Tyler LaBonte

PhD Student in Machine Learning
Dept. of Industrial & Systems Engineering
Georgia Institute of Technology

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Fellowship Materials

Georgia Tech undergrads: Please contact me! I am happy to chat about research (or anything, really).

About Me

I am a first-year PhD student in Machine Learning at the Georgia Institute of Technology advised by Tuo Zhao and a Machine Learning Research Intern at Microsoft Research advised by Neel Joshi. My work is generously supported by the DoD NDSEG Fellowship.

I received my BS in Applied and Computational Mathematics at the University of Southern California advised by Shaddin Dughmi, where I was a Trustee Scholar and Viterbi Fellow. My senior thesis in convex optimization received the USC Discovery Scholar distinction for exemplary research. During my undergraduate, I was a Machine Learning Research Intern at Google X and Sandia National Laboratories.

I use theory to develop and understand large-scale machine learning models which can be deployed in high-consequence applications. My current focus is optimization and generalization in deep learning, where I seek to reconcile theoretical understanding with empirical phenomena. I also enjoy working on self-supervised and weakly supervised machine learning, particularly for computer vision applications.

In 2021, I was one of two undergraduates to receive both the DoD NDSEG and NSF GRFP fellowships in Computer Science; the other was Ethan Fahnestock. To democratize the fellowship application process, I have made my essays available here.


Journal Articles

  1. Michael C. Krygier, Tyler LaBonte, Carianne Martinez, Chance Norris, Krish Sharma, Lincoln N. Collins, Partha P. Mukherjee, and Scott A. Roberts. Quantifying the Unknown: Impact of Segmentation Uncertainty on Image-Based Simulations. Nature Communications, 12(5414), 2021.


  1. Tyler LaBonte. Finding the Needle in a High-Dimensional Haystack: Oracle Methods for Convex Optimization. Undergraduate Thesis, 2021. USC Discovery Scholar distinction.


  1. Tyler LaBonte, Carianne Martinez, and Scott A. Roberts. We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric Uncertainty. Manuscript, 2019.


  1. DoD NDSEG Fellowship ($170,000)
  2. NSF Graduate Research Fellowship ($138,000—declined)
  3. USC Discovery Scholar (research distinction for <100 USC graduates)
  4. USC Trustee Scholar ($225,000)
  5. USC Viterbi Fellow ($24,000)

Industry Experience

  1. Machine Learning Research Intern, Microsoft Research (2021)
  2. Machine Learning Research Intern, Google X (2020)
  3. Machine Learning Research Intern, Sandia National Labs (2019)
  4. Machine Learning Engineer Intern, Air Force Research Lab (2018)


  1. Pratik Deolasi—Georgia Tech undergrad (2021)
  2. Rishit Mohan Ahuja—Georgia Tech undergrad (2021)


  1. USC CSCI 270: Intro to Algorithms and Theory of Computing (2021)
  2. USC Center for AI in Society: Introduction to Machine Learning (2020)
  3. USC CSCI 170: Discrete Methods in Computer Science (2019)

Service and Leadership

  1. Projects Lead, USC Center for AI in Society (2019)
  2. Associate Director of Robotics Outreach, USC Viterbi K-12 STEM Center (2018)
  3. Volunteer VEX Robotics Mentor, USC Viterbi K-12 STEM Center (2017—2018)