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Hi I'm John! 👋

I'm a postdoc in the Coley Research Group at MIT, previously 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:

📄 Code for Papers

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".

🎓 Teaching

ML for Molecules is a notebook I wrote for a tutorial at the Summer school for Machine Learning in Bioinformatics, Moscow, 2020.

🧺 Misc

GNN is a library for graph neural networks in PyTorch.

(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).


  1. Tutorial for "Advances in machine learning for molecules" (for Summer School for Machine Learning in Bioinformatics,

    Jupyter Notebook 10 2

  2. electro Public

    Core code for the paper "A Generative Model For Electron Paths" (

    Jupyter Notebook 13 3

  3. Code for our paper "A Model to Search for Synthesizable Molecules" (

    Python 58 21

  4. 1
    import os
    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data as mnist_input_data

10 contributions in the last year

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October 2021

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