Dan has more than 10 years experience in researching and engineering machine learning algorithms and systems. He has published in top machine learning and computer vision conferences and journals, with a primary focus on efficient techniques for Bayesian inference in machine learning. Bayesian inference is a useful tool for ascertaining the confidence associated with a prediction from a machine learning algorithm. This is of particular relevance when decisions made based on these predictions affect people.
He has also helped start and actively maintains several open source software projects that include implementations of machine learning algorithms, as well as systems for large scale, distributed, spatial prediction.
Dan has worked as a researcher and engineer in CSIRO’s Data61, NICTA and at the University of Sydney. He obtained his PhD in 2013 at the Australian Centre for Field Robotics, University of Sydney.