• The Alignment Project, UK AI Safety Institute (AISI), 2026. Probabilistic Interpretability of Transformers. £85,000.
• Discovery Early Career Researcher Award (DECRA), Australian Research Council (ARC), 2020–2023. A$397,642.
• Unrestricted Gift, Google Research, 2022.

• Approximately Bayes, ICMS, Isle of Skye, May 2026

• Focus Period on Mathematical Science of AI Safety, Sydney Mathematical Research Institute, Nov–Dec 2025. Co-organised with Daniel Murfet and Geordie Williamson.
• Frontiers in Statistical Machine Learning (FSML), IMS, Vanderbilt University, Nashville, Aug 2025. Inaugural workshop.
• Bayesian Learning of Very High-Dimensional Physical Process Models, MATRIX, Creswick, Jun–Jul 2025.
• Deep Learning Down Under, Lorne, Jan 2024. Co-organised with Peter Bartlett. Supported by Google Research.

In Winter 2021, I gave a week-long lecture series on Neural Networks and Related Models as part of the Australian Mathematical Sciences Institute (AMSI) Winter School program, an annual event open to graduate students, early career researchers, and industry members across Australia. The course was an introduction to deep learning as well as some probabilistic models involving neural networks (flow-based models and deep generative models).

You can find my lecture slides here for Part 1 of the course where I covered the following topics:

• An Introduction to Neural Networks: key components of DL pipeline, multilayer perception, forward/backward propagation, computational graphs
• Stochastic Optimization and Extensions
• The Art of Model Training and Regularization: Model selection, weight decay, dropout, initialization
• Convolutional Neural Networks and Recurrent Neural Networks

The second part of the module on deep generative modeling was given by Robert Salomone. You can find his excellent teaching materials here.