Topological and Geometrical Structures of Information (TGSI), August 2017

Manifold Learning, From Euclid to Riemann Workshop (PC, ICCV'17)

Differential Geometry on Computer Vision and Machine Learning(PC, CVPR'17)

Entropy 2018 (invited talk, Barcelona)

- Research areas: Data science, Geometric science of information, Big Data, Machine Learning, High-dimensional statistics.

Challenge: Current Data Science is often biased by inappropriate representation, and adhoc data/model goodness-of-fit or distances (poster).

Goal: Learn appropriate data/model geometry for*Intrinsic Data Science*with principled distances.

How to: By building a theory of*Computational Information Geometry*...

... and showcase it in applications arising in data science, learning, intelligence, vision and imaging. [+]

[publications] (books | arxiv | DBLP) [lectures] [video] [slides] [software] [services/events/editorship][+] Recent highlights

- Teaching:

- Introduction to HPC with MPI for Data Science, ISBN 978-3-319-21902-8, Springer, 2016.
- A Concise and Practical Introduction to Programming Algorithms in Java, ISBN 978-1-84882-338-9, Springer, 2009. (also translated in Chinese)
- Visual Computing: Geometry, Graphics, and Vision, ISBN 1-58450-427-7, Charles River Media, 2005.
- Ecole Polytechnique: INF442, INF517, INF591, PSCs. (past teaching: INF311, INF555)

[+] Contact

© Frank Nielsen, September 2016. (old web page)