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Mathematics - Geometry & Topology | Information Geometry

Information Geometry

Information Geometry

Editor-in-Chief: Shinto Eguchi
Co-Editors: N. Ay; F. Nielsen; J. Zhang

ISSN: 2511-2481 (print version)
ISSN: 2511-249X (electronic version)

Journal no. 41884

About the Editors

Shinto Eguchi (Editor-in-Chief) 

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Shinto Eguchi is a Professor at the Department of Mathematical Analysis and Statistical Inference, Institute of Statistical Mathematics, Japan.
His main focus is on the information geometry associated with divergence, cross entropy and entropy, including generalized minimum divergence and maximum entropy. Further, he is interested in those areas of statistics and machine learning in which information geometry plays a central role, and in statistical applications related to biostatistics, bioinformatics and ecology.

Nihat Ay (Co-Editor) 

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Nihat Ay is Group Leader at the Max Planck Institute for Mathematics in the Sciences in Leipzig, heading the group "Information Theory of Cognitive Systems" which applies tools from information geometry and information theory to the field of embodied cognitive systems. He works on various subjects relevant to biological systems, including theoretical approaches to complexity, robustness, and causality. He is Professor of the Santa Fe Institute and Honorary Professor at the University of Leipzig.
Research areas: Learning and Evolution; Embodied Cognitive Systems; Complexity; Robustness; Information Geometry; Graphical Models and Causality; Information Theory

Frank Nielsen (Co-Editor) 

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Frank Nielsen is a Professor at the Computer Science Department of Ecole Polytechnique, France.
He is investigating computational information geometry with applications to machine learning, artificial intelligence, data science and visual computing.
He teaches Big Data, Machine Learning and High Performance Computing at Ecole Polytechnique (see textbook 'Introduction to HPC with MPI for Data Science', Springer, 2016, http://www.springer.com/fr/book/9783319219028)

Jun Zhang (Co-Editor) 

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Jun Zhang is a Professor of Psychology and Professor of Mathematics at the University of Michigan, Ann Arbor, USA.
His main focus includes generalizing classic information geometry to arbitrary embedding functions, and linking statistical structure to symplectic, (para-)Hermitian, and (para-)Kahler geometry. He is also interested in application of information geometry to physics (e.g., geometric mechanics and thermodynamcis), and to neural-cognitive systems.

For authors and editors

  • Aims and Scope

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    This journal will publish original work in the emerging interdisciplinary field of information geometry, with both a theoretical and computational emphasis. Information geometry connects various branches of mathematical science in dealing with uncertainty and information based on unifying geometric concepts. Furthermore, it demonstrates the great potential of abstract thinking and corresponding formalisms within many application fields.

    Theoretical topics of interest will include, but are not limited to, the Fisher–Rao metric, the Amari–Chentsov tensor, alpha geometry, dual connections, exponential and mixture geodesics, divergence functions, information and entropy functions, convex analysis, Hessian geometry, information projections, q-statistics and deformed exponential/logarithm, algebraic statistics, optimal transport geometry, and related topics.

    The authors and audience of this journal will be interdisciplinary, coming from the many disciplines that inspire the development of information-geometric methods and benefit from their application, including mathematics, statistics, machine learning, neuroscience, information theory, statistical and quantum physics, control theory and optimization, complex networks and systems, theoretical biology, cognitive science, mathematical finance, and allied disciplines.

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