جست‌وجوی استاد و پژوهشگر

• Probability and Statistics
• Point Processes
• Reinforcement Learning
• +۷ مورد دیگر

Didier Gemmerlé is a CNRS Research Engineer affiliated with the Institute Élie Cartan de Lorraine (IECL) at the University of Lorraine's Faculty of Science and Technology. His work bridges Probability and Statistics with computational methods, focusing on spatial point processes, Bayesian inference, and machine learning applications in astrophysics and paleobiology. Research Areas: Point Processes, Reinforcement Learning, Causality, Neural Networks, Galaxy Distribution Modeling Responsibilities: Head of IT Infrastructure, CNRS/University signature delegations, Mathrice network member His recent publications emphasize computational frameworks for spatial statistics and cosmology. He actively contributes to open-source tools like DRlib (C++) and Treex (Python). Mentoring across M2 IMSD projects and co-supervising doctoral students (e.g., Nathan Gillot) highlights his advisory role. Current involvements include the European Twinning EXCOSM project for cosmological research.

• Data Privacy
• Semantic Graph Databases
• Differential Privacy
• +۴ مورد دیگر

Cédric EICHLER is a Lecturer at INSA Centre Val de Loire , affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research lab. His work focuses on Data Privacy , Semantic Graph Databases , and Graph Rewriting techniques to enhance confidentiality in heterogeneous systems. His research explores Differential Privacy for RDF graphs, semantic data sanitization , and privacy-preserving data transformations . Recent publications analyze privacy operators , membership inference attacks on large language models, and secure geo-distributed graph processing using synthetic graphs. Key collaborations include Benjamin NGUYEN, Sara TAKI, and Adrien BOIRET. Cédric contributes to frameworks for multi-scale software architectures and industrial IoT security , emphasizing formal methods and privacy-by-design principles. His affiliations span projects like SENDUP and GIRAFON , addressing privacy in semantic networks and k-core computation . No scientific awards are explicitly mentioned in the provided data.

• Machine Learning
• Pattern Recognition
• Computer Vision
• +۳ مورد دیگر

Dr. Franck Dufrenois is an Associate Professor at LISIC (Laboratory of Computer Science, Signal and Image of the Opal Coast), affiliated with the University of the Littoral Opal Coast in Calais, France. He successfully defended his Habilitation to Direct Research (HDR) on January 22, 2025, titled "Contributions to Discriminant Analysis: Applications to Signals and Images," qualifying him to supervise doctoral research and advance within the French academic system. His research centers on enhancing Linear and Kernel Discriminant Analysis (LDA/KDA) to address modern challenges in Big Data, including scalability for massive datasets, robustness against outliers, and integration of multimodal data (images, audio, text). His work bridges theoretical machine learning with practical applications in computer vision, IoT systems, and image classification, emphasizing efficient dimensionality reduction and structural pattern discovery. As an active LISIC laboratory member, Dr. Dufrenois contributes to research in tensor-based multimodal learning and contaminated-context single-class analysis. His HDR thesis demonstrates expertise in developing memory-efficient algorithms for real-world data analysis while maintaining strong generalization capabilities across diverse application domains.

• Distributed Systems
• Blockchain Technology
• Cybersecurity
• +۵ مورد دیگر

Romaric LUDINARD is an Associate Professor at IMT Atlantique since 2017, affiliated with the Department of Systems, Networks, Cybersecurity, and Digital Law (SRCD). He holds leadership roles, including co-head of the post-master professional certificate in Cybersecurity (with CentraleSupélec) and Deputy Head of the SRCD department. Previously, he was Associate Professor at ENSAI (2015–2017), where he led the Data Scientist specialization and computer science team. His career includes research and teaching roles at the University of Rennes 1 and international collaborations, such as a postdoc at Sapienza University of Rome (2015). Research Interests: Focus on distributed systems, blockchain protocols (safety, scalability), fault tolerance, and cybersecurity. Key areas include consensus mechanisms, intrusion detection, and Byzantine fault tolerance in large-scale networks. Current projects involve blockchain compression (BC4SSI), federated learning security (RADAR), and 5G radio access networks. Publications & Projects: Over 30 publications in top venues (IEEE/ACM conferences, journals). Notable works include geometric FTM localization techniques, Bitcoin network analysis, and federated learning reputation systems. Active in grants like Beyond5G (BPI France) and ANR JCJC (BC4SSI). Holds patents for anomaly detection in distributed systems. Teaching: Courses on blockchain, distributed systems, cybersecurity, and programming (Python, object-oriented design). Supervised 10+ PhD/Master students, including defended theses on blockchain improvement proposals and federated learning trust frameworks. Service & Talks: Organizer of AlgoTel workshops, TPC member for SSTIC, NCA, and ICDCN. Delivered keynote speeches on blockchain scalability and fork avoidance (e.g., BART Initiative, DSN'16). Active in conference organization and review committees.

Denis Conanمشاهده پروفایل

دانشیار

• Distributed Systems
• Middleware
• Mobile Computing
• +۴ مورد دیگر

Denis Conan is an Associate Professor (HDR) at Télécom SudParis, part of the Institut Polytechnique de Paris. He is affiliated with the SAMOVAR laboratory, specifically the ACMES team and groups PDS and DiSSEM. His research focuses on distributed systems, middleware, mobile and ubiquitous computing, IoT, fault tolerance, and context management. He holds a HDR from Sorbonne Universités and has extensive experience in academic projects like ANR INCOME and Totem. He teaches courses on distributed systems, software architecture, and compilation. Notable contributions include middleware frameworks like MIMOSAE and muDEBS, and he advises numerous PhD students in IoT and distributed systems. Education: HDR in Distributed Systems (2015), PhD from Sorbonne Universités, and Engineer from ENSIIE. His research interests span IoT middleware, adaptive systems, and energy-efficient architectures. Recent work emphasizes IoTVar middleware for application development and EDICT simulation tools for IoT environments. He has contributed to privacy-preserving publish/subscribe systems and energy-aware IoT frameworks. Awards include a best paper at IEEE Pervasive Intelligence and Computing 2017. Advising includes projects on self-adaptive IoT systems, edge infrastructure planning, and context-aware data exchange. He collaborates on projects like INCOME (context management for IoT) and Totem (mobile mixed reality games). His software contributions include COSMOS and MIMOSAE, addressing context composition and microservice evolution.

Daniel RANCمشاهده پروفایل

مدرس ارشد

• Big Data
• Cloud Computing
• IP Multimedia Subsystem
• +۵ مورد دیگر

Daniel RANC is a Senior Lecturer at Telecom SudParis, affiliated with the NeSS research group within the SAMOVAR laboratory. His academic work focuses on Big Data processing, cloud computing, and IP Multimedia Subsystem (IMS) networks, with particular expertise in data modeling, NoSQL databases, and quality of service management. His research interests span multiple domains including Big Data analytics, where he has developed preprocessing and modeling tools; cloud computing with predicate-based resource management; and telecommunications with a focus on IMS networks and QoS management. His work demonstrates strong interdisciplinary connections between data science, network engineering, and educational technology. RANC's publication record shows consistent contributions to his fields from 2007 through 2016, with a particular concentration on Big Data processing frameworks and network management systems. His research demonstrates a progression from eLearning systems interoperability toward more complex Big Data and cloud computing challenges. Daniel RANC collaborates extensively with researchers including Hadi Hashem and Brahim Raouyane, with whom he has co-authored numerous publications across multiple conferences and journals. His work appears in reputable venues including Foundations of Computing and Decision Sciences, International Journal of Computer Science and Applications, and various IEEE conferences.

Xinyu Wangمشاهده پروفایل

استادیار

• Programming Languages
• Formal Methods
• Software Engineering
• +۷ مورد دیگر

Xinyu Wang is an assistant professor in the Computer Science and Engineering Department at the University of Michigan, Ann Arbor. Their research focuses on program synthesis, formal methods, programming languages, and AI-assisted programming. They previously worked as a researcher at Microsoft and earned their PhD from the University of Texas at Austin. Current affiliation: University of Michigan (College of Engineering) Education: PhD in Computer Science from University of Texas at Austin Research interests center on AI-assisted programming and program synthesis, combining formal methods, programming languages, AI/ML, and human-centered computing. Key areas include optimizing database queries, neuro-symbolic automation, and synthesis-aided software systems for diverse domains. Recent publications span SQL optimization (2025–2024), web automation (2023–2022), and synthesis algorithms (2021–2020). Work addresses scalability, domain-specific insights, and formal reasoning techniques for complex workloads. Scientific awards include: Distinguished Paper Award at OOPSLA 2024 Best Paper Nomination at VLDB 2024 Best Paper Honorable Mention at UIST 2022 Distinguished Paper Award at OOPSLA 2020 Advises PhD students and postdoctoral fellows in program synthesis and AI-assisted systems. Serves on program committees for PLDI, POPL, and OOPSLA. Develops synthesis-aided tools for database optimization, web automation, and computational science.

Vincent Griponمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Few-Shot Learning
• +۸ مورد دیگر

Vincent Gripon is a Full Professor at IMT Atlantique, France's leading engineering institutions. He leads the BRAIn team within Lab-STICC (CNRS UMR 6285), focusing on Artificial Intelligence intersections with Deep Learning, Signal Processing, and Neuroimaging. IMT Atlantique Lab-STICC (CNRS UMR 6285) Mathematical and Electrical Engineering (MEE) Department Research Interests span Artificial Intelligence with specialization in: Few-Shot Learning Neural Network Pruning & Quantization Brain-Computer Interfaces Graph Signal Processing Thrifty AI for Edge Computing Geometry-Driven Model Optimization Selected Publications demonstrate his focus on efficient AI systems through: Training-free adaptation techniques for large models Quantization-aware hardware design Manifold intrusion prevention methods Neuroimaging data analysis frameworks Honors & Distinctions : CVPR 2025 publication (ProKeR) AMD Open Hardware Competition winner (2023) DCASE 2023 Task 5 Jury Award

Maria Potop-Butucaruمشاهده پروفایل

استاد

• Distributed Systems
• Blockchain Technology
• Distributed Algorithms
• +۱۰ مورد دیگر

Maria Potop-Butucaru is a Full Professor at Sorbonne Université, affiliated with the Laboratoire d'Informatique de Paris 6 (LIP6) in the NPA team. Her research focuses on distributed systems, blockchain technologies, fault tolerance, and self-stabilizing algorithms. Her research interests include distributed systems, blockchain technology, distributed algorithms, fault tolerance, self-stabilizing systems, mobile robotics, peer-to-peer networks, wireless body area networks (WBAN), federated learning, cross-chain interoperability, formal verification, and consensus protocols. She investigates the theoretical and practical aspects of dynamic and mobile distributed systems, aiming to design robust, self-organizing, and secure protocols. Her recent publications (2023–2025) reveal a strong focus on blockchain scalability, federated learning security, gossip learning vulnerabilities, and distributed algorithms for time-varying graphs. Key themes include Byzantine-resilient learning, hub-based topologies, data poisoning attacks, cross-chain swaps, and payment channel networks. Her work combines theoretical analysis with practical implementations in emerging decentralized systems. She has supervised numerous PhD students, including Gabriel Fontes Rebello, Pierre Civit, Léonard Lys, Yackolley Amoussou-Guenou, and Laure Millet, covering topics such as blockchain scalability, secure inter-blockchain exchanges, and formal verification of robot protocols.

Aymeric Fromherzمشاهده پروفایل

پژوهشگر

• Formal Methods
• Programming Languages
• Computer Security
• +۹ مورد دیگر

Aymeric Fromherz is a researcher at Inria Paris, affiliated with the Prosecco Team, focusing on formal methods for secure systems. His work bridges programming languages, computer security, and computational law, with significant contributions in verified systems and cryptographic software. Research Interests: His research spans formal verification, programming languages (especially Rust), dependent types, separation logic, and the application of formal methods to legal computation via the Catala language. He develops high-assurance software with a focus on memory safety, concurrency, and correctness. Publications Trends: His recent publications (2020–2025) reflect a strong trend toward verified systems: from memory allocators (StarMalloc) and cryptographic primitives (HACL×N, EverCrypt) to legal verification (CUTECat, Catala). These works emphasize correctness, performance, and practical deployment, often in top-tier venues like POPL, ICFP, OOPSLA, and S&P. Scientific Awards: ACM SIGSAC Dissertation Award A.G. Milnes Dissertation Award Distinguished Artifact Award (ESOP 2025) Best Tool Paper Award (ESOP 2024) Spotlight Paper at ICLR 2021 Advising and Grants: He collaborates closely with researchers across institutions, mentoring students and co-developing tools and frameworks. His involvement in the Everest Project indicates long-term, large-scale funding for high-assurance software. He has contributed to grant-funded efforts in verified cryptography and formal legal systems. Labs and Teams: He is a core member of the Prosecco team at Inria, which specializes in computer security and formal methods. He was previously part of the Everest Project at Carnegie Mellon University, a multi-institution effort to build verified, industrial-grade cryptographic software.

• Constraint Programming
• Artificial Intelligence
• Combinatorial Optimization
• +۷ مورد دیگر

Jean-Charles Régin is a full Professor at Université Côte d'Azur, affiliated with the I3S Laboratory, where he leads the Constraints and Applications (C&A) research team. His work is central to the 3IA Côte d’Azur initiative, focusing on Decision Intelligence and explainable AI under real-world constraints. His research interests include: Constraint Programming (CP) Global Constraints and Efficient Algorithms Multi-valued Decision Diagrams (MDD) Parallel and Distributed Constraint Programming Statistical Constraints (Sampling, Markov Chains, Spread, Deviation) Modeling and Solving Real-World Problems (Bin Packing, TSP, Car Sequencing) Over-Constrained and Multi-Objective Optimization Régin is renowned for his theoretical and practical contributions to Constraint Programming. His work shows a consistent trend in enhancing the scalability and expressiveness of CP systems, integrating machine learning-inspired methods, and applying CP to industrial problems. He has pioneered algorithms such as the one for the alldifferent constraint, widely used in CP solvers. His scientific recognition includes: Research Excellence Award, Association for Constraint Programming (ACP), 2013 Jean-Charles Régin leads the C&A team at I3S and contributes to the 3IA Côte d’Azur chair on Decision Intelligence. He has advised numerous researchers and students in the field, though specific names are not listed in the provided text. His work often involves interdisciplinary collaboration and the development of practical tools for optimization under uncertainty. The C&A team under his leadership focuses on advancing constraint-based reasoning, explainable decision-making, and integration with data-driven methods to solve complex industrial and societal challenges.

Goran Frehseمشاهده پروفایل

استاد

• Cyber-physical systems
• Hybrid systems
• Formal verification
• +۷ مورد دیگر

Goran Frehse is a Professor at ENSTA Paris where he leads the Semantics of Hybrid Systems (SSH) team and serves as Director of the Computer Science and Systems Engineering Unit (U2IS). His work bridges theoretical computer science with practical engineering applications, focusing on verification methods for complex cyber-physical systems. Dr. Frehse's research interests center on the safety verification of cyber-physical systems, particularly those incorporating artificial intelligence. His expertise spans formal methods for hybrid systems (combining discrete events with continuous dynamics described by differential equations), model checking , reachability analysis , statistical verification techniques , and safe reinforcement learning . His work addresses critical challenges in verifying systems where traditional testing approaches are insufficient due to complexity and safety requirements. Analysis of his recent publications (2021-2024) reveals a growing emphasis on integrating machine learning techniques with formal verification methods. His research increasingly focuses on data-driven reachability analysis , verification of AI-based control systems , and explainable AI safety , reflecting the field's evolution toward addressing the verification challenges posed by modern AI-integrated systems. His work maintains strong connections to practical applications while advancing theoretical foundations. As a research advisor, Dr. Frehse has supervised multiple PhD students including Abdelmouaiz Tebjou, Gwendal Priser, and David Brellmann. His research has been supported through significant projects such as IPL Modeliscale (2017-2021), UnCoVerCPS (2015-2018), and industrial collaborations including a project with DENSO Automotive Deutschland focused on control design verification for complex systems. Dr. Frehse leads the Semantics of Hybrid Systems research team within U2IS, which develops foundational methods and practical tools for system verification. His team's work has direct applications in safety-critical domains including autonomous vehicles, industrial robotics, and defense systems, where formal guarantees of system behavior are essential.

Florence d'Alché-Bucمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Bioinformatics
• +۶ مورد دیگر

Florence d'Alché-Buc serves as Professor at Telecom Paris (Institut Polytechnique de Paris) and holds a Simons chair at Isaac Newton Institute, Cambridge (May-June 2025). She leads the Signal, Statistics and Machine Learning Research Team (S2A) within LTCI laboratory's Image, Data and Signal Department, and co-organizes the 2025 thematic programme 'Representing, Calibrating and leveraging predictive uncertainty' at Isaac Newton Institute. Her research centers on Machine Learning & Artificial Intelligence with applications in bioinformatics, medical domains, and industrial settings. Key subdomains include (Operator-valued) Kernel Methods, Structured Output Prediction, Complex data analysis, Reliable Machine Learning, and Dynamical Systems Modeling. Her work emphasizes robust theoretical frameworks for real-world deployment, particularly in safety-critical applications requiring interpretability and uncertainty quantification. Recent publications (2019-2024) reveal consistent innovation in kernel-based structured prediction, sketching techniques for scalability, and graph learning with optimal transport. She bridges theoretical advances with industrial applications through collaborations with Airbus, ENGIE, SAFRAN and others, focusing on efficient algorithms for complex data while maintaining rigorous statistical foundations. Scientific recognition includes: Simons chair at Isaac Newton Institute (2025) Ellis Fellow and Board member overseeing European AI PhD programmes She advises doctoral candidates like Jayneel Parekh and leads major initiatives including the Télécom Paris Chair on Data Science & AI (2019-2023) funded by industrial partners, and the ELIAS project (European Lighthouse on AI & Sustainability). Her service includes senior editorial roles at IEEE TPAMI and JMLR, plus program leadership at NeurIPS and ICML. As LTCI laboratory member and occasional CMAP collaborator at Ecole Polytechnique, she drives cross-institutional research through the S2A team while co-organizing international workshops on frugal AI and kernel methods.

Rajeev Alurمشاهده پروفایل

استاد

• Formal Methods
• Cyber-Physical Systems
• Programming Languages
• +۷ مورد دیگر

Overview Rajeev Alur is the Zisman Family Professor and Director of the ASSET Center for Trustworthy AI at the University of Pennsylvania . His career spans groundbreaking work in formal methods, cyber-physical systems, and programming languages. PhD from Stanford University (1991) Bachelor's degree from IIT Kanpur (1987) Research Interests His research focuses on ensuring safety and trustworthiness in AI systems, particularly autonomous systems like self-driving cars and medical devices. Key areas include: Neurosymbolic integration Temporal logic in reinforcement learning Verification of neural network controllers Trustworthy machine learning Extending formal methods to modern AI Recent Publications Rajeev's recent work (2023-2025) emphasizes compositional verification, neurosymbolic learning, and secure microservice architectures. Notable trends include: Combining formal specifications with deep learning Scalable verification techniques for neural networks Relational query synthesis Application to autonomous systems with neural perception Chordal sparsity models for efficient analysis Scientific Recognition Alonzo Church Award (2016) Knuth Prize (2024) EATCS Award (2025) ACM/IEEE Fellow Advising & Grants He has advised 30+ PhD students now in academia (Georgia Tech, UCSD, etc.) and industry. As lead PI of the NSF Expeditions project ExCAPE , he drove program synthesis research. Currently serves as General Chair for FLoC 2026 . Labs & Teams Directs the ASSET Center and collaborates with Penn's PRECISE Center and PL Club . His work integrates with industry verification tools at Amazon, Microsoft, and Google.

Sabine Barratمشاهده پروفایل

استادیار

• Image Analysis
• Image Indexing
• Image Retrieval
• +۴ مورد دیگر

Sabine Barrat is an Assistant Professor at the University of Tours, affiliated with the University Institute of Technology of Tours (IUT) and the Fundamental and Applied Computer Science Laboratory (LIFAT). She teaches courses in website design and databases. Dr. Barrat obtained her PhD in Computer Science from Nancy 2 University in 2009, focusing on probabilistic models for image recognition. She completed a JSPS Postdoctoral Fellowship at Osaka Prefecture University and served as Vice-President for Digital Systems at the University of Tours (2016-2020). Her research explores image analysis, indexing/retrieval systems, automatic annotation, and document processing. Core methodologies include Bayesian networks, feature indexing structures, and hybrid visual-semantic modeling. Recent publications emphasize scalable image retrieval systems and document classification techniques. She received the JSPS Postdoctoral Fellowship for her work on character recognition. Dr. Barrat collaborates with the RFAI research team at LIFAT laboratory, focusing on pattern recognition and intelligent indexing systems.