Projects |

Ax

Thu, 01 Aug 2024 00:00:00 +0000

is an open-source platform for developed by Meta’s Adaptive Experimentation team. It provides a unified interface for , , multi-objective, and constrained optimization, built on top of .

I contribute to Ax as part of my work at Meta, with a particular focus on sample-efficient methods for , capacity management, and scaling-law-based modeling. Co-first author on (AutoML 2025).

GPflux

Wed, 01 Sep 2021 00:00:00 +0000

is a TensorFlow/Keras framework for Deep , developed at Secondmind Labs. It builds on and exposes Deep GP layers as familiar Keras building blocks, making it easier to compose deep .

Contributed during my doctoral student researcher appointment at Secondmind Labs, alongside Vincent Dutordoir, ST John, and other members of the lab.

BORE

Thu, 01 Jul 2021 00:00:00 +0000

is the reference implementation of (Tiao et al., ICML 2021). It recasts the acquisition function in as a probabilistic classification problem via , sidestepping the analytical-tractability constraints of conventional surrogate-based methods.

Developed with Aaron Klein.

AutoGluon

Sun, 01 Sep 2019 00:00:00 +0000

is an open-source toolkit from AWS that automates ML for tabular, image, and text data. During my AWS Berlin internship I was a core developer of the -based searcher module — described in and later forming the basis of .

Aboleth

Thu, 01 Jun 2017 00:00:00 +0000

is a minimalistic TensorFlow framework for scalable and approximation, focused on techniques. Built at CSIRO Data61 with Daniel Steinberg and Lachlan McCalman.

revrand

Sun, 01 Nov 2015 00:00:00 +0000

is a Python library for scalable generalized linear models with random feature approximations and stochastic gradient . Built at NICTA with Daniel Steinberg, Lachlan McCalman, Alistair Reid, and Simon O’Callaghan.