Speaker |
Stephen Roberts |
|---|---|
Affiliation |
Oxford University |
Date |
Friday, 28 November 2014 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
Roberts G08 (Sir David Davies lecture theatre) |
Event series |
Jump Trading/ELLIS CSML Seminar Series |
Abstract |
Astronomy has become a data-intensive science. In this talk we highlight our recent work in using scalable machine learning methods for astronomical data analysis and exploration. We consider the role of scalable Bayesian inference for inferring and removing systematic corruptions in data, detection of astrophysical transients & large-scale aggregation of information in a citizen science project. Bio: Stephen's main area of research lies in machine learning approaches to data analysis. He has particular interests in the development of machine learning theory for problems in time series analysis and decision theory. Current research applies Bayesian statistics, graphical models and information theory to diverse problem domains including astronomy, mathematical biology, finance and sensor networks. He leads the Machine Learning Research Group, is a Professorial Fellow of Somerville College and a faculty member of the Oxford-Man Institute. speaker's webpage: http://www.robots.ox.ac.uk/~sjrob/ Video of the talk here. |
Biography |