You can get in touch with me at mailto:[email protected].

My academic interests are mostly machine learning, especially 'big learning' - distributed and parallel methods applied to large-scale problems. I've had experience implementing large-scale machine learning systems at Facebook, and I'm comfortable working in C++, Python, Go, R, Haskell, Rust, MatLab, and other languages. See my GitHub profile for some of my open-sourced code - for example,

• A highly efficient Go implementation of random forests and boosted decision trees.
• A Python implemention of Microsoft's AdPredictor (a Bayesian online probit regression model).
• The first Haskell implementations (that I can find) of Hopfield networks and gradient boosted decision trees.
• A short and sweet Python kernel SVM implementation (for pedagogical purposes).

I've made some signficant contributions to larger open-source projects such as

• Improving performance of regression algorithms in scikit-learn,

and some minor ones, such as

• Cleanups to the Torch deep learning library.
• Fixing crashes when parsing malformed YAML in Clang,
• Minor cleanups to Chromium and LLVM

I'm currently a member of the Facebook AI Research Group (FAIR), working on large-scale problems in machine intelligence. I recently finished graduate school in mathematical statistics at Trinity College, Cambridge, graduating with distinction (the highest grade). Before graduate school, I worked on machine learning systems at Facebook from April 2012 until starting graduate school in October 2013, working on all aspects of the advertising machine learning platform, from feature engineering to the inference platform to the real-time serving system.

Before Facebook, I did my honours degree in mathematics at the University of Sydney, graduating with first class honours and the university medal in mathematics, as the student with the highest GPA in the Faculty of Science. My honours thesis was on advanced Levy process models for multivariate credit risk, supervised by Marek Rutkowsi. I interned and then worked at Goldman Sachs in Sydney as a quant, first on the FICC structuring and then algorithmic trading in the last year of my degree.