Inria · Paris-Saclay · Head of the BOOST team
Research Director at Inria — control theory & signal processing.
Building estimation methods and algorithms that reveal the hidden state of physical systems — and turn that insight into control and monitoring strategies for real engineering and bioengineering problems.
Taous-Meriem Laleg is a Research Director at Inria, the French National Institute for Research in Digital Science and Technology, based in Paris-Saclay, where she leads the BOOST project team.
From 2011 to 2021 she was an Assistant and then Associate Professor of Electrical and Computer Engineering and Applied Mathematics at King Abdullah University of Science and Technology (KAUST). There she founded and led the Estimation, Modeling, and Analysis (EMAN) research group, was affiliated with the Bioengineering Program and the Computational Bioscience Research Center (CBRC), and contributed to the KAUST Smart Health Initiative.
Her research sits at the meeting point of control theory and signal processing. The common thread is estimation: developing methods and algorithms to understand physical systems, extract hidden information, and design advanced control and monitoring strategies — always driven by concrete problems in engineering and bioengineering.
Dr. Laleg is actively involved in the scientific community. She currently serves as Associate Editor for IEEE Transactions on Automatic Control and IEEE Transactions on Network Systems, and as Editor for the International Journal of Robust and Nonlinear Control. She is a member of the IEEE Control Conference Editorial Board (CEB) and of the EURASIP Technical Area Committee on Theoretical and Methodological Trends in Signal Processing (TMTSP), and serves as Vice-Chair for Social Media of the IFAC Technical Committee on biological and medical systems (TC 8.2) — see the full list of editorial activities below.
Estimation and observer design for nonlinear ODEs, PDEs and fractional-order systems — spanning asymptotic observers with prescribed-time convergence, non-asymptotic algebraic methods based on modulating functions, and, more recently, learning-based estimation combining observer theory with machine learning, including KKL observers with deep learning, physics-informed neural networks, and contraction-based analysis.
A quantum-inspired signal and image analysis method built on the spectral properties of the Schrödinger operator, extended to noise reduction, image contrast enhancement, MR spectroscopy and biomedical signal classification.
Cardiovascular and cerebral signal modeling and monitoring, with a current focus on brain-heart interaction — including stress assessment in athletes — alongside blood pressure and arterial stiffness from PPG and prediction of vulnerable carotid plaques from medical imaging — applied to health and to performance and wellbeing in sport.
“Modeling Brain-Heart Interaction: A Review of Mechanistic Dynamical Models” (S. N. Sadoun, A. Boutin, F. Cottin, T.-M. Laleg-Kirati).
“Image contrast enhancement based on the Schrödinger operator spectrum” (J. M. Vargas, T.-M. Laleg-Kirati).
“Signal-Based Monitoring for Tissue Oxygenation & Diabetes Characterization” (A. Guir, C. French, D. Robbins, D. Gordon, M. Gernigon, T.-M. Laleg-Kirati); “CT-Based Classification of Symptomatic vs. Asymptomatic Carotid Plaques Using Schrödinger Spectrum Features” (J. M. Vargas Garcia, L. Wang, A. Piedelièvre, G. Goudot, J. M. Davaine, T.-M. Laleg-Kirati); and “State and Unknown Input Estimation Using a Left-Invertibility Constrained Neural Estimator in Delayed Autonomic Cardiac Dynamics” (S. N. Sadoun, G. A. D’Inverno, A. Boutin, F. Cottin, T.-M. Laleg-Kirati).
“Physics-Informed Bank of Estimators for Joint Estimation of States and Parameters for Nonlinear Disturbed Systems” (M. Boukaf, Z. Belkhatir, M. Chadli, T.-M. Laleg-Kirati) and “Physics-Informed Neural Estimation of State and Unknown Input in Autonomic Cardiac Dynamics with Left-Invertibility Constraints” (S. N. Sadoun, G. A. d’Inverno, A. Boutin, F. Cottin, T.-M. Laleg-Kirati).
“Neural Contraction Metrics for Observer-Based Trajectory Tracking with ISS Guarantees” (M. Boukaf, Y. Marani, I. J. Santos Filho, M. Chadli, Z. Belkhatir, T.-M. Laleg-Kirati) and “An Explicit Surrogate for Gaussian Mixture Flow Matching with Wasserstein Gap Bounds” (E. Rostami, T.-M. Laleg-Kirati, H. Tembine).
“SpaTeoGL: Spatiotemporal Graph Learning for Interpretable Seizure Onset Zone Analysis from Intracranial EEG” (E. Rostami, A. Einizade, T.-M. Laleg-Kirati) and “A Non-Separable Spectral Image Representation Based on the Semi-Classical Schrödinger Geometry” (J. M. Vargas, I. J. Santos Filho, T.-M. Laleg-Kirati).
“Drone Reference Tracking in a Non-Inertial Frame Using Sliding Mode Control Based Kalman Filter with Unknown Input” (Y. Marani, K. Telegenov, T.-M. Laleg-Kirati) and “A Deep-Learning-Based Observer for State Estimation of Direct Contact Membrane Distillation” (Y. Wang, Y. Marani, T.-M. Laleg-Kirati).
Spectrogram image-based machine learning for carotid-to-femoral pulse wave velocity from PPG (J. M. Vargas Garcia, M. Bahloul, T.-M. Laleg-Kirati), and a self-adaptive epileptic spike detection mechanism (P. Li, M. Castillo, T.-M. Laleg-Kirati).
Accelerating extremum-seeking convergence via Richardson extrapolation (J.-H. Metsch, J. Neuhauser, J. Jouffroy, T.-M. Laleg-Kirati, J. Reger), and high-gain observer design for nonlinear systems with delayed output measurements (A. Adil, I. N'Doye, T.-M. Laleg-Kirati).
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