Biography

I am a PhD student at the University of Washington in the Baker Lab. My research lies at the intersection of deep learning and protein design, particularly in the context of small molecules. I am particularly interested in geometric deep learning and diffusion modeling.

Interests
  • Machine Learning
  • Protein Design
  • Explainable AI
Education

  • PhD in Computer Science, Current

    University of Washington

  • B.E. in Computer Science, 2017

    University of Michigan

Recent Publications

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Saige Rutherford, Pascal Sturmfels, Mike Angstadt, Jasmine Hect, Jenna Wiens, Marion I van den Heuvel, Dustin Scheinost, Chandra Sripada, Moriah Thomason (2021). Automated Brain Masking of Fetal Functional MRI with Open Data. Neuroinformatics.

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Joseph D Janizek, Pascal Sturmfels, Su-In Lee (2021). Explaining explanations: Axiomatic feature interactions for deep networks. Journal of Machine Learning Research.

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Ivan Evtimov, Pascal Sturmfels, Tadayoshi Kohno (2021). FoggySight: A Scheme for Facial Lookup Privacy.. Proc. Priv. Enhancing Technol..

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Gabriel Erion, Joseph D Janizek, Pascal Sturmfels, Scott M Lundberg, Su-In Lee (2021). Improving performance of deep learning models with axiomatic attribution priors and expected gradients. Nature Machine Intelligence.

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Projects

Path Explanations

Path Explanations

A GitHub package to explain not only which features are most important to a neural network prediction, but also how those features interact with each other.

Attribution Priors

Attribution Priors

A GitHub package to regularize a neural network’s feature attributions during training in order to encode human-level prior knowledge into the training process.