Nate Gruver


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PhD Candidate, Computer Science, Courant Institute of Mathematical Sciences
MS & BS, Computer Science, Stanford University

I am currently working on problems in probabilistic machine learning with the goal of improving measures of model uncertainty and tasks like imitation learning. I perform research as part of the CILVR lab at NYU Courant, currently advised by Andrew Gordon Wilson and Kyunghyun Cho.

I received my BS and MS degrees in computer science from Stanford University. While at Stanford, I worked with Stefano Ermon, Mykel Kochenderfer, and Chris Piech. I have worked in industry at Waymo, Apple and Google.

Papers and Projects


Multi-agent Adversarial Inverse Reinforcement Learning with Latent Variables

N. Gruver, J. Song, M. Kochenderfer, S. Ermon. AAMAS 2020.

Adaptive Informative Path Planning with Multimodal Sensing

S. Choudhury*, N. Gruver*, M. Kochenderfer. ICAPS 2020.


Using Latent Variable Models to Observe Academic Pathways

N. Gruver, A. Malik, B. Capoor, C. Piech, M. L. Stevens, A. Paepcke. International Conference on Education Data Mining (2019).


Online Stochastic Planning for Multimodal Sensing and Navigation under Uncertainty

S. Choudhury*, N. Gruver*, M. Kochenderfer. RSS Workshop (2018).

Imitation Learning for Code Generation via Recurrent State Space Embeddings

M. Gomez, N. Gruver, M. Lam, R. Saxena, L. Wang. CS379C course project, 2018.

Incentives in Choosing Academic Research Projects

Y. Carmon, I. Fosli, N. Gruver. CS269I course project, 2018.


Selecting Youtube Video Thumbnails via Convolutional Neural Networks

N. Arthurs, S. Birnbaum, N. Gruver. CS231N course project, 2017.

Quasi-static acoustic tweezing thromboelastometry

R. G. Holt, D. Luo, N. Gruver, D. B. Khismatullin. Journal of Thombosis and Haemostasis, 2017.