Frank Nielsen

I investigate the geometric sciences of information with applications ranging from data analytics to machine learning and artificial intelligence. I deal with large high-dimensional, noisy, and heterogeneous datasets that are inherently non-Euclidean. To better understand those datasets and extract both unbiased qualitative and quantitative information, I focus on the novel paradigm and toolbox of computational information geometry.

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[Keywords]
Computational Geometry / Information Geometry / Statistics / Machine Learning / Artificial Intelligence

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Selected Publications

Matrix Information Geometry, F. Nielsen and R. Bhatia (Eds), ISBN 9783642302312, Springer (2013).

k-MLE: A fast algorithm for learning statistical mixture models, F. Nielsen, CoRR 1203.5181, IEEE ICASSP (2012).

Visual computing: Geometry, graphics, and vision, F. Nielsen, ISBN 9781584504276, Charles River Media (2005).

Profile

Frank Nielsen graduated the master in parallel computing from ENS (France), and obtained his PhD in computational geometry at INRIA (France).
He joined Sony CSL (Japan) in 1997 to first conduct research in visual computing and currently investigate the geometric sciences of information with path breaking applications ranging from data analytics to machine intelligence.
He wrote several textbooks and edited books, co-authored 220+ papers, and co-organize the international conference Geometric Science of Information (GSI).
He taught several computer science curricula at Ecole Polytechnique (France), and is a senior member of ACM and IEEE.

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