June 2019 – Present
Berlin, Germany

Applied Scientist Intern

Amazon Research

July 2017 – Present
Sydney, Australia

PhD Candidate

University of Sydney

July 2016 – April 2019
Sydney, Australia

Research Engineer

CSIRO Data61

May 2015 – June 2016
Sydney, Australia

Software Engineer

National ICT Australia (NICTA)

November 2013 – February 2014
Sydney, Australia

Research Intern

Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Recent Posts

This post demonstrates how to approximate the KL divergence (in fact, any f-divergence) between implicit distributions, using density ratio estimation by probabilistic classification.

We illustrate how to build complicated probability distributions in a modular fashion using the Bijector API from TensorFlow Probability.

An in-depth practical guide to variational encoders from a probabilistic perspective.

The meshgrid function is useful for creating coordinate arrays to vectorize function evaluations over a grid. Experienced NumPy users will have noticed some discrepancy between meshgrid and the mgrid, a function that is used just as often, for exactly the same purpose. What is the discrepancy, and why does a discrepancy even exist when “there should be one - and preferably only one - obvious way to do it.” 1

Recent Publications

We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent …



Expanding the scope and applicability of variational inference to encompass implicit probabilistic models.


Aboleth is a minimalistic TensorFlow framework for scalable Bayesian deep learning and Gaussian process approximation.

Determinant is a software service that makes predictions from sparse data, and learns what data it needs to optimise its performance.


Revrand is a full-featured Python library for Bayesian generalized linear models, with random basis kernels for large-scale Gaussian process approximations.


COMP9418: Advanced Topics in Statistical Machine Learning (UNSW Sydney)

The course has a primary focus on probabilistic machine learning methods, covering the topics of exact and approximate inference in directed and undirected probabilistic graphical models - continuous latent variable models, structured prediction models, and non-parametric models based on Gaussian processes.

Lab exercise on Gaussian Process Regression, running in JupyterLab.

Lab exercise on Gaussian Process Regression, running in JupyterLab.

This course has a major emphasis on maintaining a good balance between theory and practice. As the teaching assistant (TA) for this course, my primary responsibility was to create lab exercises that aid students in gaining hands-on experience with these methods, specifically applying them to real-world data using the most current tools and libraries. The labs were Python-based, and relied heavily on the Python scientific computing and data analysis stack (NumPy, SciPy, Matplotlib, Seaborn, Pandas, IPython/Jupyter notebooks), and the popular machine learning libraries scikit-learn and TensorFlow.

Students were given the chance to experiment with a broad range of methods on various problems, such as Markov chain Monte Carlo (MCMC) for Bayesian logistic regression, probabilistic PCA (PPCA), factor analysis (FA) and independent component analysis (ICA) for dimensionality reduction, hidden Markov models (HMMs) for speech recognition, conditional random fields (CRFs) for named-entity recognition, and Gaussian processes (GPs) for regression and classification.