Projects
We use deep learning and computer vision to detect individual ice floes in aerial imagery with the aim to understand the frequency distribution of sea ice by their area.
Photo by Cassie Matias on Unsplash
Leveraging large language models (BERT) for natural language processing of social media posts by Australian MPs to detect corruption. In collaboration with Sean Johnson of Openpolitics.
Photo by Aditya Joshi on Unsplash
An analysis of the frequency of amendments of the twenty largest Federal legislative bills since their inception. This project involved the automated acquisition of the documents and natural language processing of nearly twelve gigabytes of text data. The project was led by A/Prof Lisa Crawford of the Law School, University of Sydney.
Photo by Tingey Injury Law Firm on Unsplash
We use fluid dynamics to simulate realistic images of the ocean surface with the triangular wave patterns behind moving objects (kelvin wakes) in water overlayed on wind generated waves. A deep learning (u-net) algorithm was trained to detect the presence absence of the kelvin wakes in the images. The prediction skill is further improved with the use of diffusion models to improve the signal to noise ratio between the kelvin wakes and the wind generated waves. This project was in collaboration with Naval Group Australia.
Photo by Martin Adams on Unsplash
