Legacy wide-area radio surveys have much science to offer, if we can get useful data out. I developed a machine learning approach to cross-identifying a large number of radio sources, and used this to make radio luminosity functions. In this talk presented at WSU's astronomy machine learning projects meeting, I show some preliminary results.
Next-generation radio telescopes will change radio astronomy. But what are next-generation radio telescopes? And what do we see in the radio sky? A talk presented at Smith's Alternative for the ANU Astronomy Society.
I discuss the science of Sailor Scouts in this comedy talk for the Stromlo Student Seminars 2018.
How does sourcing data from citizen science affect model validation? A talk presented at the Collaborative Conference on Computational and Data Intensive Science 2018 at the Melbourne Conference and Exhibition Centre.
We present a machine learning approach for the task of determining the infrared host galaxies of radio emissions detected in wide-area radio surveys. Our method is trained on both expert cross-identifications and crowdsourced cross-identifications from Radio Galaxy Zoo and applied to observations from the Australia Telescope Large Area Survey.
Using classifiers for things that aren't classification. A talk presented at the 2017 Mount Stromlo Student Seminars at the Australian National University.
A Python package for active learning.
Honours project at the Australian National University on machine learning applications in astronomy.
Very (very) fast introductory scientific Python workshop I ran at Bruce Hall, Canberra.
Very (very) fast introductory Haskell masterclass I ran at the National Computer Science School 2016, at the University of Sydney.
Python implementations of selected inverse reinforcement learning algorithms.