes: BA in Physics from Trinity College, University of Cambridge (1979) MSc in Computing from University College London (1981) PhD from University College London (1993) Professor Crowcroft's research spans multiple domains in computer networking and distributed systems. He has worked in Internet support for multimedia communications for over 30 years, with three main focus areas: scalable multicast routing, practical approaches to traffic management, and the design of deployable end-to-end protocols. His current research focuses on opportunistic communications, social networks, and techniques to scale infrastructure-free mobile systems. He is particularly known for his 'build and learn' paradigm for research and has recently been exploring decentralized digital identification systems, smart cities, and edge computing. His work often bridges theoretical foundations with practical implementations, emphasizing privacy-preserving approaches and sustainable network architectures. Professor Crowcroft has received numerous prestigious awards recognizing his contributions to the field, including: Election as Fellow of the Royal Society (2013) ACM SIGCOMM Award (2009) ACM Fellow (2002) Fellow of the Royal Academy of Engineering IEEE Fellow (2004) Chartered Fellow of the British Computer Society Throughout his career, Professor Crowcroft has advised numerous PhD students, including Mark Handley and Pan Hui, who have themselves become influential researchers in the networking community. He has authored several influential books that have been adopted internationally in academic courses, such as 'TCP/IP & Linux Protocol Implementation,' 'Internetworking Multimedia,' and 'Open Distributed Systems.' His research has been supported by various grants and collaborations with both academic institutions and industry partners, contributing to successful startup projects and influencing Internet standards. Professor Crowcroft is actively involved in several research initiatives, including serving on the Scientific Council of IMDEA Networks Institute since 2007 and the advisory board of the Max Planck Institute for Software Systems. He is also a director of the Matrix Foundation, which develops open network protocols. His current research group focuses on privacy-preserving analytics, decentralized systems, and the future of Internet architecture.

• Computer Networks
• Distributed Systems
• Opportunistic Communications
• +۷ مورد دیگر

Antoine Doucet is a Full Professor at the University of La Rochelle, where he teaches in the Computer Science department of the University Institute of Technology (IUT). He conducts his research at the Computer Science, Image and Interaction Laboratory (L3i) within the 'Images and Content' team, which he has led since 2015. He is also a member of the Franco-Vietnamese laboratory ICTLab and serves as Director of the ICT Department at the University of Science and Technology of Hanoi since 2016. His research focuses on information retrieval, natural language processing, text mining, and artificial intelligence, with emphasis on automatic analysis of text in all forms across languages. His work prioritizes generic methods that work across languages without relying on language-specific linguistic resources. This approach is particularly valuable for under-resourced languages and noisy texts from sources like social media or OCR output. As coordinator of the Horizon 2020 NewsEye project, he led efforts to improve access to European historical newspapers through semantic enrichment and advanced search capabilities. His research has practical applications in epidemic surveillance, document fraud detection, and historical content analysis. The NewsEye project involved 11 teams across Europe, including 3 national libraries and multiple research groups. Best paper award from IMIA Yearbook 2016 (among 1,272 candidates) Best paper award at HCI International with Ilona Nawrot Press coverage for ACL 2013 paper in major publications Recipient of French scientific excellence award (Prime d'Excellence Scientifique) Doucet actively supervises PhD and Master's students, with recent advisees including Chloé Artaud (Document fraud detection), Paul Martin (Photograph Time-Stamping), Ilona Nawrot (Temporal and Multilingual Text Analysis), and Gaël Lejeune (Multilingual Epidemic Surveillance). His research has been funded through multiple projects including ANR Digistory, AmeliOCR, PHC Nusantara, and USTH SWARMS. He has also coordinated significant European projects like NewsEye and Embeddia. At L3i, he leads a research group of approximately 40 persons focused on Images and Digital Content. His work bridges theoretical advances in multilingual text processing with practical applications in historical document analysis, epidemic surveillance, and document security.

• Information Retrieval
• Natural Language Processing
• Text Mining
• +۷ مورد دیگر

Evripidis Bampis is a Professor of Computer Science at Sorbonne Université, affiliated with the LIP6 laboratory and the Operations Research (RO) group. He currently serves as the Head of the Master of Computer Science program and leads the AGAPE research group within the GDR Recherche Opérationnelle. His research focuses on approximation algorithms, energy-aware computing, algorithmic game theory, scheduling, and graph problems. He has been actively involved in organizing international conferences and workshops, including co-chairing the International Symposium on Fundamentals of Computation Theory (FCT) in 2021 and 2023, and chairing the Symposium on Experimental Algorithms (SEA) in 2015. His collaborative projects include ANR-funded initiatives such as ALGORIDAM (2020–2024) and ENERGUMEN (2018–2023), which address algorithmic theory and energy efficiency in distributed systems. Bampis has advised numerous PhD students and co-authored influential publications in scheduling and algorithm design. His international collaborations span institutions like the Technical University of Munich, the Chinese Academy of Sciences, and Imperial College London. He maintains a strong presence in academic committees and has contributed to EU-funded thematic networks like APPOL and APPOL II.

• Approximation Algorithms
• Energy-Aware Algorithms
• Algorithmic Game Theory
• +۵ مورد دیگر

Marc Plantevit is a Full Professor at EPITA, member of the Laboratoire LRDE (LRDE). Previously, he served as an Associate Professor at University Claude Bernard Lyon 1 (2010–2021), leading the Data Mining & Machine Learning group at LIRIS lab. He holds a PhD in Computer Science from the University of Montpellier (2008), supervised by Maguelonne Teisseire and Anne Laurent at LIRMM Lab. His research focuses on foundational data mining, graph mining, subgroup discovery, and explainable AI. He is an editorial board member of Data Mining and Knowledge Discovery Journal and has held roles such as CAPES NSI jury member and former head of the Data Mining & Machine Learning group at LIRIS. Research Interests : Data Mining, Machine Learning, Explainable AI, Graph Mining, Subgroup Discovery, Exceptional Model Mining, Constraint-based Pattern Mining, and applications in neuroscience and energy systems. His work explores interpretable AI, GNN explainability, and interdisciplinary applications like odor perception modeling and electricity price forecasting. Key Contributions : Best Paper Award at EGC'22 for work on GNN representations. Active in program committees for ECMLPKDD, IJCAI, and IEEE ICDM . Supervised PhD students working on GNN explainability, electricity forecasting, and machine learning in exposome studies. Labs & Teams : LRDE (EPITA), previously involved with LIRIS (UMR CNRS 5205) and collaborative projects with institutions like INSA Lyon and ISGlobal (Barcelona).

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Eric TOTEL is a Professor at Telecom SudParis, specializing in cybersecurity and network security. His research focuses on intrusion detection systems, graph-based anomaly detection, machine learning applications in security, and data confidentiality in distributed systems. He has contributed to projects such as DAMS (DDoS mitigation using deep reinforcement learning), Sec2Graph (novelty detection on graph-structured data), and DAEMON (dynamic autoencoder-based anomaly detection). His work emphasizes scalable solutions for multi-step attack detection and privacy-preserving infrastructure for encrypted DNS logs. Key contributions include developing correlation engines for distributed systems, formalizing invariant-based attack detection in web applications, and exploring static analysis for information flow control. He has authored over 50 peer-reviewed publications and served on program committees for conferences like RAID, CRiSIS, and EuroS&P. His HDR (2012) formalized error-detection techniques applied to intrusion detection. Advising and grants: He collaborates on projects funded by French national research agencies and has mentored students in cybersecurity, AI for defense (CAID conferences), and cloud infrastructure security. His research often bridges theoretical models and practical implementations, with tools like STARLORD for 3D graph visualization of security data.

• Network Security
• Intrusion Detection Systems
• Data Confidentiality
• +۳ مورد دیگر

Nofar Carmeli is a researcher at Inria , affiliated with the Boreal joint project-team (LIRMM, Inria, University of Montpellier, CNRS) in Montpellier, France. Her work focuses on theoretical aspects of database query optimization, particularly through the lenses of fine-grained complexity and enumeration complexity. PhD from Technion (2015-2020), advised by Prof. Benny Kimelfeld Postdoctoral Researcher at ENS Paris (2021-2022) and Inria's Valda project-team Holds the Schmidt Postdoctoral Award (2021-2022) Her research explores optimal algorithms for database query answering, including direct access to ranked answers, quantile computation, and handling of conjunctive queries with self-joins or negation. She investigates how structural properties of queries and data constraints like functional dependencies affect computational complexity. Key contributions include: Establishing tractability boundaries for direct access to conjunctive queries Developing efficient algorithms for minimal triangulation enumeration Advancing probabilistic database representations for infinite domains Creating explainable opinion graph frameworks for review summarization Scientific awards: Google PhD Fellowship (2019) Schmidt Postdoctoral Award (2021-2022) PODS Best Student Paper (2019) She has served on program committees for STACS (2025), PODS (2023-2025), and ICDT (2022-2024), and contributed to open-access research dissemination through Arxiv and dblp.

• Database Query Optimization
• Fine-Grained Complexity
• Enumeration Complexity
• +۲ مورد دیگر

Basile de Loynes is a Lecturer at the French National School of Statistics and Information Analysis (ENSAI), holding a permanent academic position since at least 2016. He maintains a dual affiliation as a CREST (Center for Research in Economics and Statistics) Affiliated Member, contributing to interdisciplinary economic-statistical research. His academic trajectory includes a postdoctoral position at the University of Neuchâtel (2012), followed by temporary lecturer roles at the University of Burgundy (2013-2014) and University of Strasbourg (2014-2016). His research centers on advanced probability theory with specific expertise in stochastic processes on non-Euclidean structures. Key areas include: Random walks on algebraic structures (groups, groupoids, tilings, graphs) Poisson-Martin boundary theory and potential analysis Long memory processes and invariance principles Graph signal processing with Fourier/wavelet methods His publication record shows consistent output in top-tier journals since 2012, with recent work (2021-2023) focusing on graph-based signal denoising and differential privacy applications. Analysis of his 10 most recent publications reveals a strong methodological thread connecting classical probability theory with modern graph-based signal processing. Approximately 60% of his work since 2016 involves graph-structured stochastic models, demonstrating an evolving research trajectory from theoretical random walk properties toward applied graph signal analysis. The recurring subfields across publications include Markov additive processes, spectral graph theory, and wavelet transforms on non-Euclidean domains. His academic service includes developing comprehensive teaching materials for core probability and measure theory courses at ENSAI, with publicly available lecture notes and examinations dating back to 2016.

• Probability Theory
• Stochastic Processes
• Graph Signal Processing
• +۴ 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.

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

Amin Mesmoudi serves as Associate Professor in Data Engineering at the University of Poitiers' IUT (Institut Universitaire de Technologie), with dual laboratory affiliations at LIAS-ENSIP (Poitiers campus) and LIAS-ISAE-ENSMA (Chasseneuil campus). His research bridges theoretical database systems with practical large-scale data engineering challenges, particularly in semantic web technologies and machine learning applications. The laboratory maintains physical presences at both ENSIP's Bâtiment B25 in Poitiers and ISAE-ENSMA's Téléport 2 facility in Chasseneuil, facilitating cross-institutional collaboration. Mesmoudi's research program centers on scalable data management systems, with three interconnected pillars: (1) RDF and graph-based query optimization techniques for billion-triple datasets, (2) machine learning integration for spatial query performance and anomaly detection, and (3) explainability frameworks for complex black-box models. His work demonstrates consistent evolution from foundational database systems (2011-2016) toward contemporary AI-driven data engineering, particularly evident in his 2023-2025 publications on temporal dependency preservation and co-selection explainability. The Data Engineering team within LIAS laboratory provides the primary research context for these investigations. Publication analysis reveals strong methodological continuity in addressing scalability bottlenecks across database paradigms. Early work focused on SQL-on-MapReduce benchmarking for astronomy databases (2015-2016), transitioning to specialized RDF processing frameworks (2019-2021), and culminating in current hybrid approaches combining temporal modeling with machine learning (2023-2025). Key technical themes include fragmentation strategies for distributed data, optimizer feedback mechanisms, and graph-based query acceleration - all targeting real-world performance constraints in big data environments. As a core member of LIAS laboratory's Data Engineering team, Mesmoudi contributes to France's national research infrastructure in computer science and automation systems. The laboratory's dual-university structure enables unique cross-pollination between University of Poitiers' academic programs and ISAE-ENSMA's engineering specialization, with Mesmoudi's work exemplifying this synergy through applications spanning astronomy databases to wireless sensor networks.

• Data Engineering
• Machine Learning
• Anomaly Detection
• +۵ مورد دیگر