- September 21st, 2012
- 1:39 pm
I will be speaking on my research into topological data analysis for political data sets at the University of Edinburgh, at 4:10pm on 15th October, in Room 6206, James Clerk Maxwell Building.
Data Analysis on politics data
Data analysis has played a growing role in politics for many years now; analyzing polling data to predict outcomes of elections is perhaps the most well-known application.
A different approach that has gotten more and more traction lately is to analyze the voting behaviour of elected representatives as a way to understand the inner workings of parliaments, and to monitor the elected representatives to make sure they behave as they once promised. Sites like GovTrack and VoteView bring machine learning and data analysis tools to the citizens, and illustrate and visualize the groupings and behaviour in political administration.
ATMCS 5 – Algebra and Topology, Methods, Computing, and Science
This meeting will take place in the period July 2-6, at the ICMS in Edinburgh, Scotland. The theme will be applications of topological methods
in various domains. Invited speakers are
J.D. Boissonnat (INRIA Sophia Antipolis) (Confirmed)
R. Van de Weijgaert (Groningen) (Confirmed)
N. Linial (Hebrew University, Jerusalem) (Confirmed)
S. Weinberger (University of Chicago) (Confirmed)
S. Smale (City University of Hong Kong) (Confirmed)
H. Edelsbrunner (IST, Austria) (Confirmed)
E. Goubault (Commissariat à l’énergie atomique, Paris) (Confirmed)
S. Krishnan (University of Pennsylvania) (Confirmed)
M. Kahle (The Ohio State University) (Confirmed)
L. Guibas (Stanford University) (Confirmed)
R. Macpherson (IAS Princeton) (Tentative)
A. Szymczak (Colorado School of Mines) (Confirmed)
P. Skraba/ M. Vejdemo-Johansson (Ljubljana/St. Andrews) (Confirmed)
Y. Mileyko (Duke University) (Confirmed)
D. Cohen (Louisiana State)
V. de Silva (Confirmed)
There will be opportunities for contributed talks. Titles and abstracts should be send to Gunnar Carlsson at firstname.lastname@example.org.
- January 4th, 2011
- 8:09 pm
This is a typed up copy of my lecture notes from the seminar at Linköping, 2010-08-25. This is not a perfect copy of what was said at the seminar, rather a starting point from which the talk grew.
In my workgroup at Stanford, we focus on topological data analysis — trying to use topological tools to understand, classify and predict data.
Topology gets appropriate for qualitative rather than quantitative properties; since it deals with closeness and not distance; also makes such approaches appropriate where distances exist, but are ill-motivated.
These approaches have already been used successfully, for analyzing
- physiological properties in Diabetes patients
- neural firing patterns in the visual cortex of Macaques
- dense regions in of 3×3 pixel patches from natural (b/w) images
- screening for CO2 adsorbative materials