Frank NIELSEN (PhD 1996, HDR 2006 in computer science)
Challenge: Current Data Science is often biased by inappropriate representation,
and adhoc data/model goodness-of-fit or distances
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.
(books | arxiv | DBLP)
- 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)
© Frank Nielsen, September 2016. (old web page)