جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Artificial Intelligence
• Bioinformatics
• +۹ مورد دیگر

Florence d'Alché-Buc is a Professor at Télécom Paris (Institut Polytechnique de Paris), holding an Isaac Newton Institute Simons Chair (2025) and leading the Data Science and Artificial Intelligence for Digitalized Industry & Services (DSAI) Chair. She heads the Image, Data, and Signal Department and is part of the Signal, Statistics, and Learning (S2A) team at the LTCI laboratory. Her research focuses on machine learning, bioinformatics, and industrial applications, emphasizing kernel methods, structured prediction, and reliable AI. Education: Previously a professor at Université d’Evry and deputy director of the IBISC lab. Co-director of the Paris-Saclay Data Science Master and creator of specialized AI programs (e.g., Certificate of Specialized Studies in AI). Research highlights include contributions to operator-valued kernel methods, graph prediction, and frugal AI. She actively collaborates with institutions like Inria, École Polytechnique, and industry partners (Airbus, Engie, etc.). Notable roles: Scientific director of Digicosme Labex, Ellis Fellow, and board member of IVADO (Montreal). Her recent work addresses AI explainability, robustness, and sustainability, including projects on interpretable networks and energy-efficient models.

Abderrahim Benslimaneمشاهده پروفایل

استاد

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

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.

François Baccelliمشاهده پروفایل

استاد مهمان

• Applied Probability
• Stochastic Geometry
• Wireless Networks
• +۴ مورد دیگر

François Baccelli is a Research Director at INRIA Paris and Visiting Professor at Télécom Paris, leading the ERC Advanced Grant NEMO project on network mathematics. He holds a Doctor Honoris Causa from Heriot-Watt University and is a Member of the French Academy of Sciences. His career spans key roles including head of the Simons Center on Communication, Information and Network Mathematics (2012–2019), and faculty positions at École Polytechnique (1991–2003) and the University of Texas at Austin (2012–2021) as Simons Math+ECE Chair. Education: PhD in Applied Mathematics from Université Paris-Sud (1983), advised by Erol Gelenbe. Graduate of Télécom Paris (1977). Research Interests: Baccelli’s work bridges applied probability, stochastic geometry, and network dynamics with applications to telecommunications. He pioneered spatial stochastic networks, stochastic geometry modeling of wireless systems, and max-plus algebra for discrete-event systems. His contributions include foundational theories on queuing networks, Palm probabilities, and latency-constrained system design. Awards & Recognition: 2024 Blackwell Prize (INFORMS Applied Probability Society). 2014 ACM Sigmetrics Achievement Award. 2014 IEEE Stephen O. Rice and Leonard G. Abraham Prizes. Collaborations: Co-founder of the LINCS joint laboratory (INRIA/IMT/Nokia Bell Labs/Sorbonne/SystemX), fostering interdisciplinary research in communications and networks. Supervised numerous graduate students who now lead academic and industrial research globally. Labs & Teams: LINCS laboratory and ERC NEMO project, focused on network mathematics and large-scale system dynamics.

Petros Eliaمشاهده پروفایل

استاد

• Information Theory
• Coding Theory
• Caching
• +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.

Taein Kwonمشاهده پروفایل

پژوهشگر ارشد

• Egocentric Vision
• Action Recognition
• Contextual AI
• +۶ مورد دیگر

Taein Kwon is a postdoctoral research fellow at the Visual Geometry Group (VGG) within the Department of Engineering Science at the University of Oxford, working under Prof. Andrew Zisserman. Previously, he completed his PhD at ETH Zurich under Prof. Marc Pollefeys and earned master's and bachelor's degrees from UCLA and Yonsei University, respectively. His educational background includes: Bachelor's in Electrical Engineering from Yonsei University, Seoul, Korea Master's degree from UCLA PhD from ETH Zurich (defended July 2024) His research spans Egocentric Vision, Action Recognition, Hand-object Interaction, Video Understanding, AR/VR, and Multi-modal Learning, with emphasis on first-person perspective analysis for AI assistants and human-computer interaction. His work integrates 3D reconstruction, pose estimation, and multimodal signals to model complex human activities and physical interactions. Analysis of his 2021-2025 publications reveals a consistent focus on egocentric vision datasets (H2O, HoloAssist, EgoPressure) and novel frameworks for hand-object interaction, action recognition, and gesture understanding. His research demonstrates strong interdisciplinary connections between computer vision, robotics, and human-centered AI, with increasing emphasis on pressure sensing, co-speech gestures, and cross-modal alignment. His scientific recognition includes: CVPR Egovis 2022/2023 Distinguished Paper Award for HoloAssist (July 2024) SNSF Postdoc.Mobility fellowship (May 2024) He actively mentors students on egocentric vision projects, supervising master's theses, semester projects, and collaboration initiatives leading to publications at top conferences. His research is supported by the SNSF fellowship and industry collaborations with Meta Reality Labs and Microsoft Research. As part of Oxford's Visual Geometry Group, he contributes to cutting-edge computer vision research while maintaining strong ties with ETH Zurich's computer vision community through ongoing collaborations and dataset development efforts.

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

Christophe Charrierمشاهده پروفایل

استاد

• Digital Image Forensics
• Video Forensics
• Biometrics
• +۷ مورد دیگر

Christophe Charrier is a Full Professor in Forensics and AI at Université de Caen Normandie, affiliated with GREYC UMR CNRS 6072 and IUT Grand Ouest Normandie's Multimedia and Internet Department (Dept. MMI). He obtained his PhD in Computer Science from Université Jean Monnet (Saint-Etienne) in 1998, followed by an HDR (Habilitation à Diriger des Recherches) in 2011 from Université de Caen Normandie. His academic journey includes roles as a Postdoctoral Researcher at Université Laval (1998-2001), Associate Professor at IUT Saint-Lô (2001), and Visiting Scholar/Professor positions at University of Texas at Austin (2008) and University of Sherbrooke (2009-2011). His research focuses on Digital Image and Video Forensics (e.g., deepfake detection), Image/Video Quality Assessment , Computational Vision , and Biometrics (fingerprint quality, template update, presentation attack detection). He leads the SAFE research group since 2016 and collaborates with the E-payment & Biometrics team at GREYC. His work integrates machine learning for quality metrics, biometric system evaluation, and forensic analysis. Recent publications highlight advancements in deepfake detection , 3D mesh quality assessment , and biometric security . Articles span journals like Intelligent Service Robotics (2024), IEEE Access (2024), and conferences such as CORESA (2024) and Cyberworlds (2023-2024). His studies on fingerprint systems, behavioral biometrics, and environmental impacts on data quality underscore his interdisciplinary approach. Scientific Awards : Best PhD Paper Award (ASONAM 2022) Best Full Paper Award (CW2022) He has mentored 14 PhD students since 2003, including notable alumni like Xinwei Liu (Zhejiang Wanli University) and Antoine Cabana (ALTEN, Toulouse). His projects span biometric certification, latent space manipulation, and 3D mesh evaluation, often in collaboration with institutions in Canada, Norway, and Morocco.

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.

Jamal Atifمشاهده پروفایل

استاد

• Artificial Intelligence
• Responsible AI
• Privacy Preservation in Machine Learning
• +۴ مورد دیگر

Jamal Atif is a Professor at Paris-Dauphine University and holds multiple significant leadership positions including Project Manager for 'Data Science and Artificial Intelligence' at the Institute of Information Sciences and their Interactions (INS2I) of the CNRS, Deputy Scientific Director of 3IA PRAIRIE, Head of the MILES team/project at LAMSADE (UMR CNRS-Université Paris-Dauphine), Co-leader of the Transverse Artificial Intelligence Program at PSL University, and Director of the Dauphine Numérique program. Professor Atif's primary research focuses on the foundations of responsible artificial intelligence, with specific expertise in privacy preservation in machine learning, robustness of deep learning algorithms to malicious attacks, causality, and explainability. His work bridges theoretical foundations with practical applications in security and reliability of AI systems. He has developed innovative approaches to address adversarial vulnerabilities in machine learning models and has made significant contributions to privacy-preserving techniques in data analysis. His publication record demonstrates a consistent focus on robust and trustworthy AI systems, with recent work exploring differential privacy in clustering, adversarial robustness, and explainable AI. The research spans theoretical foundations in logic and knowledge representation to practical applications in finance, healthcare, and computer vision. His publications appear in top-tier venues including Machine Learning journal, Neural Information Processing Systems, and International Joint Conferences on Artificial Intelligence. Scientific Awards: Recipient of two awards from the North American Society of Radiology for his thesis work Professor Atif has co-supervised or is currently supervising around fifteen doctoral students, demonstrating his commitment to mentoring the next generation of AI researchers. His leadership extends to directing major institutional programs including Dauphine Numérique and the Transverse Artificial Intelligence Program at PSL University, where he shapes strategic research directions in AI. He leads the MILES team/project at LAMSADE, which focuses on foundational aspects of machine learning and artificial intelligence. The team's research spans theoretical aspects of learning algorithms to practical applications requiring robust and reliable AI systems, with particular emphasis on security and privacy considerations in modern machine learning deployments.

Andrea Simonettoمشاهده پروفایل

استاد پژوهش

• Optimization
• Control Theory
• Quantum Computing
• +۵ مورد دیگر

Andrea Simonetto is a Research Professor at the Applied Mathematics Unit (UMA) , ENSTA Paris, Institut Polytechnique de Paris. His work spans optimization, control theory, and learning algorithms for large-scale and streaming data , with applications in smart grids, intelligent transportation, personalized health, and quantum computing. Current research focuses on online algorithms for time-varying optimization , personalized optimization for cyber-physical systems , and variational quantum algorithms . Past contributions include theoretical and algorithmic advances in convex/non-convex optimization, distributed optimization (robotic networks, smart grids), and signal processing for sparse reconstructions and parallel computing in particle filtering. Key application domains include renewable energy integration , quantum state preparation , and human-in-the-loop control systems . His research is published in journals like ACM Transactions on Quantum Computing , IEEE Control Systems Letters , and Automatica .

Rémi Flamaryمشاهده پروفایل

استاد

• Machine Learning
• Optimal Transport
• Domain Adaptation
• +۷ مورد دیگر

Rémi Flamary is a Professor in the Applied Mathematics department at École Polytechnique, France, and a member of the CMAP Laboratory. Previously, he held an Associate Professor position at Université Côte d'Azur in the Department of Electronics and Lagrange Laboratory. He completed his PhD at Rouen University under Alain Rakotomamonjy, focusing on statistical signal processing and optimization. His research interests span machine learning, optimal transport, domain adaptation, and their applications in biomedical engineering, energy, and remote sensing. He leads the development of the POT (Python Optimal Transport) library and contributes to projects like SKADA for domain adaptation. Education: PhD in Applied Mathematics, Rouen University (LITIS Laboratory) Research Focus: Flamary's work emphasizes leveraging optimal transport theory for machine learning tasks such as graph prediction, signal normalization, and cross-domain adaptation. His contributions include novel algorithms for unbalanced transport, semi-relaxed Gromov-Wasserstein distances, and end-to-end graph generation frameworks. He actively collaborates on applications in neuroscience, astronomy, and energy systems. Professional Activities: He has presented at NeurIPS and other top conferences, supervised PhD students like Cédric Vincent-Cuaz, and contributed to open-source software. His teaching includes courses on signal processing and machine learning at École Polytechnique.

Ammar Mianمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Machine Learning
• Riemannian Geometry
• +۴ مورد دیگر

Ammar Mian is an Associate Professor at Université Savoie Mont Blanc, affiliated with the LISTIC lab and Polytech Annecy-Chambéry. He holds a PhD from CentraleSupélec (2016-2019) and conducted postdoctoral research at Aalto University (2019-2020). His research focuses on statistical signal processing, machine learning, and Riemannian geometry with applications in remote sensing and frugal computations. He leads the Qanat project, an experiment tracking tool for reproducible research. Research interests include covariance-based methods for SAR image analysis, robust detection algorithms for sonar and GPR systems, and optimization on Riemannian manifolds. His work emphasizes reproducibility in ML and efficient computational techniques for resource-constrained environments. Key contributions include real-time SAR time-series change detection, robust classification using second-order deep learning models, and novel methods for handling missing data in EEG signals. His recent articles (2023-2025) explore reproducibility frameworks, GPR-based object classification, and Riemannian geometry applications. No awards listed, but maintains active collaborations through LISTIC and industry partnerships. Advises students via internship programs (e.g., Federated ML energy cost analysis). Lab work involves developing open-source tools like Qanat for experiment management and reproducibility.

Paul Thevenonمشاهده پروفایل

استادیار

• GNSS Signal Processing
• Navigation Systems
• Satellite Communication
• +۳ مورد دیگر

Dr. Paul Thevenon is an Assistant Professor at ENAC (National Civil Aviation School) in Toulouse, France, since July 2013. He holds a Habilitation à Diriger les Recherches (2023) and focuses on GNSS signal processing, integrity monitoring, and hybrid sensor systems. His research integrates advanced algorithms for cycle slip detection, multipath mitigation, and hybrid navigation filters using 5G/ LTE signals. Education: Bachelor's in Electronic Engineering from École Centrale de Lille (2004) Master's in Space Telecommunications from ISAE (2007) PhD in Signal Processing from ENAC (2010) Research Themes: GNSS robustness against malicious signals (e.g., evil waveforms) Multi-receiver systems for RTK positioning enhancement Integration of non-GNSS signals (5G, LTE) for hybrid navigation Factor graph approaches for carrier phase processing Awards: 2023: Habilitation à Diriger les Recherches Technical Contributions: Developed CNN-based multipath detection systems Advanced RTK ambiguity resolution via multi-rover arrays Validated TDCP measurements in low-cost PPP-IMU filters Labs/Teams: Core member of the SIGNAV lab, specializing in signal processing and navigation systems.

Djamal Zeghlacheمشاهده پروفایل

استاد

• Network Virtualization
• Cloud Computing
• Software-Defined Networking
• +۵ مورد دیگر

Djamal Zeghlache is a Professor at Telecom SudParis (now part of Institut Polytechnique de Paris), where he leads research in network virtualization, cloud computing, and software-defined networking. His work spans multiple domains including wireless communications, network security, and AI applications in networking systems. Affiliated with the SAMOVAR laboratory (Services and Architectures for Multimedia and Ubiquitous Access to Resources), he has established himself as a prominent researcher in network and service management. Professor Zeghlache's research interests focus on the intersection of networking and artificial intelligence, with particular emphasis on network virtualization, cloud computing, and software-defined networking. His work addresses critical challenges in network function virtualization, resource allocation, and optimization in distributed systems. In recent years, he has expanded his research to include AI-driven approaches for network management, healthcare monitoring using wireless technologies, and optical network control. His publications demonstrate a consistent trajectory from traditional networking challenges to more complex, AI-integrated solutions for modern network infrastructures. Analysis of his recent publications (2023-2025) reveals a strong trend toward integrating machine learning, particularly deep reinforcement learning and graph neural networks, into network management systems. His work spans multiple application areas including healthcare monitoring (using RF-based techniques for vital sign detection), transportation systems (bike-sharing optimization), and optical networking (disaggregated control frameworks). The interdisciplinary nature of his research connects computer networking with biomedical engineering, urban mobility, and formal methods for system verification. Professor Zeghlache has supervised numerous PhD students and collaborated extensively with researchers across Europe and internationally. His work has resulted in significant contributions to network virtualization, cloud resource management, and network security. He has been involved in multiple research projects focusing on next-generation networking technologies, including 5G, network slicing, and cloud-native network functions.

• Medical Imaging
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Diana Mateus is a Researcher at Centrale Nantes, affiliated with the LS2N Laboratory. She holds a bachelor's in Electronic Engineering from the University of Javeriana (Colombia), a Master's in Control and Automation from Paul Sabatier University (France), and a PhD in Computer Vision from INRIA Grenoble. After postdoctoral work and eight years as an associate researcher at Technical University of Munich and Helmholtz Zentrum (Germany), she returned to France to join Centrale Nantes via the Connect Talent Program, securing a starting grant for interdisciplinary research in medical image analysis and machine learning. Her research focuses on Milcom —a project integrating multimodal imaging (MRI, CT, ultrasound, PET) with machine learning to advance computational medicine. Key objectives include computer-aided diagnosis, disease knowledge advancement, and improving imaging quality. Collaborations include the hospital's Nuclear Medicine Department, focusing on multiple myeloma, and companies Hera-Mi and Keosys for breast cancer research. She supervises six PhD students and two postdocs, emphasizing synergies between engineering and healthcare. At Centrale Nantes, she benefits from dynamic institutional support, quality students, and complementary team skills. Her lab recently acquired a research-dedicated ultrasound machine to develop machine learning-driven imaging optimization. Living in Nantes, she values its family-friendly environment and proximity to academic and medical networks.