Research πŸ”¬

My research has mainly been focused on computer vision, a subfield of artificial intelligence 🧠 that aims to create programs that enable computers to πŸ‘οΈ "see" like humans. I have been lucky enough to work with lots of amazing researchers from around the world πŸ‡ΊπŸ‡ΈπŸ‡¨πŸ‡¦πŸ‡¬πŸ‡§. Below is a sample of what we have accomplished together.

AI Benchmark

GRIT tests the versatility of computer vision systems and sets a shared goal for researchers working on the next generation of AI models. Check out the website, paper, and CVPR talk (timestamp).

Private ML

In sensitive applications such as healthcare or banking, privacy is paramount, preventing the development of data hungry models. I evaluated the usability of privacy preserving machine learning tools.

Smart Photos

To capture better photos, it is helpful to respond to the lighting and materials in a scene. We created a dataset to train such models. Check out the website, paper (ICCV 2019), and video below.

Fast Physics

Efficiently compressing physics simulations speeds up visual effects pipelines. I procedurally generated the 3D scenes used for testing this method. Check out the website, paper (SCA 2018) , and video below.

Perceptual Grouping

One approach to making computers "see" like humans is to leverage our interdisciplinary understanding of the human vision system (psychology, biology, and optics) to design systems that mimic properties that we observe in humans. For example, I have trained a neural network to classify textures as a human would. Additionally, we have created a system that responds to optical illusions by "filling in gaps" similar to way a human does. Check out the website, paper (BMVC 2021), and presentation below.