I'm a 2nd year PhD student in Computer Science and Robotics at Purdue University's Computational Motion, Manipulation, and Autonomy Lab, advised by Zachary Kingston.

In my research I study one of the most basic questions in robotics: how can we make robots that are aware of uncertainty, and act in the world despite it, safely and reliably, even as things change? Humans do this naturally, but many of today's robots, especially those driven by large-scale AI systems, make confidently wrong predictions in new situations, sometimes with costly consequences. I address this gap by combining generative modeling, probabilistic programming, and Bayesian inference.

Previously, I was a research engineer at MIT-IBM, contributing to DARPA's Machine Common Sense program. I also built ModPPL, a dynamic probabilistic programming language in Rust.

At MIT, I completed my undergraduate degree in Computer Science & Physics and my M.Eng. in EECS. There, I was advised by Vikash Mansinghka in the Probabilistic Computing Project, where I worked on probabilistic programming for 3D scene perception. As an undergraduate, I researched models of intuitive physics in the Computational Cognitive Science Group.

News

Publications

  • Barrier Functions Enable Safety-Conscious Force-Feedback Control
    Charles Dawson, Austin Garrett, Falk Pollok, Yang Zhang, Chuchu Fan
    arXiv preprint, 2022
    arXiv
  • 3DP3: 3D Scene Perception via Probabilistic Programming
    Nishad Gothoskar, Marco Cusumano-Towner, Ben Zinberg, Matin Ghavamizadeh, Falk Pollok, Austin Garrett, Joshua B. Tenenbaum, Dan Gutfreund, Vikash K. Mansinghka
    NeurIPS, 2021
    arXiv
  • Infrastructure for Modeling and Inference Engineering with 3D Generative Scene Graphs
    Austin Garrett
    M.Eng. Thesis, MIT, 2021
    code

Software

  • ModPPL

    Experimental modular probabilistic programming language in Rust, with modeling and inference separated by a generative function trait interface.

    codevideo
  • PointCNN

    PyTorch implementation of PointCNN for point-cloud learning.

    code

All repositories →

Writing

  • lazy-monad.rs ↗
    May 30th 2026

    An exploration in code of functional type class hierarchies in Rust using GATs and lifetime parameters, with reverse-mode and forward-mode automatic differentiation.

  • Importance Sampling over Discrete Structures
    March 13th 2020

    A short proof of the consistency of importance sampling when a model contains discrete choices over structures with different sets of parameters.