Frederic Pascal

Frédéric Pascal received the Master’s degree (“Probabilities, Statistics and Applications: Signal, Image et Networks”) with merit, in Applied Statistics from University Paris VII – Jussieu, France, in 2003. Then, he received the Ph.D. degree of Signal Processing, from University Paris X – Nanterre in 2006. From November 2006 to February 2008, he made a post-doctoral position in the SATIE laboratory, at ENS Cachan, France. Between 2008 and 2011 (resp. 2012 – 2013), he was an Assistant Professor (resp. Associate Professor) in SONDRA at CentraleSupélec. In 2012, he obtained a Research Directorship Habilitation (HDR) thesis in Signal Processing from the University of Paris-Sud. Between 2013 and 2014, he was a Visiting Associate Professor in the ECE department at the National University of Singapore. From Jan. 2014, Frederic Pascal is a Full Professor in the L2S laboratory at CentraleSupélec. From Jan. 2017 to Dec. 2019, he was the head of the “Signals and Statistics” group of L2S. From Jan. 2020, he is the coordinator of Artificial Intelligence and Data Science activities at CentraleSupélec. From 2015 to 2017, Frederic Pascal was the Chair of the EURASIP SAT in Theoretical and Methodological Trends in Signal Processing (TMTSP) and he is a member of the IEEE SPS SAM technical committee (Jan. 2015-present). Frederic Pascal serves as an Associate Editor for the IEEE TSP (2015-2018), Elsevier SP (2018-present), and EURASIP JASP (2015-present). His research interests contain estimation, detection, and classification for statistical signal processing and applications. He is the author/co-author of more than one hundred papers in the top journals and conferences in Signal Processing, Image Processing, and Statistics / Machine Learning.

Education

• Habilitation à Diriger des Recherches Université Paris Sud (Paris XI) (2012 — 2012)
• Doctorat Université Paris X Nanterre (2003 — 2006)
• DEA Laure Elie (M2MO) - Statistiques, Probabilités et Finance Université Denis Diderot (Paris VII) (2002 — 2003)

Books

An Overview of Covariance‐based Change Detection Methodologies in Multivariate SAR Image Time Series
Mian, Ginolhac, Ovarlez, Breloy, Pascal
in Change Detection and Image Time Series Analysis 1, Wiley, 2021

A Comparative Study of Statistical-Based Change Detection Methods for Multidimensional and Multitemporal SAR Images
Mian, Pascal
in Advances in Condition Monitoring and Structural Health Monitoring, pp. 189-206, Springer Singapore, 2021

Improving portfolios global performance using a cleaned and robust covariance matrix estimate
Jay, Soler, terreaux, Ovarlez, Pascal, De Peretti, Chorro 2019

Covariance Matrix Estimation in SIRV and Elliptical Processes and Their Applications in Radar Detection
Ovarlez, Pascal, Forster
in Modern Radar Detection Theory, pp. 295-332, Scitech Publishing, 2015

Journal articles

MIMO Filters based on Robust Rank-Constrained Kronecker Covariance Matrix Estimation
Breloy, Ginolhac, Gao, Pascal
Signal process., 187, Elsevier, p. 108-116, 2021

Joint Estimation of Location and Scatter in Complex Elliptical Distributions: A robust semiparametric and computationally efficient R-estimator of the shape matrix
Fortunati, Renaux, Pascal
Journal of Signal Processing Systems, 2021

Special issue on statistical signal processing solutions and advances for data science: Complex, dynamic and large-scale settings
Muma, Ollila, Pascal
Signal process., 182, Elsevier, p. 107892, 2021

A Riemannian Framework for Low-Rank Structured Elliptical Models
Bouchard, Breloy, Ginolhac, Renaux, Pascal
IEEE Transactions on Signal Processing, 69, p. 1185-1199, 2021