ETIS-Cell seminar - Camille Simon Chane
2026-03-25 15:30 - 17:00 (Europe/Paris) | Online
We are delighted to welcome Camille Simon Chane over Teams for an ETIS-Cell seminar, which will occur on Wednesday, 25 March 2026. Room D331 at ENSEA has been booked for colleagues who would like to see the presentation on a bigger screen.
Title: Acquisition- and data-driven processing for demanding interdisciplinary applications: from cultural heritage to biodiversity assessment
Abstract: This talk presents an overview of my work in computer vision for interdisciplinary applications in cultural heritage and public health, working with unconventional acquisition systems such as event-based cameras. A particular focus will be given to the evolution and positioning of this work as it pertains to planetary boundaries.
Bio: Camille Simon Chane received her MSc in Electrical and Computer Engineering from the Georgia Institute of Technology in 2008, followed in 2009 by the French Engineering diploma from ENSEA. She earned her PhD in Imaging Instrumentation and Computer Vision from the University of Burgundy in 2013, where her research focused on the registration of featureless 3D and multispectral data. From 2013 to 2014, she worked at the Cité de la Musique, developing a luminescence multispectral camera for the study of violin varnishes. She then joined the Vision and Natural Computation team at the Institut de la Vision, where she expanded her expertise in event‑based data processing. Since 2017, she has been an Associate Professor at ENSEA, conducting her research within the Cell team of the ETIS laboratory. Her work centers on data processing from unconventional sensors, with applications in public health and cultural heritage. For example, she contributes to projects assessing the conservation state of book bindings in heritage libraries and developing insect recognition methods based on wing morphology. She is focusing on insect recognition for the 2025−2026 academic year through a sabbatical at Theoretical Ecology Department at CSIC−CEAB.
Link: https://teams.microsoft.com/meet/33442086490136?p=D06BGQX6z0iCVGvFeA
Meeting ID: 334 420 864 901 36
Passcode: [REDACTED]

Séminaire ETIS-CELL - Ghazi Bouaziz
2026-03-26 10:00 - 11:30 | Online
Orateur : Ghazi Bouaziz
Titre : E-santé frugale : Développement d’un système frugal pour détecter l’isolement social via les repas et les activités de mobilité chez les personnes âgées vivant à domicile.
Résumé :
Le vieillissement de la population pose des défis majeurs en matière de maintien à domicile et de prévention de l’isolement social chez les personnes âgées. Dans ce contexte, les systèmes de e-santé constituent une solution prometteuse, mais soulèvent des enjeux importants liés à la consommation des ressources numériques, à la protection des données de santé et à la dépendance technologique.
Ce travail s’inscrit dans une démarche de numérique frugal, responsable et souverain pour le suivi des personnes âgées à domicile. Il propose le développement d’un système intelligent de détection de l’isolement social reposant sur l’observation non intrusive de deux activités de la vie quotidienne : la mobilité et la prise de repas. Le dispositif s’appuie sur des capteurs simples et peu énergivores, intégrés dans un environnement de maison intelligente, et sur un traitement majoritairement local des données.
L’identification automatique des activités est réalisée à l’aide d’algorithmes d’apprentissage automatique non supervisés et légers, permettant de détecter des changements de comportement, qu’ils soient progressifs ou brusques. Ces changements sont ensuite analysés afin d’évaluer un niveau de risque et de déclencher des actions adaptées, allant de l’accompagnement aux activités quotidiennes à l’alerte en cas de situation de mise en danger.
Cette approche démontre qu’il est possible de concevoir des systèmes de e-santé performants tout en limitant leur empreinte numérique, en renforçant la maîtrise des données de santé et en favorisant l’acceptabilité sociale. La frugalité numérique apparaît ainsi comme un levier essentiel pour une e-santé durable, éthique et souveraine.
Short bio : Ghazi Bouaziz est enseignant-chercheur en Internet des Objets à ESIEE-IT (France). Il est titulaire d’un doctorat en ingénierie pour la santé et pour le vivant de l’Université Toulouse III, où ses travaux ont porté sur le développement de systèmes intelligents pour la détection de l’isolement social des personnes âgées, en s’appuyant sur la reconnaissance des activités de la vie quotidienne à partir de capteurs domotiques et d’algorithmes d’intelligence artificielle.
Ses recherches actuelles portent sur l’Internet des Objets, l’habitat intelligent et l’e-santé, avec un intérêt particulier pour le traitement de données multimodales, l’intelligence artificielle, la surveillance non intrusive et l’analyse comportementale appliquées au maintien à domicile.
Teams link: https://teams.microsoft.com/meet/38497897320600?p=AsRd3jueKOI0XsEJqb
Meeting ID: 384 978 973 206 00
Passcode: [REDACTED]

ETIS-CELL seminar - Louis L'Haridon
2026-03-26 16:00 - 17:30 | ENSEA, salle 331, 6 avenue du Ponceau, Cergy, 95000, France
We are delighted to welcome Louis L’Haridon on Thursday, 26 March 2026, in room D331 (ENSEA) at 4:00pm for the second ETIS-Cell seminar of the week.
Title: Bio-Inspired Frugal and Adaptive Embedded AI for Constrained Systems
Abstract: In this talk, I present my work on bio-inspired and embodied AI models implemented on embedded robotic systems under real-time and resource constraints. I developed artificial mechanoreceptive and nociceptive sensing, neuromodulation-based regulation, and event-driven representations to enable adaptive behavior with controlled computational cost, following an Application–Architecture Adequation (AAA) approach. Building on these results, I outline a research trajectory toward hardware-aware embedded AI for constrained systems.
Bio: Louis L’Haridon is enseignant-chercheur CDD at CY Cergy Paris Université (ETIS) where he study adaptive and, bio-inspired and embedded AI on constrained systems like robots. He teaches class on Embedded computing, Real Time, computer architecture and algorithmic.
The seminar will also be accessible via video-conference:
Teams link: https://teams.microsoft.com/meet/38361145050952?p=KR9SG0zCT2IaenxoX4
Meeting ID: 383 611 450 509 52
Passcode: [REDACTED]

Séminaire ETIS-CELL - Paul Desombre
2026-03-30 11:00 - 12:30 | Online
Orateur : Paul Desombre (ETIS)
Title: Communications Front-End modeling: Photodiodes, Digital Power Amplifiers & Systems
Abstract:
As energy efficiency and sobriety becomes more and more a societal concern, communication systems need to be adapted and to consume less energy. In this talk, I will present my research on front-end component modeling, from photodiodes to Digital Power Amplifiers. In photonic-based sub-TeraHertz communications, the photodiode is a key element on the available transmit power. In my thesis, I have proposed a model and shown that Continuous Phase Modulations has a better resistance against photodiodes non-linear effects as well as a better energy efficiency than traditional waveforms. During my post-doc at ETIS I focus on Digital Power Amplifiers (DPAs) that have emerged in modern Wireless transmission because of their better efficiency than Analog Power Amplifiers (APAs). I propose a model along with a new Digital Pre-Distortion scheme, adapted to this new kind of power amplifiers. The talk will conclude on a projection plan of my research with short-term and long-term projects.
Team link: https://teams.microsoft.com/meet/31942320814023?p=pSkW9xXckmBVCDlmqr
Meeting ID: 319 423 208 140 23
Passcode: [REDACTED]

[ETIS - Lab Seminar] On the Many Facets of Fairness and Explainability in Graph Data
2026-03-30 13:30 - 15:00 | ENSEA, 6, avenue du Ponceau, Cergy, 95000, France
Date: 30/03/2026
Time: 13h30
Place: ENSEA (room to be defined)
Speaker: Prof. Evaggelia Pitoura, University of Ioannina
Title: On the Many Facets of Fairness and Explainability in Graph Data
Abstract:
As AI systems are increasingly used in domains with societal impact, ensuring fairness and transparency has become a central challenge. In this talk, I will present an overview of our recent research on fairness, explainability, and their interplay, focusing on graph data, graph-based learning tasks and knowledge graphs. I will discuss multiple facets of fairness in data graphs, ranging from lack of bias in knowledge graph tasks to structural properties such as connectivity, and examine the challenges of explaining decisions and outcomes in graph settings, including counterfactual explanations. More broadly, the talk will highlight fairness and explainability as closely connected dimensions of responsible graph data management.
Bio:
Evaggelia Pitoura is a Professor at the Department of Computer Science and Engineering at the University of Ioannina and a Lead Researcher at Archimedes Research Unit, Athena RC, Greece. She holds a BEng degree from the University of Patras, Greece, and an MS and PhD from Purdue University, USA. Her current research interests focus on two primary areas: responsible data management, with a focus on fairness, explainability, and their interplay; and on graph exploration and analysis. For her work, he has received best paper awards, a Marie Currie Fellowship and two Recognition of Service Awards from ACM. She is an ACM senior member, founding chair of the Hellenic ACM SIGMOD chapter, and member of the sectorial scientific council of Greece National Council for Research, Technology and Innovation.

Séminaire ETIS-ICI - Samar Chebbi
2026-03-31 10:30 - 12:00
Orateur : Dr. Samar Chebbi will present the following work.
Title: From MIMO-NOMA Optimization to EMF Mapping: Towards Sustainable and Energy-Efficient Wireless Networks
Abstract:
The presentation will first introduce the context of next-generation wireless networks and the main challenges related to massive connectivity, interference management, and resource allocation. I will then present my doctoral work on MIMO-NOMA systems, focusing on user clustering and power allocation strategies based on optimization techniques, including metaheuristic approaches. In a second part, I will present my current research at Télécom Paris on electromagnetic field (EMF) exposure mapping, where machine learning methods are used to model and predict exposure levels from heterogeneous data sources combining measurements and simulations. Finally, I will discuss the perspective of sustainable wireless networks, highlighting the need to jointly address performance, energy efficiency, and EMF exposure, and outlining future research directions at the intersection of optimization, physical modeling, and artificial intelligence.

Séminaire ETIS-CELL - Aurélique Saulquin
2026-03-31 14:00 - 15:30 | Online
Orateur : Aurélique Saulquin (CRIStAL, Université de Lille)
Title: Spiking Neural Network emulation on FPGA for low-power AI
Abstract:
AI, and particularly ANNs have become central to modern computing with remarkable performances for complex task resolution. However, deploying ANNs on embedded systems remains challenging due to their complexity. Neuromorphic computing, and especially Spiking
Neural Networks (SNNs) offers an alternative solution, drawing inspiration from the brain.
Although promising, neuromorphic chips are limited and poorly reconfigurable, making FPGAs an interesting target for research and embedded low-power classification tasks.
In this context, we develop ModNEF, an open-source modular FPGA architecture for SNN inference. Modnef is based on the interconnection of independent modules, offering high implementation flexibility and control. ModNEF was validated with standard neuromorphic
datasets and with a use case for sperm whale detection in the Mediterranean Sea.
Team link: https://teams.microsoft.com/meet/31942320814023?p=pSkW9xXckmBVCDlmqr
Meeting ID: 319 423 208 140 23
Passcode: [REDACTED]

Séminaire ETIS-DATA&AI - Intégration de métadonnées de nœuds dans des modèles graphiques gaussiens contraints par le Laplacien
2026-03-31 15:00 - 16:30 | Online
Titre :
Intégration de métadonnées de nœuds dans des modèles graphiques gaussiens contraints par le Laplacien
Résumé :
Nous nous intéressons à l’apprentissage de graphes dans le cadre des modèles graphiques gaussiens (GGM). Dans ce contexte, les matrices de données sont souvent accompagnées de métadonnées auxiliaires (par exemple, des descriptions textuelles associées à chaque nœud), qui sont généralement ignorées dans les méthodes traditionnelles d’estimation de graphes.
Pour combler cette lacune, nous proposons une approche d’apprentissage de graphe fondée sur des GGM contraints par le Laplacien, exploitant conjointement les signaux des nœuds et ces métadonnées. La formulation obtenue conduit à un problème d’optimisation pour lequel nous développons un algorithme de majoration-minimisation (MM) efficace, avec des mises à jour sous forme fermée à chaque itération. Des résultats expérimentaux sur des données financières réelles montrent que la méthode proposée améliore significativement les performances de regroupement de graphes, illustrant ainsi l’intérêt de la fusion de ces deux sources d’information.
Intervenant :
Jianhua WANG, CNAM / Université Paris-Nanterre
Séminaire en ligne, lien à venir.

Séminaire ETIS-CELL - Pascal Cotret
2026-04-01 13:30 - 15:00 | Online
Orateur : Pascal Cotret (ENSTA Brest)
Title: Contributions to software-hardware border security
Abstract:
This seminar provides an overview of some research activities, which examines security at various levels in both software and hardware. First, I will discuss work in which hardware supports software security (code monitoring on ARM, securing JIT compiled code). In the second part, the contributions presented involve hardware accelerators for security challenges: here, there is a strong algorithm-architecture fit to optimize the use of the FPGA resources to solve security problems. Finally, in the last section, we will focus on security at the microarchitecture level of embedded systems, and more specifically on cache memories in the context of using a TEE (Trusted Execution Environment) on a RISC-V processor.
Short bio: Pascal Cotret is an associate professor at ENSTA on the Brest campus. He earned his Ph.D. from the University of South Brittany in 2012, served as a lecturer and researcher at CentraleSupélec Rennes from 2014 to 2017, and then worked for two years at Thales SIX GTS before joining ENSTA in 2019. His expertise focuses on security at the software/hardware interface and embedded systems (heterogeneous architectures, reconfigurable components such as FPGAs). He is also interested in the algorithm-architecture matching of security mechanisms.
Teams Link: https://teams.microsoft.com/meet/34941367702216?p=vvHuTzMkM1BarALE08
Meeting ID: 349 413 677 022 16
Passcode: [REDACTED]

Séminaire ETIS-ICI - Adrien Berthelot
2026-04-01 14:00 - 15:30 | Online
Orateur : Dr. Adrien Berthelot (Univ. Bristol)
Titre: Empreinte environnementale du numérique : comprendre et évaluer
Résumé :
Des préoccupations autour de la consommation d’électricité du numérique pendant la crise énergétique de 2022, en passant par les controverses autour de l’empreinte carbone de ce secteur, et sans oublier les tragiques évènements au Congo, l’industrie du numérique pose des problèmes aussi divers et complexes que son écosystème. Les impacts environnementaux du numérique sont de diverses natures : contribution au réchauffement climatique, consommation d’eau, extraction des métaux et des énergies fossiles. De plus, ces impacts sont répartis de manière hétérogène entre les différentes phases de cycle de vie des équipements et les différentes parties des services numériques, des ordinateurs individuels aux immenses infrastructures de communications et de calculs. Il y a derrière le plus commun des usages du numérique, comme naviguer sur un site internet, une complexité d’attribution des couts environnementaux et par extension des leviers de réductions de ces “couts” environnementaux.
Dans cette présentation, je vous propose d’explorer l’empreinte du numérique au travers de la notion transversale de service numérique. Loin d’une métrique unique, nous verrons comment percevoir ces impacts au travers d’analyse de cycle de vie (ACV) de services numériques. Sur la base de cette évaluation des impacts directs, nous détaillerons les leviers possibles pour réduire l’empreinte environnementale du numérique. Nous évoquerons les problèmes posés par l’utilisation de métrique unique, de performance ou d’efficacité. Nous verrons alors que ces leviers sont aussi divers que les problèmes posés par le numérique, et nécessitent, là aussi, une approche multicritère.
The talk is in French and slides are in English.

Séminaire ETIS-CELL - Khemmar Redouane
2026-04-02 10:00 - 11:30 | Online
Orateur : Khemmar Redouane (ESIGELEC)
Titre : Contribution à la perception pour la smart mobilité
Résumé :
Les véhicules autonomes sont de plus en plus présents dans notre quotidien, ouvrant de nouvelles perspectives pour la smart mobilité. Un véhicule autonome doit comprendre 3 fonctions essentielles : perception, décision et actions. Plus le système est capable de percevoir son environnement, plus il prendra de meilleures décisions lui permettant, in fine, de déclencher des actions répondant aux exigences de sécurité, confort et d’énergie. La détection, localisation et tracking d’objets sont des tâches indispensables pour la perception. Depuis 2012, le deep learning est devenu un outil très puissant en raison de sa capacité à traiter de grandes quantités de données. L’apparition de nombreuses méthodes basées sur l’apprentissage profond a conduit à des progrès significatifs. Malgré cet engouement à l’IA, peu de méthodes se concentrent sur l’aspect temps-réel, essentiel pour les applications réelles et ce, en raison des coûts de calcul élevés. En plus, ces algorithmes présentent des lacunes évidentes dans les scènes complexes en partie à cause du manque de données vérité terrain comme pour la smart mobilité ferroviaire et la santé. En plus de la précision et la vitesse, les algorithmes de perception doivent prendre en compte la contrainte d’énergie liée aux systèmes embarqués. Mes travaux de recherche sont concernés par cette problématique et se concentrent donc sur la perception d’environnement pour deux domaines de la smart mobilité : routier/ferroviaire et robotique mobile/santé. L’objectif est d’atteindre un niveau d’analyse et de compréhension de scènes complexes permettant d’assurer une smart mobilité de très haut niveau de sécurité, de confort et d’énergie optimale. Cela repose sur deux briques essentielles et complémentaires : 1. Système fusion multicapteurs permettant d’enrichir davantage la perception avec des données hétérogènes, 2. Perception d’environnement basée IA permettant l’exploitation des données collectées pour une meilleure prédiction de l’ensemble des situations. Il s’agit donc du développement de plateformes génériques ouvertes pour expérimenter et valider des concepts technologiques et scientifiques du monde académique et industriel.
Short bio: Après une formation d’ingénieur en électronique, un DEA en informatique industrielle et d’un Master européen en traitement d’images à l’Université de Poitiers (2002), j’ai préparé au sein de l’Université de Strasbourg un doctorat en traitement d’images et vision par ordinateur (2005). J’ai obtenu par la suite une Habilitation à Diriger des Recherches (HDR) en vision par ordinateur, intelligence artificielle et smart mobilité à l’Université Rouen Normandie (2022). J’ai été moniteur au Centre d’Initiation à l’Enseignement Supérieur (CIES Alsace) pour une formation d’enseignant chercheur (2002-2005). J’ai occupé le poste d’Attaché Temporaire d’Enseignement et de Recherche (ATER) pendant 2 ans à l’Université de Strasbourg (2005-2007) et un PostDoc en gestion électronique des documents (GED) et dématérialisation au sein du groupe Jouve (2007-2008). Par la suite, j’ai occupé plusieurs postes en industrie en tant que chef de projet au sein des grands comptes industriels (JOUVE, THALES, ALTEN). Depuis 2009, je suis enseignant chercheur à l’ESIGELEC en systèmes embarqués, robotique mobile et smart mobilité. Mes travaux de recherche portent principalement sur la vision par ordinateur, robotique mobile, intelligence artificielle et smart mobilité.
Teams link: https://teams.microsoft.com/meet/382497063309?p=QKk3yAxhEeTcXWt1Ch
Meeting ID: 382 497 063 309
Passcode: [REDACTED]

Séminaire ETIS-CELL - Sylvain Colomer
2026-04-02 16:00 - 17:30 | Online
Orateur : Sylvain Colomer
Title: Visual perception across scales: from bio-inspired navigation to large-scale scene understanding
Abstract:
Visual perception, or computer vision, is a central component of intelligent and robotic systems. It plays a key role in a wide range of applications, from robotic navigation to remote sensing, scene understanding, medical imaging, or large-scale visual data analysis. Although visual data is very rich in information, it is also highly complex, as it is subject to many challenges such as illumination variations, weather conditions, viewpoint and scale changes, as well as domain shifts or noise. Learning efficient and meaningful representations from such data is therefore a major challenge for intelligent systems, especially under constraints related to performance, computational cost, and energy efficiency. In this context, my research focuses on understanding how visual representations can be learned and exploited across different scales and application domains. In particular, I investigate how perception models can be designed to remain robust and adaptable when moving from local, embodied settings to large-scale visual understanding tasks. During my PhD, I developed bio-inspired approaches for visual navigation and localization, leveraging principles from neuroscience to design robust and adaptive models for decision-making in autonomous systems. These models aim to bridge perception and action through structured visual representations, with a particular emphasis on efficiency through the use of simple, optimized monocular vision systems and lightweight computational models. In addition, part of this work focused on deploying these models on FPGA platforms, with the goal of designing efficient hardware implementations and exploring circuit architectures adapted to bio-inspired and neural computation. More recently, my work has explored large-scale scene understanding in the context of remote sensing, using deep learning techniques for tasks such as instance segmentation and analysis of hyperspectral aerial imagery, in particular for large-scale forest monitoring in Canada. This setting introduces new challenges related to scale, variability, and data complexity, and raises questions about the deployment of such systems directly on drones to enable efficient and adaptive forest monitoring. In this talk, I will highlight the connections between these research directions and show how they contribute to the design of more general, robust, and efficient visual perception systems. I will finally discuss perspectives towards adaptive, energy-efficient, and deployable AI models, capable of bridging embodied perception and large-scale visual understanding in real-world applications.
Bio: Sylvain Colomer is a researcher in computer vision and robotic navigation. His work focuses on designing robust and efficient visual models for real-world applications, from autonomous navigation to large-scale remote sensing. He obtained his PhD from CY Cergy Paris University, where he developed bio-inspired approaches for visual navigation on embedded systems. He then worked as a postdoctoral researcher at the University of Toronto on deep learning methods for hyperspectral image analysis and forest monitoring. His research aims to bridge embodied perception and large-scale visual understanding. His current research focuses on designing robust and efficient visual perception models that can operate in real-world conditions, from resource-constrained embedded systems to large-scale data analysis, with an emphasis on multimodality, generalization, and deployment.
Teams link: https://teams.microsoft.com/meet/37168326511389?p=dQSREX67PUox8rsHTo
Meeting ID: 371 683 265 113 89
Passcode: [REDACTED]

Séminaire ETIS-CELL - Julien Le Kernec
2026-04-02 16:00 - 17:30 | ENSEA, salle 331, 6 avenue du Ponceau, Cergy, 95000, France
Orateur : Julien Le Kernec, Senior Lecturer, HDR, University of Glasgow.
Title: Radar sensing in assisted living: an overview
Abstract: In this keynote, I will discuss the place of radar for assisted living. First, the context of assisted living and the urgency to address the problem will be described. The second part will give an overview of existing sensing modalities for assisted living and explain why radar is an upcoming preferred modality to address this issue. The third section presents developments in machine learning that help improve performances in classification, especially with deep learning with a reflection on lessons learned from it. Finally, I’ll conclude with open challenges and future developments.
Bio: Dr Julien Le Kernec is currently a Senior Lecturer with the School of Engineering in the Autonomous Systems & Connectivity Group, University of Glasgow, he is also an adjunct Associate Professor at the University Cergy-Pontoise, France, in the ETIS (Information and Signal Processing group). Previous to this, he held a post-doctoral position with the Kuang-Chi Institute of Advanced Technology, Shenzhen, China, from 2011 to 2012 and he was a Lecturer at the Department of Electrical and Electronic Engineering at the University of Nottingham Ningbo China, from 2012 to 2016. Dr Le Kernec received his B.Eng. and M.Eng. degrees in Electronic Engineering from the Cork Institute of Technology, Ireland, in 2004 and 2006, respectively, and his Ph.D. degree in Electronic Engineering from the University Pierre and Marie Curie, France, in 2011. In 2022, he received “Habilitation a Diriger des Recherches” from University Cergy-Pontoise, France.
His research interests include radar system design, software-defined radio/radar, signal processing, and health applications. Dr Le Kernec has over 130 publications in journals (IEEE sensors, IEEE Signal processing Magazine, IEEE Journal of Biomedical and Health Informatics, Nature Scientific Reports), Conferences (IET internation radar conference, IEEE radarcon,..), book chapters, patents and databases.
Teams link: https://teams.microsoft.com/meet/335139198265625?p=Gym3TZyH3Fd4cQMDaB
Meeting ID: 335 139 198 265 625
Passcode: [REDACTED]

Séminaire ETIS-ICI - Sotiris Skaperas
2026-04-09 10:30 - 12:00 | ENSEA, salle 331, 6 avenue du Ponceau, Cergy, 95000, France
Title: Efficient and robust learning approaches for dynamic wireless and edge systems.
Orateur : Dr. Sotiris Skaperas, ETIS.
Abstract: Next-generation communication and computing infrastructures operate under dynamic conditions, resource constraints, and increasing demands for reliability and security. In this talk, I will present recent contributions on learning-based methods that address these challenges across wireless and edge environments. The presentation will cover anomaly detection in wireless mesh networks, efficient and robust learning in dynamic edge systems, and physical-layer authentication under adversarial conditions. Overall, these works explore how intelligent methods can improve reliability, efficiency, and security in dynamic wireless and edge systems.
Bio: Sotiris Skaperas is postdoctoral researcher at ETIS UMR 8051, CY Cergy Paris University, ENSEA, CNRS and he is currently working on the EU SNS JU ROBUST-6G project. His research focuses on 6G wireless systems, IoT, and edge-cloud environments, with an emphasis on statistical modeling and machine learning for anomaly detection, resource management, and wireless physical-layer security.

ETIS-ICI Seminar - E. Veronica Belmega
2026-04-14 10:30 - 12:00 | ENSEA, salle 331, 6 avenue du Ponceau, Cergy, 95000, France
We are glad to announce the upcoming ETIS-ICI seminar by Prof. E. Veronica Belmega, which will take place on Tuesday, 10:30am, April 14th at D331 ENSEA.
It is also accessible by the following link https://webconf.numerique.gouv.fr/ETISICI2026
Title: Research overview and two recent contributions
Abstract: After a brief research activity overview, the presentation will focus on two recent contributions. 1) Beam coherence time prediction in mobile high frequency (0.1THz) communications exploiting deep learning, in collaboration with Irched Chafaa and Giacomo Bacci at Univ. of Pisa; 2) Defence against false data injection attacks in the power grid exploiting a two-player zero-sum game in collaboration with Sajjad Maleki (PhD co-tutelle CYU Cergy Paris Univ. and Univ. of Warwick) and Subhash Lakshminarayana at Univ. of Warwick.
Bio: E. Veronica Belmega is a Professor at Univ. Gustave Eiffel – ESIEE Paris and LIGM laboratory, Marne-la-Vallée, France, since May 2022. Previously, she was an Associate Professor (MCF HDR) with ENSEA graduate school and ETIS laboratory, Cergy-Pontoise, France, where she is currently an Associate Researcher. Her main research interests lie in convex and online optimization, game theory and machine learning applied to resource optimization and security of wireless communication and smart grid networks.

Séminaire ETIS-ICI - Laura Luzzi
2026-04-15 10:30 - 12:00 | ENSEA, salle 331, 6 avenue du Ponceau, Cergy, 95000, France
Title: Covert communication over additive noise channels
(based on joint work with Cécile Bouette, Ligong Wang and Matthieu Bloch)
Abstract: In the first part of the talk, I will give a brief overview of my research in the areas of physical layer security and post-quantum cryptography.
Then, I will focus on some recent results in the setting of covert communication in physical layer security. In this scenario, a transmitter and a receiver wish to communicate reliably while preventing an eavesdropper from detecting the fact that a communication is ongoing. It is known that the channel capacity under this constraint is zero, and the amount of information that can be sent reliably and covertly scales like the square root of the number of channel uses. We study the scaling constant of the square root law, or “covert capacity”, for a general class of memoryless additive noise channels, and show that it is upper bounded by the square root of the varentropy of the noise. Under some additional assumptions, we show that this upper bound is tight.
In the last part of the talk, we consider the asymptotic limits of covert communication over an i.i.d. Gaussian channel when we allow a positive average error probability ε. In this case, the strong converse does not hold, and the scaling constant C_ε depends on ε. We derive upper and lower bounds for C_ε and show that allowing a small positive error probability enables the transmission of additional covert information.
Bio: Laura Luzzi is an Associate Professor (MCF HDR) at ENSEA, Cergy-Pontoise, France, and a researcher at ETIS (UMR 8051, CY Cergy Paris Université, ENSEA, CNRS). She is currently a visiting researcher with Project COSMIQ, Centre Inria de Paris. Her research interests include physical layer security and post-quantum cryptography.

ETIS seminar: Claudia Paris
2026-05-11 13:30 - 15:00 | Online
Title: Integrating multimodal satellite data, ground-based field photos and AI to go beyond traditional mapping.
Abstract
Increasingly frequent extreme weather events and climate change pose growing threats to human well-being and ecosystem health. To monitor and assess environmental change and land degradation (e.g., deforestation, desertification, urban expansion), satellite data can be leveraged for the regular production of land-cover maps that capture dynamics across the Earth’s surface. These data have been widely used to generate consistent, large-scale land-cover information over time, enabling monitoring at regional to global scales.
With rapid advancements in Earth Observation technologies and computational capabilities, there is now an opportunity to move beyond traditional land-cover mapping toward more comprehensive and actionable characterizations of landscape dynamics. In particular, the increasing availability of crowdsourced, geo-tagged field photos introduces an additional and complementary data source. When integrated with satellite imagery, these data enable richer, multi-dimensional representations of land systems.
This integration allows information to be captured at multiple levels of semantic detail, which is essential for understanding complex and heterogeneous landscapes. While satellite image time series provide valuable temporal information on land-cover dynamics, ground-level photos offer fine-grained spatial detail and contextual insights into local conditions, such as vegetation structure, land management practices, disturbance events, and small-scale landscape features that are difficult to observe from space.
Recent advances in Artificial Intelligence (AI), particularly in visual-language models, self-supervised learning, and multimodal approaches, create new opportunities to effectively combine satellite and ground-based data. The integration of geo-tagged field photos, satellite image time series, and AI methods enables more detailed and scalable interpretation of land-cover patterns and processes, supporting improved detection and understanding of environmental changes that were previously difficult to capture.
Bio
Claudia Paris is a Senior Assistant Professor (UD1) in the Faculty of Geoinformation and Earth Observation Sciences (ITC) at the University of Twente, Enschede, the Netherlands. She received the “Laurea” (B.S.), the “Laurea Specialistica” (M.S.) (summa cum laude) degrees in Telecommunication Engineering and the Ph.D. in Information and Communication Technology from the University of Trento, Italy, in 2010, 2012, 2016, respectively. She accomplished the Honors Master Program in Research within the Master’s Degree in Telecommunication Engineering in 2012. Claudia Paris’ research encompasses image processing, signal processing, pattern recognition, machine learning, and deep learning, specifically applied to remote sensing image analysis. She focuses on designing innovative and automated workflows for the analysis and classification of large-scale Earth Observation (EO) data for various applications (e.g., forest/agricultural mapping and monitoring) by leveraging high-performance computing (HPC) and cloud computing platforms (Google Earth Engine). Her main research interests focus on the classification and fusion of multisource remote sensing data, multitemporal image analysis, domain adaptation methods, and land cover map updates. She has been conducting research on these topics in the framework of national and international projects. She is a member of the scientific and programme committee of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS) and the SPIE International Symposium on Remote Conferences, respectively, and is also a referee for several international journals. Dr. Paris was twice the recipient of the prestigious Symposium Prize Paper Award (exceptional paper in terms of content and impact on the Geoscience & Remote Sensing Society) at the 2016 IEEE IGARSS (Beijing, China, 2016) and at the 2017 IEEE IGARSS (Fort Worth, TX, USA, 2017). She also won the IEEE Geoscience and Remote Sensing Society 2022 Letters Prize Paper Award (exceptional paper in terms of content and impact on the GRS-Society).
Place: online (link to be shared)
