Geometric Structures of Information

Frank Nielsen is Professor at the Laboratoire d'informatique de l'École polytechnique, Paris, France. His 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, he 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.

Rho-Tau Embedding of Statistical Models.- A class of non-parametric deformed exponential statistical models.- Statistical Manifolds Admitting Torsion and Partially Flat Spaces.- Conformal attening on the probability simplex and its applications to Voronoi partitions and centroids Atsumi Ohara.- Monte Carlo Information-Geometric Structures.- Information geometry in portfolio theory.- Generalising Frailty Assumptions in Survival Analysis: a Geometric Approach.- Some Universal Insights on Divergences for Statistics, Machine Learning and Articial Intelligence.- Information-Theoretic Matrix Inequalities and Diusion Processes on Unimodular Lie Groups.