Nate Gruver

I am a machine learning PhD student 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.

I spent my last two summers working on generative modeling for applications in chemistry and biology at FAIR. I've also spent time at Waymo, Apple, and Google.

Starting October 2024 I will on the job market. I am primarily looking for industry research positions, but I will also be considering engineering roles and postdoc positions if the team is really exciting. Please send me an email if you see a good fit!

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I work on deep learning and generative modeling with 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]