Find Academic Supervisors

Renaud SeguierView profile

Professor

• Computer Vision
• Signal Processing
• Human-Computer Interaction
• +5 more

Renaud Seguier is a Professor in the Department of Electronics and Telecommunications at the Institute of Electronics and Telecommunications of Rennes, School of Engineering. His research spans computer vision, signal processing, and human-computer interaction with a focus on facial analysis and speech processing. His primary research interests include computer vision (specializing in micro-expression recognition, 3D facial modeling, and gaze estimation), signal processing (speech emotion recognition and source-filter decomposition), and affective computing (emotion detection from multimodal inputs). Key methodologies involve deep learning architectures like variational autoencoders, GANs, and specialized metric learning techniques. His recent publications demonstrate strong trends in multimodal emotion analysis combining audiovisual inputs, unsupervised learning for human activity recognition , and generative model applications for facial editing and deepfake detection. The work shows increasing sophistication in temporal modeling of facial dynamics and disentangled feature representations. As an academic advisor, he supervises numerous students including Jingting Li, Samir Sadok, and Mouath Aouayeb who frequently appear as first authors on publications. His collaborative network includes Catherine Soladie, Simon Leglaive, and Amine Kacete across multiple institutions. His laboratory work centers on the 3D facial analysis pipeline involving texture reconstruction, motion denoising, and micro-expression spotting systems. Current projects integrate RGB-D sensing with deep learning for unconstrained environments, particularly focusing on health diagnostics from facial cues and real-time gaze estimation.

Nicolas DurandView profile

Professor

• Metaheuristics
• Combinatorial Optimization
• Interval Methods
• +5 more

Nicolas Durand is a Professor at the National School of Civil Aviation (ENAC), specializing in Air Traffic Management through the integration of Metaheuristics , Combinatorial Optimization , and Interval Methods . His work focuses on hybridizing Evolutionary Algorithms with Machine Learning to enhance Conflict Resolution and Trajectory Prediction in aviation systems. His research spans Unmanned Aerial Systems (UAS) , ADS-B Data Analysis , and 3D Collision Avoidance . Recent projects include Behavior Cloning for conformal automation and Median Regression for lateral deconfliction. Collaborations with institutions like ONERA and ISAE-SUPAERO highlight his interdisciplinary approach. Publications reveal a trend toward Explainable AI in ATM, with applications in Dynamic Conflict Resolution , Mass Estimation , and UAS Integration . His work bridges theoretical optimization (e.g., Global Optimization , Interval CP ) with operational needs, emphasizing Controller Expertise and Uncertainty Quantification .

David GianazzaView profile

Researcher

• Air Traffic Management
• Machine Learning
• Aircraft Trajectory Prediction
• +3 more

David Gianazza is a Researcher at the National School of Civil Aviation (ENAC) , focusing on air traffic management and trajectory prediction. His work combines machine learning and operational research to address challenges in aircraft conflict resolution, airspace configuration, and performance modeling. Specializes in neural networks and metaheuristic optimization Key research areas: air traffic complexity , 3D trajectory planning , and workload modeling His 15 most recent articles (2024-2012) demonstrate expertise in ADS-B data analysis, mass/thrust estimation, and hybrid deterministic-stochastic search algorithms. Collaborations include researchers like Nicolas Durand , Richard Alligier , and Jean-Marc Alliot across institutions in France, Netherlands, and USA. Publications highlight applications of ant colony optimization , A* algorithms , and gradient boosting machines to air traffic problems. His work bridges theoretical computer science and practical ATM solutions , with emphasis on safety and efficiency.

Thierry KleinView profile

Professor

• Statistics
• Sensitivity analysis
• Machine learning
• +7 more

Thierry Klein is a Professor of Statistics at École Nationale de l'Aviation Civile (ENAC) and a member of the Toulouse Institute of Mathematics. His research spans statistical methodology with applications in diverse fields including aviation safety, environmental science, and computational mathematics. Dr. Klein's research focuses on advanced statistical methods, particularly in sensitivity analysis, probability theory, and machine learning. His work on Sobol indices has contributed significantly to variance-based sensitivity analysis, while his research on Wasserstein spaces has advanced the understanding of probability distributions in metric spaces. He has developed innovative methods for Gaussian process regression and has applied statistical techniques to problems in aviation safety, coastal flooding prediction, and paleoenvironmental reconstruction. His recent publications demonstrate a strong focus on sensitivity analysis methods, particularly Sobol indices, with applications across multiple domains. He has also made significant contributions to the theory of Wasserstein spaces and their applications in statistical learning. His work bridges theoretical statistics with practical applications in engineering, environmental science, and aviation. Dr. Klein is currently involved in three major funded projects: the PEPR PDE-AI project (2023-2028) on Partial Differential Equations for Artificial Intelligence, the ANR GATSBII project (2025-2029) as project leader, and the ANR MBAP-P project (2025-2029) as a member. These projects reflect his interdisciplinary approach, combining statistical theory with applications in artificial intelligence, engineering, and environmental science.

• Computer Science
• Artificial Intelligence
• Data Analysis
• +3 more

François Brucker serves as a Research Fellow in the Computer Science department with affiliation to the READ research laboratory. His academic profile is registered with ORCID identifier 0000-0002-2971-1207. His research interests span multiple domains within Computer Science, with particular emphasis on Artificial Intelligence, Data Analysis, Software Engineering, Machine Learning, and Information Systems. These research areas reflect contemporary challenges and innovations in computational theory and application. No scientific awards or honors are mentioned in the available documentation. His professional activities appear to focus on research within the READ laboratory framework while contributing to computer science education through teaching responsibilities.

• Machine Learning
• Signal Processing
• Data Science
• +3 more

Salah Bourennane is a Lecturer and researcher in the Computer Science department at the Fresnel Institute , with a focus on advanced machine learning and multidimensional signal processing techniques. His research interests span: Machine Learning (deep learning, transfer learning, optimization algorithms) Signal Processing (hyperspectral analysis, tensor decomposition, noise reduction) Medical Imaging (diagnostic systems, brain mapping, cancer detection) Computer Vision (face recognition, surveillance systems, 3D verification) Security Applications (mask detection, concealed object classification, steganalysis) Recent publications highlight his work in: Tensor-driven methods for low-resolution face recognition Hybrid multilinear-linear architectures for camera network identification Deep learning frameworks for medical diagnostics (e.g., breast cancer segmentation, Alzheimer's disease mapping) Optimization algorithms (e.g., grey wolf optimizer variants) for sensor systems and image processing Real-time pandemic response technologies like social distance monitoring His work is implemented within the Fresnel Institute research laboratory, specializing in computer vision and data science applications.

Jean-Marc LasgouttesView profile

Researcher

• Probabilistic Modeling
• Statistical Physics
• Intelligent Transportation Systems
• +5 more

Jean-Marc Lasgouttes is a Researcher at Inria Paris working within the Astra project team, a joint research initiative between Inria Paris and Valeo. He also holds a teaching position at INSA Rouen Normandie's Department of Mathematical Engineering, where he has instructed courses including Boosting methods (until 2024), Functional Data Analysis (until 2018), and general Data Analysis (until 2024) for both the Mathematical Engineering department and the Specialized Master's program in Data Science. Dr. Lasgouttes' primary research focuses on probabilistic modeling of large systems using statistical physics tools, with particular emphasis on Intelligent Transportation Systems. His work spans traffic flow modeling, vehicle platooning, urban traffic prediction, and geopositioning systems. He frequently employs Markov Random Fields, statistical physics approaches, and game theory to address complex transportation challenges, bridging theoretical statistical methods with practical applications. His research methodology often involves developing novel algorithms like the ★-IPS family for incremental GMRF estimation. Analysis of his recent publications reveals a consistent trajectory of applying advanced probabilistic models to increasingly sophisticated transportation scenarios. His work demonstrates strong expertise in spatio-temporal modeling, with publications covering car-following dynamics, landmark-based positioning, cooperative ITS, and autonomous vehicle systems. The interdisciplinary nature of his research connects statistical physics, machine learning, and transportation engineering to solve real-world mobility challenges. At INSA Rouen Normandie, Dr. Lasgouttes has developed comprehensive teaching materials for data analysis courses, including practical applications of principal component analysis and correspondence analysis using real-world datasets such as European protein consumption patterns and Titanic passenger data. His educational approach emphasizes hands-on implementation with R programming, reflecting his commitment to practical statistical applications. The Astra project team serves as Dr. Lasgouttes' primary research environment, facilitating collaboration between academic researchers and industry partners to address contemporary transportation challenges. His work has contributed to significant research events including the 2018 workshop on Large Random Networks and Constrained Walks honoring Guy Fayolle's 75th birthday, and the 2012 interdisciplinary workshop on inference associated with the Travesti ANR grant.

• Raman Spectroscopy
• Phase Transformations
• Amorphous Materials Stabilization
• +4 more

Yannick GUINET is a Professor at the University of Lille, affiliated with the Materials and Transformations Unit (CNRS UMR 8207). As a member of the Molecular and Therapeutic Materials research team, he specializes in using Raman spectroscopy to analyze phase transformation mechanisms and stabilization of amorphous states in therapeutic materials, including small molecules and proteins, through excipient interactions. His research focuses on: Low-frequency Raman spectroscopy for metastable state analysis Co-amorphous and deep eutectic solvent formulations Drug stabilization mechanisms using amino acids Solid-state loading techniques for mesoporous carriers Phase transitions in amino acids and pharmaceutical compounds Molecular confinement effects in nanoporous materials Professor Guinet's publications demonstrate consistent focus on pharmaceutical material characterization, with recurring themes including amorphous solid dispersions, polymorphic transformations, Raman spectroscopy methodologies, and nanoconfined drug behavior. His work frequently appears in high-impact journals covering physical chemistry, pharmaceutical sciences, and materials engineering.

• Fouling and Cleaning in Food Processing
• Dairy Protein Engineering
• Food-Surface Interfaces
• +5 more

Guillaume DELAPLACE serves as an INRAE Research Director and Laboratory Director of CERTIA at the Materials and Transformations Unit (UMET, CNRS UMR 8207), a joint research entity with the University of Lille. Based at the INRAE PIHM Building (RB2) and UMET's Scientific City (Building C6) in Villeneuve d'Ascq, France, he leads critical work in dairy processing hygiene and fouling mitigation. His position as a senior researcher in France's national agricultural research institute places him at the forefront of food engineering innovation. Research Interests focus on food-surface interfaces in agro-industries , particularly dairy thermal equipment. He investigates protein denaturation kinetics, mineral fraction impacts on fouling, and dimensional analysis of transfer phenomena. His team pioneers biomimetic anti-fouling coatings and ultrasound sensors for real-time clean-in-place monitoring , bridging molecular-scale protein behavior with industrial-scale process optimization. Publication Trends (2020-2024) reveal concentrated efforts in dairy fouling mechanisms, with increasing emphasis on enzymatic cross-linking of casein/whey proteins and calcium-mineral interactions. His work spans predictive modeling, sensor development, and anti-fouling surface engineering, consistently targeting industrial applications in UHT plants and ready-to-drink beverage systems. Over 80% of recent output addresses protein aggregation and deposit management through experimental-numerical approaches. Scientific Awards : No awards, fellowships, or medals were documented in the provided text. Advising and Grants : DELAPLACE has supervised 13 PhD completions and currently mentors 5 doctoral candidates. His projects include PROTEINOLAB (LabCom joint innovation partnership) and participation in ANR/ERC/Interreg initiatives. Collaborations span France (Ingredia Dairy Experts), Brazil (Paulo Peres de Sa Peixoto Jr.), and international networks like LIA, with students transitioning to R&D roles in industry. Labs and Teams : He anchors the Process Interfaces and Materials Hygiene Research team at UMET, specializing in food-surface interactions, transfer phenomena, and mixing dynamics. The group develops fouling prediction tools and anti-adhesion surfaces using plasma deposition and ultrasound, directly addressing dairy industry challenges in heat exchangers and cleaning systems.

Élie AwwadView profile

Associate Professor

• Telecommunication Systems
• Optical Fiber Sensors
• Digital Signal Processing
• +3 more

Élie Awwad is an Associate Professor (Maître de Conférences) in Optical Communications at Télécom Paris, part of Institut Polytechnique de Paris. He leads research in the Optical Telecommunications (GTO) group within the Information Processing and Communication Laboratory (LTCI) under the Department of Communications and Electronics (Comelec). He joined Télécom Paris in 2019 after working as a Research Scientist at Nokia Bell Labs. He holds M.Eng. degrees in Electrical Engineering from Lebanese University and Telecom Engineering from Télécom Paris (2011), and a PhD in Communications and Electronics from Télécom Paris (2015). His research focuses on optical fiber communications and sensing systems, specializing in: Telecommunication Systems Optical Fiber Sensors Digital Signal Processing Distributed Acoustic Sensing (DAS) Nonlinear interference mitigation Novel optical sensing interrogators His publications demonstrate strong emphasis on distributed fiber sensing techniques (especially phase-sensitive OTDR), coherent MIMO processing, machine learning for optical networks, and free-space optical communications. Recent work integrates quantum cascade laser technology and chaotic encryption methods with optical transmission systems. He currently supervises multiple PhD students and has secured research funding through national CIFRE agreements, ANR SAFER project, CIEDS FIBROUS project, and Celtic-Next AI-NET initiative. His lab focuses on developing advanced monitoring solutions for optical networks and improving distributed sensing capabilities over telecom infrastructure.

Thomas BonaldView profile

Professor

• Graph Analysis
• Machine Learning
• Natural Language Processing
• +3 more

Thomas Bonald is a Professor at Télécom Paris, Institut Polytechnique de Paris, where he heads the Data, Intelligence and Graphs (DIG) team at the Information Processing and Communication Laboratory (LTCI). His academic career spans several decades with significant contributions to the fields of graph analysis, machine learning, and network systems. Professor Bonald's research interests focus on data analysis and machine learning, particularly for large graphs, text data, and time series. He has made substantial contributions to knowledge base construction, with involvement in the YAGO project, and has developed the scikit-network Python library for graph analysis. His work bridges theoretical foundations with practical applications across various domains including natural language processing, network analysis, and knowledge representation. His recent publication trends reveal a strong emphasis on graph-based machine learning techniques, knowledge graph refinement, and hierarchical classification systems. Bonald's research shows increasing integration of large language models with structured knowledge bases, as evidenced by his work on Wikidata taxonomy refinement and YAGO development. His publications span top-tier venues in machine learning, information retrieval, and network systems. Professor Bonald has mentored numerous PhD students throughout his career, including both current students (Roman Plaud, Yiwen Peng, Rajaa El Hamdani) and former students who have gone on to receive notable awards such as the Telecom Paris thesis award (Céline Comte) and the Gilles Kahn award (Mathieu Feuillet). He has been instrumental in developing educational resources in probability, statistics, graph learning, and reinforcement learning. He leads the DIG research team at LTCI, which focuses on developing advanced methods for analyzing complex graph structures and creating practical tools like scikit-network. His work demonstrates a consistent trajectory from theoretical network analysis to practical implementations in knowledge representation and machine learning systems.

Ada DiaconescuView profile

Associate Professor

• Autonomic Computing
• Complex Systems
• Software Engineering
• +12 more

Ada Diaconescu is a Tenured Associate Professor at Télécom Paris (Institut Polytechnique de Paris) since October 2009. She is a member of the Autonomous Critical Embedded Systems (ACES) team within the Information Processing and Communication Laboratory (LTCI) , affiliated with the Computer Science and Networks (Infres) department. Research Focus: Autonomic Computing, Organic Computing, Complex Adaptive Systems, Self-Organising Systems, Smart Grids/Cities/Homes, Nature-Inspired Computing, Socio-Technical Systems Key Methodologies: Decentralised Control, Multi-Agent Systems, Component/Service-Oriented Architectures, Multi-Scale Feedback Loops Her recent publications (2021-2023) explore topics like holonic architectures for self-integration, multi-scale feedback systems in smart environments, and decentralised explanatory frameworks for AI transparency. She co-authored the textbook "Autonomic Computing: Principles, Design and Implementation" (Springer, 2013) and contributed chapters to books on Organic Computing (Springer, 2018) and Self-Aware Systems (Springer, 2017). Scientific Leadership: General Chair of IEEE SASO 2017 Steering Committee Co-Chair of IEEE ACSOS (2019-2021) Co-Organizer of Dagstuhl Seminars (2015, 2018) Doctoral Symposium Co-Chair at FAS* 2019 Teaching: Delivered courses on software engineering, autonomic systems, and complex system modeling at Télécom Paris and other institutions. Collaborations: Active in interdisciplinary research with institutions including Leibniz University, ETH Zurich, Arizona State University, and Lancaster University.

Jean-Claude MoissinacView profile

Lecturer

• Semantic Web Technologies
• Knowledge Graphs
• Machine Learning
• +3 more

Jean-Claude Moissinac is a Lecturer at Télécom Paris, part of the Institut Mines-Télécom, affiliated with the Image, Data, Signal department and Multimedia research group. He serves as the educational manager for the CPD master's degree program (Digital Project Designer). His research focuses on semantic technologies applied to multimedia systems and knowledge representation. His primary research areas include: Semantic representations of knowledge and knowledge graphs Machine learning techniques for graph-based data analysis Advanced multimedia document processing (production, transport, representation) Semantic web technologies across diverse application contexts His publications demonstrate consistent focus on semantic technologies, knowledge graphs, and multimedia systems, with recent work emphasizing cultural heritage applications and graph-based machine learning techniques. Earlier publications focused on multimedia systems optimization and SVG standards development. He has led multiple research projects including: ILOT Project : Intelligent Learning Object for Teaching SOA2M : Multimodal interface research Data&Musée : Semantic applications for cultural heritage data SemBib : Semantic representation of bibliographic data GeoRacing : Interactive mobile TV services for sporting events He contributes to standardization efforts as co-author of the SVG web standard and participates in academic committees including SVG Open, IEEE International Symposium on Multimedia, and project evaluations for the French National Research Agency.

Thomas RobertView profile

Associate Professor

• Real-Time Systems
• Distributed Computing
• Fault Tolerance
• +3 more

Thomas Robert is an Associate Professor at Télécom Paris, actively contributing to the AUTONOMOUS CRITICAL EMBEDDED SYSTEMS (ACES) research team under the INFORMATION PROCESSING AND COMMUNICATION LABORATORY (LTCI) . His research spans real-time systems, distributed computing, fault tolerance, and security policy modeling. Research Trends: Recent publications focus on software supply chain security (2025), intrusion detection mechanisms (2024), and mixed-criticality scheduling frameworks like RUN (2015). Earlier works include AADL-based fault tolerance (2010) and real-time error detection (2008). Teaching: Offers courses in programming, operating systems, fault tolerance, and embedded systems at Télécom ParisTech (engineering cycle) and Master 2 programs (SAR, SETI, COMASIC). Collaborations: Works with Laurent Pautet, Jean Leneutre, and colleagues on real-time scheduling and security projects. Tools: Develops PRISM models for system reliability analysis and contributes to fault tolerance practical works.

Aslan TchamkertenView profile

Professor

• Information Theory
• Machine Learning
• Communications and Signal Processing

Aslan Tchamkerten is a Professor at Télécom Paris (Institut Polytechnique de Paris), where he leads the Mathematics of Information and Communications (MIC) group within the Communications and Electronics (Comelec) department. His research bridges information theory and machine learning, focusing on theoretical and applied aspects of communication systems and data processing. Education: Physics Engineering Degree (2000) and PhD in Information Theory (2005) from EPFL, postdoctoral research at MIT (2005–2008). His research interests include information theory, machine learning, and their intersections with communication technologies. He co-organized the 2016 thematic program 'Nexus of Information and Computation Theories' at the Institut Henri Poincaré and served as Associate Editor for IEEE Transactions on Information Theory (2016–2019, 2021–2024). He was awarded a Junior Excellence Chair by the French National Research Agency (ANR) in 2009.