PhD (1996) HDR (2006)
ACM senior member
IEEE senior member

Computational information geometry for Imaging and Intelligence

Dreaming digital worlds... My research aims at understanding the nature and structure of information and randomness in data, and exploiting algorithmically this knowledge in innovative imaging applications. For that purpose, I coined the field of computational information geometry (computational differential geometry) to extract information as regular structures whilst taking into account variability in datasets by grounding them in geometric spaces. Geometry beyond Euclidean spaces has a long history of revolutionizing the way we perceived reality: Curved spacetime geometry sustained relativity theory and fractal geometry unveiled the scale-free properties of Nature. In the digital world, geometry is data-driven and allows intrinsic data analytics by capturing the very essence of data through invariance principles without being biased by such or such particular data representation.

Information geometry portal

interests ::= ((computational | information) geometry) | (computer |
(graphics | vision)) |  (machine | (learning|vision|teaching)) | optimization

Essentially, all models are wrong, but some are useful George E. P. Box (Statistician)
One geometry cannot be more true than another; it can only be more convenient Jules H. Poincaré (Universalist)

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