Find Academic Supervisors

Tijani CHAHEDView profile

Professor

• Network Optimization
• Edge Computing
• Machine Learning in Networks
• +4 more

Prof. Tijani CHAHED is a Professor at Telecom SudParis, part of Université Paris-Saclay, affiliated with the SAMOVAR laboratory and the NeSS research group. His work focuses on network optimization, edge computing, machine learning applications in telecommunications, and game-theoretical frameworks for distributed systems. He holds a position in the Department of Computer Science and Telecommunications. His research spans resource allocation in 5G/6G networks, energy efficiency strategies for mobile infrastructure, reinforcement learning for dynamic systems, and coalitional game theory for multi-agent systems. Key contributions include optimization of cache allocation in edge computing, latency-critical traffic management (URLLC), and strategic investment models for distributed computing infrastructures. Selected articles highlight advances in edge computing resource management, metaverse data transport over 5G, and energy-efficient sleep mode control for base stations. His work often intersects with industrial applications in green networks and smart grid integration for mobile infrastructure. Collaborations involve institutions like École Polytechnique, INRIA, and industry partners in telecommunications. Current projects include 6G network architectures, metaverse-enabled edge services, and decentralized resource allocation frameworks. Labs/Teams: SAMOVAR Lab (Signal and Media Access Networks, Optical and Radio Networks), NeSS Group (Networked Systems and Services).

Clément FERNANDESView profile

Lecturer

• Hidden Markov Models
• Data Segmentation
• Image Segmentation
• +3 more

Clément FERNANDES is a Lecturer at Telecom SudParis, part of the SAMOVAR laboratory under Institut Polytechnique de Paris. His research focuses on statistical modeling and unsupervised learning techniques, particularly applying hidden Markov chains to data and image segmentation. He holds a PhD in Discrete Mathematics from Institut Polytechnique de Paris (2022), where his thesis explored Triplet Markov Chains in image segmentation. His recent work includes advancing non-stationary data segmentation methods and developing forecasting models using Gaussian Markov frameworks. Though no explicit grants or awards are mentioned, his publications reflect contributions to approximate reasoning and applied statistics in engineering contexts. He is affiliated with the SAMOVAR laboratory and maintains an active publication record in top-tier journals like International Journal of Approximate Reasoning .

• Nonparametric Statistics
• High-Dimensional Statistics
• Inverse Problems
• +3 more

Cristina Butucea is a Professor of Statistics and Machine Learning at ENSAE-CREST, Institut Polytechnique de Paris . She was nominated an IMS Fellow (2019) for her contributions to nonparametric and high-dimensional statistics. She has co-organized major conferences like Fréjus 2018 , Luminy 2019-2020 , and Oberwolfach 2021 , and serves as Associate Editor for ALEA . Fields of interest: Nonparametric statistics, quantum statistics, differential privacy, inverse problems, machine learning. Awards: IMS Fellowship, conference organization leadership. Research trends: Focuses on optimal estimation under privacy constraints, quantum state reconstruction, high-dimensional inference, and adaptive nonparametric methods. Email: Cristina.Butucea@ensea.fr | Cristina.Butucea@ip-paris.fr

Taein KwonView profile

Research Fellow

• Egocentric Vision
• Action Recognition
• Contextual AI
• +6 more

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.

Chadi BarakatView profile

Researcher

• Internet Measurement
• Network Traffic Analysis
• Quality of Experience (QoE)
• +3 more

Chadi Barakat is a Senior Researcher (Directeur de Recherche) at Université Côte d'Azur's Inria research center, leading the DIANA project-team. He holds a PhD in Computer Science from University of Nice Sophia Antipolis (2001) and Habilitation (HDR) in 2009, with academic credentials from Lebanese University (1997) and French institutions. PhD: Computer Science (2001), University of Nice Sophia Antipolis HDR: Computer Science (2009), University of Nice Sophia Antipolis Master's: Computer Science (1998), University of Nice Sophia Antipolis BSc: Electrical & Electronics Engineering (1997), Lebanese University His research focuses on Internet measurement and traffic analysis , with significant contributions to Quality of Experience (QoE) modeling, 5G/ICN/SDN network architectures , and network performance evaluation . Recent articles highlight browser-based network monitoring, fidelity-aware network emulation, and ray tracing optimization for radio frequency mapping. He has supervised 12 PhD students to completion and currently directs the Academy of Excellence 'Networks, Information, and Digital Society' at Université Côte d'Azur. His work has received multiple best paper awards at CNSM, CloudNet, and SECON conferences, while serving as associate editor for Elsevier Computer Networks journal and active in ACM/IEEE conference committees. Director, Academy of Excellence 'Networks, Information, and Digital Society' (2025-present) Senior IEEE Member (2010) & ACM Senior Member (2018) General Co-Chair: ACM IMC 2022, ACM CoNEXT 2012 Guest Editor: IEEE JSAC special issue on Internet Sampling

Christophe CharrierView profile

Professor

• Digital Image Forensics
• Video Forensics
• Biometrics
• +7 more

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 ChristophidesView profile

Professor

• Databases
• Web Information Systems
• Big Data Processing
• +9 more

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.

Inbar FijalkowView profile

Professor

• Signal Processing
• Wireless Communications
• Optimization
• +3 more

Inbar Fijalkow is a Full Professor at the National School of Electronics and Computer Science (ENSEA) within CY Cergy Paris University. She is a member of the ETIS Research Unit (UMR 8051), focusing on signal processing for wireless communications, optimization, and machine learning applications. Her research bridges theoretical advancements with practical implementation in emerging communication systems. Education & Career: PhD in Signal Processing from TelecomParisTech (1993) Postdoctoral Fellow at Cornell University (1994–1995) Professor at ENSEA since 1999 Former Head of ETIS Research Unit (2004–2013) Research Interests: Signal processing for wireless communications Optimization techniques in massive MIMO and NOMA systems Machine learning applications in communication systems Nonlinear effects mitigation in high-power amplifiers Community & Awards: Member of CoNRS Section 7 (National Committee for Scientific Research) Chevalier de l’Ordre National du Mérite (2015) Founder of the CY Alliance Women in Science Prize (2017) Recent Projects: Active in ANR-funded initiatives (e.g., EcoBioH2, AI4code) and EU projects (e.g., PERSEUS). Her work emphasizes sustainability and AI-driven communication systems. Teaching: Teaches signal processing and wireless communications at ENSEA. Supervises PhD students and master’s theses in communication systems and signal processing.

• AI-enabled communications
• Green and energy-efficient communications
• Online optimization and machine learning
• +6 more

E. Veronica Belmega is a Full Professor at ESIEE Paris (Université Gustave Eiffel) and a researcher at the LIGM laboratory in Marne-la-Vallée, France. Previously, she served as an Associate Professor at ENSEA graduate school and Deputy Director of the ETIS laboratory in Cergy. She holds an Engineer Degree from the University Politehnica of Bucharest, M.Sc. and Ph.D. from Université Paris-Sud 11, and an HDR habilitation from Université de Cergy-Pontoise. Her research focuses on AI-driven communication systems, energy efficiency, online optimization, and cyber-physical security, with a strong emphasis on applications in smart grids and IoT. Her work spans securing wireless communications against adversarial attacks, optimizing resource allocation in cognitive radio networks, and leveraging machine learning for GNSS localization and MIMO systems. Notable contributions include game-theoretic frameworks for PMU deployment and energy-efficient NOMA systems. Belmega has received prestigious awards such as the 2021 CY Alliance Award and the L’Oréal-UNESCO fellowship, and serves as an Area Editor for IEEE Trans. on Machine Learning in Communications and Networking. Current projects include a CEA LETI postdoc position on AI localization and a PEPR 5G project. She actively contributes to special issues like the EURASIP JASP on sustainable wireless communications. Belmega’s advising includes PhD student S. Maleki, co-author of her 2024 IEEE SmartGridComm Best Paper Award-winning work.

Xavier Alameda-PinedaView profile

Researcher

• Machine Learning
• Computer Vision
• Audio Processing
• +7 more

Xavier Alameda-Pineda is a Research Director at Inria Grenoble Rhône-Alpes, where he leads the RobotLearn Team. He is affiliated with Université Grenoble Alpes and has been a key member of the Perception team. His work integrates machine learning, computer vision, and audio processing for scene understanding and human-robot interaction. Research Interests: His research lies at the intersection of multimodal machine learning and social behavior analysis. He focuses on developing algorithms for understanding human behavior in natural settings using audio-visual signals, with applications in robotics and AI companions. His work emphasizes real-world challenges such as noisy data, missing modalities, and dynamic environments. Publication Trends: His recent publications reflect a consistent focus on multimodal fusion, particularly combining vision and audio for social scene analysis. Themes include group behavior recognition, sound source separation, and cross-modal learning, often applied in robotics contexts. Scientific Awards: SIGMM Rising Star Award 2018 IEEE TMM Outstanding Associate Editor Award 2022 ACM TOMM Best Paper Award 2020 Best Paper Award, ACM MM 2015 Best Scientific Paper Award, ICPR 2016 Best Student Paper Award, IEEE WASPAA 2015 Outstanding Paper Award, ICMI 2011 Novel Technology Paper Award Finalist, IROS 2017 Advising and Grants: Xavier has mentored students and early-career researchers, evidenced by co-authored student papers. He coordinated the H2020 SPRING project on socially pertinent robots in gerontological healthcare and co-leads an AI chair on audio-visual perception for companion robots, indicating leadership in funded research initiatives. Labs and Teams: He is the leader of the RobotLearn Team at Inria and was previously part of the Perception team. He has also collaborated with the Multimodal and Human Understanding Group at the University of Trento.

Sylvain FaisanView profile

Assistant Professor

• Image Processing
• Machine Learning
• Geometry
• +6 more

Sylvain Faisan is a permanent Assistant Professor at ICube - MIV (University of Strasbourg, France). His research focuses on image processing, statistical modeling, and geometry, with applications in medical imaging and neuroscience. He works on advanced methodologies integrating machine learning and mathematical frameworks. Key Research Areas: Polarimetric image processing, retinal image registration, 3D statistical model comparison, topology-preserving image deformation, and fMRI brain mapping Technical Expertise: Bayesian inference, non-local means filtering, reversible jump MCMC algorithms, causal modeling, and constrained optimization His publications demonstrate interdisciplinary applications in optics, biomedical imaging, and computational anatomy. He contributes to developing algorithms that maintain physical admissibility and topological integrity in complex imaging problems.

Jamal AtifView profile

Professor

• Artificial Intelligence
• Responsible AI
• Privacy Preservation in Machine Learning
• +4 more

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.

Yohan PETETINView profile

Associate Professor

• Monte Carlo methods
• Bayesian filtering
• Hidden Markov models
• +5 more

Yohan PETETIN is an Associate Professor at Telecom SudParis (Institut polytechnique de Paris) in the CITI Department. His research focuses on Bayesian filtering, Monte Carlo methods, hidden Markov models, and multi-object tracking. He has authored over 20 peer-reviewed articles since 2011, with notable contributions in IEEE Transactions on Signal Processing and other top venues. His work bridges statistical signal processing with machine learning applications. PhD: Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles Markoviens (2013, Telecom SudParis) HDR: Generative models for time series data (2023, Institut polytechnique de Paris) Research interests emphasize sequential Monte Carlo algorithms, particle filtering optimizations, and deep learning integration for time-series analysis. Recent work explores expressivity comparisons between recurrent neural networks and hidden Markov models. Teaching includes courses on probabilistic graphical models, Bayesian filtering, and deep learning across undergraduate and graduate programs at Telecom SudParis and affiliated institutions.

Slim EssidView profile

Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +7 more

Slim Essid is a Full Professor at Télécom Paris, leading the Audio Data Analysis and Signal Processing (ADASP) group. He holds a Doctorat (Ph.D.) and Habilitation from Université Pierre et Marie Curie (UPMC). With 15+ years of research experience, he has advised 15 PhD graduates and currently co-advises 10 others. His work focuses on machine learning, signal processing, and multimodal systems, publishing over 150 peer-reviewed papers. He serves as a reviewer for top journals/conferences (e.g., IEEE Transactions) and research funding agencies. Education: State Engineering Degree, École Nationale d’Ingénieurs de Tunis (2001) M.Sc. (D.E.A.) in Digital Communication Systems, École Nationale Supérieure des Télécommunications, Paris (2002) Ph.D., Université Pierre et Marie Curie (2005) Habilitation (HDR), UPMC (2015) Research Interests: Multimodal learning, self-supervised representations, audio-visual segmentation, music structure analysis, domain generalization, and speech enhancement. Recent publications highlight innovations like TACO (training-free sound-prompted segmentation) and CLOUDS (domain-generalized semantic segmentation framework using foundation models). His work bridges audio processing with vision and language models, emphasizing unsupervised/zero-shot approaches. Key achievements include state-of-the-art methods in sound event detection, speaker diarization, and music segmentation. He collaborates with 14 post-docs and leads projects funded by French/EU agencies.

Nicholas EvansView profile

Professor

• Speaker Recognition
• Biometrics
• Anti-Spoofing
• +7 more

Nicholas Evans is a Professor in the Digital Security department at EURECOM, where he teaches mathematical methods for engineers and speech and audio processing. He has been affiliated with EURECOM since October 2007 and leads research in speaker recognition, anti-spoofing, and biometric security. Previously, he was a Professor at the University of Wales Swansea (2002–2006) and taught at the University of Avignon (2006–2007). Research Interests: His work centers on biometric security, particularly in detecting spoofing and deepfake attacks in automatic speaker verification systems. Key areas include presentation attack detection, privacy-preserving voice technologies, and robust speech processing under real-world conditions. He actively contributes to advancing anti-spoofing countermeasures through large-scale datasets and challenge evaluations. The recent publications reflect a strong trend in developing and evaluating spoofing detection mechanisms, with a focus on real-world applicability, adversarial robustness, and multimodal analysis. His work spans from foundational feature engineering (e.g., Constant Q cepstral coefficients) to large-scale datasets like SpoofCeleb and ASVspoof, influencing both academic research and practical security systems. NeurIPS 2023 Scholar Award for 'StressID: a multimodal dataset for stress identification' Best Paper Award at IBERSPEECH 2022 Best System Award at IberSpeech 2018 Multiple Best Paper Awards (2016, 2017) Student Paper Award at WIFS 2013 Elected to IEEE Speech and Language Technical Committee (2013) Nicholas Evans has supervised numerous students and early-career researchers, many of whom are co-authors on his publications. He leads significant research initiatives such as the ASVspoof and SpoofCeleb challenges, which are supported by collaborative grants and institutional funding. His lab focuses on developing secure, privacy-preserving speech technologies with applications in biometrics and cybersecurity. He is a key member of international research teams and contributes to major challenges in voice privacy and spoofing detection, including the Voice Privacy Challenge and BIOSIG. His work involves close collaboration with institutions across Europe and Asia, and he maintains active profiles on Google Scholar, ResearchGate, and IEEE Xplore.