National Center for Scientific Research at the LTCI lab (Laboratoire de Traitement et Communication de l'Information), Paris, France
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Biography:Jean-François Cardoso (born 1958) is Directeur de Recherche with the French CNRS (Centre National de la Recherche Scientifique) at the LTCI lab (Laboratoire de Traitement et Communication de l'Information, Paris). Since 1989, he has been extensively working on all aspects of blind source separation and independent component analysis. In 2001, he joined the Planck collaboration, a cosmological mission of the European Spatial Agency, for the analysis of Planck data. He developed in particular the blind component separation method which extracted a full-sky high-resolution map of the Cosmic Microwave Background out of the 9 Planck frequency channels. In 2014, he was awarded the CNRS silver medal.
Abstract:In 2013, the Planck collaboration from the European Spatial Agency released its first results and products, including a full-sky high-resolution map of the Cosmic Microwave Background (CMB) which made the headlines of newspapers worldwide. Indeed, that image is quite literally a snapshot of our Universe in its infancy, when it was a promising but fragile baby, only 380.000 years old. The CMB map is obtained by combining the 9 frequency channels of the Planck satellite in order to separate the precious relic radiation from many other astrophysical emissions. This is a source separation issue which, as it turned out, was best solved by a blind method -- in other words, by Independent Component Analysis-- carefully crafted to deal with the uncertainties in the sky and wild SNR conditions.
The talk will start with an introduction to Big Bang theory, the standard cosmological model, so beautifully supported by Planck results. I will then describe the Planck satellite and sketch its data processing pipeline up to the inference of the cosmological parameters. I will focus on my main contribution to it: blind separation of the CMB from all the other emissions by an ICA algorithm based on a Gaussian spectral likelihood. I will explain why, in my view, the CMB is not sparse in any useful way.
Section for Cognitive Systems, DTU Compute, Technical University of Denmark, Denmark
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Biography:Lars Kai Hansen received the Ph.D. degree in physics from the University of Copenhagen in 1986. Since 2000 he has held the professorship in digital signal processing at the Technical University of Denmark, where he also heads the Cognitive Systems Section. His research focuses on adaptive signal processing and machine learning with applications in biomedicine, digital media and cognitive systems. He has published more than 280 contributions on these subjects in journals, conferences, and books. The major contributions include introduction and analysis of neural network ensembles (1990-96), adaptation of predictive machine learning methods for neuroimage analysis (1994-), and defining the concept of cognitive components (2005-). His research has been generously funded by the Danish Research Councils, The European Commission, the US National Institutes of Health, and private foundations. He is a present member of the Danish Council for Independent Research - Technology and Production Sciences, and has been active in numerous international research evaluation committees including the Academy of Finland, Vetenskabsraadet, Sweden, INRIA, France, and the US National Institutes of Health. In 2011, he served as Cátedra de Excelencia at Universidad Carlos III de Madrid.
Abstract:EEG based real-time imaging of human brain function has many potential applications including brain based services and personal precision medicine. In mobile applications real-time imaging is attractive as an element in systems for personal state monitoring and well-being, and in clinical settings for patients who may need imaging under quasi-natural conditions. I will discuss our smartphone based imaging system and current efforts to improve algorithms and interfaces (Stopczynski et al., 2014). Real-time imaging is an interesting challenge at the interface between signal processing and machine learning. Problems related to the ill-posed nature of the EEG imaging escalate in mobile real-time systems and new algorithms and the use of strong priors informed by context and by personal meta-data may be necessary to succeed. I will describe our current research towards fast, robust, yet accurate imaging algorithms. Our work takes a Bayesian approach to sparse activation patterns and includes a Markovian prior for temporally sparse solutions and new inference schemes based on message passing such as the "Variational Garrote" (Kappen, 2011).
KU Leuven, Dept. of Electrical Engineering-ESAT, Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, and iMinds Medical IT Department, Leuven, Belgium
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