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Computational and Statistical Aspects of Topological Data Analysis Workshop
Thu, 23 Mar 2017, 09:30 – Fri, 24 Mar 2017, 17:00 GMT
NOTE: Registration for the event is compulsory; please book to ensure a space.
23 March: 9:30 - 19:00
24 March: 10:00 - 17:00
Topological Data Analysis (TDA) aims to describe the shape of a given dataset without making any prior assumptions or imposing a generative model. The vanguard technique in TDA is persistent homology, which collates the appearance and disappearance of intrinsic features such as components, tunnels and cavities in the data across various scales. Persistent homology has been successfully applied to real-world problems across a stunning diversity of disciplines, including graph reconstruction, signal processing, complex network analysis and disease propagation.
The goal of this two-day workshop is to explore the linear-algebraic optimizations involved in the efficient computation of persistent homology, as well as its interactions with other branches of mathematics and statistics. In particular, we hope to provide:
1. An introduction to the fundamentals of TDA and the popular software packages.
2. An overview of the matrix-based algorithms that compute persistent homology.
3. Interesting links between TDA and other data analysis methodologies.
Ulrich Bauer, TU Munich
Pawel Dlotko, Swansea
Gregory Henselman, Princeton
Michael Kerber, TU Graz
Subramanian Ramamoorthy, Edinburgh
Andrew Ranicki, Edinburgh
Michael Stillman, Cornell
Katharine Turner, EPF Lausanne
Matthew Wright, St Olaf College