Most recently I was a postdoc in the Coley Research Group at MIT. Before that, I was a PhD student in Machine Learning at the University of Cambridge/Max Planck Institute for Intelligent Systems. On my GitHub you can find the code for my papers as well as my implementations for some general ML algorithms. Some highlights include:

Synthesis DAGs is code for our paper "Barking up the right tree: an approach to search over molecule synthesis DAGs".
Molecule Chef is code for our paper "A Model to Search for Synthesizable Molecules".
Electro is code for our paper "A Generative Model For Electron Paths".
GPDNN (and here) is a gist demonstrating some of the key concepts of our paper "Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks".

ML for Molecules is a notebook I wrote for a tutorial about ML for molecules.
GNN is a library for graph neural networks in PyTorch.
kriggie is a playground for exploring Gaussian processes.

(Note some of this work has been done in collaboration or been heavily inspired by work elsewhere, which I have tried to highlight on the repos' READMEs).