Neil G. Marchant

PhD Candidate · School of Computing and Information Systems · University of Melbourne

I’m a PhD candidate in the Artificial Intelligence group at the University of Melbourne, supervised by Ben Rubinstein and Rebecca Steorts (at Duke University).

My PhD research applies statistical machine learning to problems in record linkage, including active labelling of ground truth and propagation of linkage uncertainty. More broadly, I am interested in human-in-the-loop machine learning systems, probabilistic graphical models, sampling and approximate inference. I’m a strong believer in the benefits of reproducible research, and strive to release open source code where possible.


Sep 17, 2020 Our paper on distributed Bayesian entity resolution was accepted for publication in the Journal of Computational and Graphical Statistics.
Jul 22, 2020 I presented my PhD research in a completion seminar—one of the final milestones of my candidature.
Aug 13, 2019 I’m presenting our work on distributed Bayesian entity resolution at the Conference on Current Trends in Survey Statistics at the National University of Singapore.