Nate Gruver, PhD

I am a machine learning researcher focusing on generative modeling and scientific discovery.

I completed my PhD at NYU Courant advised by Andrew Gordon Wilson and working closely with Kyunghyun Cho. I received a BS/MS in computer science from Stanford University, where I worked with Stefano Ermon, Mykel Kochenderfer, and Chris Piech.

During my training, I had several internships in industry, including generative modeling of crystals and proteins at FAIR, driver behaviour modeling at Waymo, and applying ML to kernel virtual machines at Google Cloud.

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My graduate work on deep learning and generative modeling had the following themes:

1. Understanding the relationship between large-scale pretraining and inductive biases [1, 2]
2. Generative modeling for protein and materials design [3, 4]
3. Combining generative models with uncertainty estimates [5, 3]