Update: I am currently at the startup FVL56, Inc.
At the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) I was a postdoc working with Tommi Jaakkola and Regina Barzilay as part of the MLPDS consortium. We worked to accelerate the process of molecular and macromolecular design and discovery with structured machine learning models.
Previously, I was a PhD student in Debora Marks' lab at Harvard Medical School. We focused on building probabilistic models of deep evolutionary variation in proteins and RNAs to better decode how the structure, function, and physiological relevance of biomolecules is encoded in their genetic sequence.
[Google Scholar] [Github]
Selected Preprints and Publications
Generative Models for Graph-Based Protein Design
John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi Jaakkola
Neural Information Processing Systems (NeurIPS), 2019
Learning protein structure with a differentiable simulator
John B Ingraham, Adam Riesselman, Chris Sander, Debora S Marks
International Conference on Learning Representations (ICLR), 2019
Deep generative models of genetic variation capture the effects of mutations
Adam Riesselman*, John B Ingraham*, Debora S Marks
Nature Methods, 2018
Mutation effects predicted from sequence co-variation
Thomas A Hopf*, John B Ingraham*, Frank J Poelwijk, Charlotta PI Schärfe, Michael Springer, Chris Sander, Debora S Marks
Nature Biotechnology, 2017
Variational Inference for Sparse and Undirected Models
John Ingraham, Debora Marks
International Conference on Machine Learning (ICML), 2017