I am a PhD student in the School of Interactive Computing at the Georgia Institute of Technology advised by James Rehg. My main research interests are in computer vision and machine learning. I am interested in studying how the relationship between 3D computer vision, self-supervision, and learning from video can lead to computer vision systems that can generalize to novel concepts and settings with limited data.
In the Summer of 2021 I was a research intern at Facebook Reality Labs. In the Fall of 2018 and Summer of 2019 I was a research intern at Amazon Lab 126.
Before Georgia Tech I did my bachelors at Bard College. I worked on Summer research projects with Michael Lawrence at the Broad Institute and with Sven Anderson at BSRI (Bard Summer Research Institute).
ShapeClipper: Scalable 3D Shape Learning via Geometric and CLIP-based Consistency
CLIP and geometric consistency constraints facilitate scalable learning of object shape reconstruction.
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization
Dense feature-level self-supervised learning from multiple camera views without
any category labels leads to representations that can generalize to novel categories.
NeurIPS 2022 - Poster
The Surprising Positive Knowledge Transfer in Continual 3D Object Shape Reconstruction
Continual learning of 3D shape reconstruction does not suffer from catastrophic
forgetting as much as discriminative learning tasks.
3DV 2022 - Poster
Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Bias
Learning representations to generalize based on 3D shape and then learning
to map images into them leads to improved low-shot generalization.
CVPR 2021 - Poster