Deep Learning

A novel application of passive GPS signals (GNSS-R) picked up by cube satellites to detect the location and age of Antarctic sea ice. A simple Bayes rule based update strategy has been implemented to improve estimates of various sea ice types using both a gradient boosting machine learning model and a robust mixture discriminent analysis statistical model as the likelihood.

Probabilistic deep learning (bidirectional LSTMs) with monte-carlo dropout for filling up to three month long gaps in daily temperature observations around the Sydney harbour and the East coast of Australia. As part of a postdoctoral role in the UNSW coastal oceanography group, part of the Integrated Marine Observing System Australia.