Prof. Hervé Glotin - Institut Universitaire de France, CNRS LSIS, FR
H. Glotin is a Professor at the Insitut Universitaire de France and Univ. of Toulon, in the Systems & Information Sciences CNRS lab. where he is leading the DYNI team towards stochastical multimodal information retrieval. He received his master in computer science from University P. & M. Curie Paris VI. He proposed in his master thesis the first modelisation of vocalic system evolution, addressing the emergence of a common phonetic code in a society of communicating speech agents using evolutionary learning. His PhD was "Robust adaptive multi-stream automatic speech recognition using voicing and localization cues" at the Inst. of Perceptual Artificial Intelligence (IDIAP) - Lausanne, and at the Inst. of Spoken Communication - Perception Team - Grenoble. In 2000 he was involved as an expert at the Johns Hopkins CSLP lab with the IBM human language team in audiovisual Large Vocabulary Speech Recognition. After two years as a research engineer at CNRS lab on phonology and Semantic analysis, he became an assistant professor at the University of Toulon in 2003. His research focuses on multimodal pattern analysis and retrieval systems, audiovisual indexing, cognitive models and bioacoustics. He is the co-author of one hundred of international refereed articles, and of an international (US, CANADA...) patent on a real-time bioacoustic tracking algorithm. He is leading the CNRS interdisciplinary project (2012-16) 'Scaled Acoustic Biodiversity' involving nearly fifty researchers on bioacoustic indexing.
Prof. Yann LeCun - New York University, USA
Yann received a Diplôme d'Ingénieur from the École Supérieure d'Ingénieur en Électrotechnique et Électronique (ESIEE), Paris in 1983, a Diplôme d'Études Approfondies (DEA) from Université Pierre et Marie Curie, Paris in 1984, and a PhD in Computer Science from the same university in 1987. His PhD thesis was entitled "Modèles connexionnistes de l'apprentissage" (connexionist learning models) and introduced an early version of the back-propagation algorithm for gradient-based machine learning. In 1987, he joined Geoff Hinton's group at the University of Toronto as a research associate. He then joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, NJ in 1988. In 1991, he spend six months with the Laboratoire Central de Recherche of Thomson-CSF in Orsay, France, after which he returned to Bell Labs. Shortly after AT&T's second breakup in 1996, he became head of the Image Processing Research Department, part of Larry Rabiner's Speech and Image Processing Research Lab at AT&T Labs-Research in Red Bank, NJ. In 2002, he became a Fellow of the NEC Research Institute (now NEC Labs America) in Princeton, NJ. He joined the Courant Institute of Mathematical Sciences at New York University as a Professor of Computer Science in 2003. He was named Silver Professor in 2008. Yann LeCun has been associate editor of PLoS ONE (2008-present), IJCV (2003-present), IEEE Trans. PAMI (2003-2005), Pattern Recognition and Applications, Machine Learning Journal (1996-1998), IEEE Transactions on Neural Networks (1990-1991). Yann LeCun has published over 130 technical papers and book chapters on machine learning. He is leading the Computational and and Biological Learning Lab at NYU.
Prof. Stéphane Mallat - Département d'Informatique - École Normale Supérieure, Paris, FR
Stéphane G. Mallat made some fundamental contributions to the development of wavelet theory in the late 1980s and early 1990s. He has also done work in applied mathematics, signal processing, music synthesis and image segmentation.
Specifically, he collaborated with Yves Meyer to develop the Multiresolution Analysis (MRA) construction for compactly supported wavelets, which made the implementation of wavelets practical for engineering applications by demonstrating the equivalence of wavelet bases and conjugate mirror filters used in discrete, multirate filter banks in signal processing. He also developed (with Sifen Zhong) the Wavelet transform modulus maxima method for image characterization, a method that uses the local maxima of the wavelet coefficients at various scales to reconstruct images.
He introduced the scattering transform that constructs invariance for object recognition purposes. Mallat is the author of A Wavelet Tour of Signal Processing (ISBN 012466606X), a common text in some applied mathematics and engineering courses.
Prof. Ofer Tchernichovski - Hunter College - CUNY, NY, USA
Ofer Tchernichovski is a professor at Hunter College - CUNY. His research uses the songbird to study mechanisms of vocal learning. Like early speech development in the human infant, the songbird learns to imitate complex sounds during a critical period of development. The adult bird cannot imitate any more - we do not know why. His lab studies the animal behavior and dynamics of vocal learning and sound production across different brain levels. The lab aims to uncover the specific physiological and molecular (gene expression) brain processes that underlie song learning. He has extensive publications in Nature and Science as Nature Letter Vol 459, 28 May 2009, "De novo establishment of wild-type song culture in the zebra finch".
Prof. Thierry Artières - Machine Learning and Information Retrieval (MALIRE) Research Team - Computer Science Lab (UMR CNRS LIP6) - Univ. Pierre et Marie Curie (UPMC), Paris, FR
He received his PhD on Neural Predictive Models Applied to Speaker Recognition in September 1995 at Paris Sud university. Then he joined University Cergy Pontoise in September 1996 and the Computer Science Lab (LIP6) in University Paris 6 in September 1999. He is currently working in the Connexionnist group headed by Professor Patrick Gallinari.
He is member of the Pascal network of excellence. His research activities concern statistical machine learning with a special interest on sequential data and signals. He focused much of his work on statistical models for sequential data like Hidden Markov Models and Hidden Markov Trajectory Models, Dynamic Bayesian Networks, Hierarchical Hidden Markov Models, Probabilistic Grammars, Conditional Markov Random Fields.
He is involved in SABIOD concerning the Large Scale bioacoustic classification.
Dr. Xanadu Halkias - CNRS LSIS, USTV, Toulon, FR
Xanadu Halkias received her PhD from the Electrical Engineering Department of Columbia University, NY. Her research focused on advanced signal processing and machine learning as it applies to bioacoustics. She is currently a post-doctorate fellow and Adjunct at the Universite du Sud – Toulon working on machine learning and specifically deep architectures and their applications. She is a primary post-doctorate researcher for the ANR Cognilego project: From pixels to semantics whose goal is to create biologically plausible models for handwritten text recognition and a researcher for the CNRS SABIOD project whose goal is the large scale assessment of ecological biodiversity using multimodal signals.