David Sweeney

COMPUTATIONAL ASTROPHYSICIST

Who Am I?

I’m a postdoctoral fellow at the University of Sydney.

Prior to this, I was invited to the European Southern Observatory (ESO) as an Early Career Visiting Scientific Visitor to impart what wisdom I could about the role of computational physics in uncovering the mysteries of the universe. During my PhD, I was fortunate enough to be awarded a Fulbright Scholarship, which I took to the University of California, Irvine to collaborate with their Department of Software Engineering and Informatics.

Broadly speaking, I use machine learning, Bayesian inference and physics-based modelling to answer a range of questions. For example:

Where is the Galactic Underworld, the Milky Way's population of black holes and neutron stars?
How can we efficiently search microlensing events to find it?
How do we best implement semi-supervised learning to take advantage of the flood of astronomical survey data?
Can we detect the greenwashing of emissions reported by companies and nations?

And when I'm not attempting to answer those questions, I'm working on translating a clever piece of photonics into a usable methane sensor for agriculture and waste management.

I have a long history of teaching computer science, data science and machine learning. As well as teaching a dozen units units, I've written tutorials ranging from introductory Python material, Bayesian inference and machine learning for honours-level Physicists to the capstone unit for the University of Sydney's Business Analytics masters program.

I currently supervise three PhD students, Stephanie Rossini-Bryson, Max Charles and Matteo Colombo, who work on identifying exoplanets with a nulling interferometer, using automatic differentiation to reconstruct images from the AMI instrument aboard the James Webb Space Telescope and finding methane on exoplanets with photonics, respectively.

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