Etis

Séminaire ETIS-ICI - Sotiris Skaperas
2026-04-09 10:30 - 12:00 (Europe/Paris) | 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)
