Laboratory of
signals and systems

The laboratory

The Laboratory of Signals and Systems (L2S, UMR 8506) is a French research laboratory created in 1974, located in Paris-Saclay University, and jointly operated by the CNRS, CentraleSupélec and the University of Paris-Saclay.

Research at L2S focuses on fundamental and applied mathematical aspects of control theory, AI, data science, information, signal and image processing, communication, and network theory.

The laboratory was evaluated by HCERES in 2018 and will be evaluated again in 2023.

Key figures

96

faculty members

123

PhD students

438

publications in 2019

Research

Research fields

Systems and Control

Our research in area of control systems covers both methodological developments and concrete applications. It addresses analysis, modeling, and control problems in fields ranging from biology to power systems engineering. Methodological developments concern, among others, hybrid systems, delay systems, and model predictive control, with a particular emphasis on nonlinear systems. These activities are often carried […]
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Signal processing and Statistics

Research activities in Signal processing and Statistics are inspired from data processing challenges in various application fields such as health engineering, nondestructive testing of materials, acoustics, remote sensing, astrophysics, transportation, electrical and mechanical engineering.
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Networks and Telecommunications

The Network and Telecommunications activity carries out fundamental research in the field of Networks and Telecommunications. In terms of applications, the main areas of interest include wireless networks, vehicular networks, but also emerging subjects such as synergy between communication networks and smart grids.
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Applications

Energy

France is developing a research and innovation strategy to develop information technologies in the field of intelligent electrical power systems (or Smart Grids), an interdisciplinary field par excellence. The applications include power generation systems (e.g. from renewable energy sources), large interconnected power transmission networks, local distribution networks, on-board electrical systems and land-based electric vehicles in […]
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Health & biology

The “data revolution” is impacting all areas of “health and life” and offers a field of application where statistics/machine learning, image processing, control theory and data protection find their full meaning. The “health and life” interdisciplinary axis aims at promoting existing actions and encourage new interdisciplinary collaborations in the field of biology and medicine. Examples […]
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Industry 4.0

Industry of the future, in the wave of the current fourth industrial revolution, will be powered by the drastic increase in the information processing capacities, the communication networks expansion, the development of modeling and simulation tools, and the advances of management and logistics processes. In this context, the “Industry of the future” interdisciplinary axis federates […]
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Agenda

12/03/2021

Speeding up of kernel-based learning for high-order tensor

Speaker — Ouafae Karmouda (SIGMA team at CRIStAL laboratory, Lille, France) Abstract — Supervised learning is a major task to […]
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09/03/2021

Séminaire de Alessio Iovine

09/03/2021 – 14h00-15h00 – Online On the utilization of Macroscopic Information for String Stability of a Vehicular Platoon Alessio Iovine […]
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05/03/2021

Sampling rates for l1 synthesis

Speaker — Claire Boyer (Sorbonne Université) Abstract — This work investigates the problem of signal recovery from undersampled noisy sub-Gaussian […]
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04/03/2021

UQSay #25

The twenty-fifth UQSay seminar on UQ, DACE and related topics, organized by L2S, MSSMAT, LMT and EDF R&D, will take […]
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18/02/2021

UQSay #24

The twenty-fourth UQSay seminar on UQ, DACE and related topics, organized by L2S, MSSMAT, LMT and EDF R&D, will take […]
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16/02/2021

Séminaire de Edouard Pauwels

16/02/2021 – 14h00-15h00 – Online A mathematical model for nonsmooth algorithmic differentiation with applications to machine learning. Edouard Pauwels (Université […]
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