Brent Yi
PhD Candidate
Summary
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RockstarBrent Yi is a PhD candidate and experienced software engineer with 11 years of hands-on experience building and improving machine learning and front-end systems. He contributes actively to high-profile academic and open-source projects, including Stanford's CS131 and the cs231n course site, where he shipped practical computer vision assignments and polished responsive web UI. His backend and ML work on Nerfstudio demonstrates attention to numerical stability, memory performance, and code quality in neural rendering projects. Based in the U.S. and rooted in the research community at UC Berkeley, he blends rigorous academic training with production-minded engineering to make complex vision models more reliable and usable.
11 years of coding experience
Github Skills (21)
pytorch10
serf10
python10
css10
responsive-design10
machine-learning10
optimisation10
html10
computer-vision10
optimization10
3d-reconstruction10
website-development9
code-optimization9
segmentation9
image-segmentation9
Programming languages (19)
Github contributions (5)
StanfordVL/CS131_release
Sep 2019 - Nov 2020
Released assignments for the Stanford's CS131 course on Computer Vision.
Role in this project:
ML Engineer
ML EngineerContributions:24 commits, 10 PRs, 28 pushes in 1 year 1 month
Contributions summary:Brent released several assignments related to the CS131 course on computer vision. These releases include code and notebooks for K-Means and HAC clustering, image segmentation, and object detection utilizing techniques such as HOG feature extraction and image pyramids. The contributions involve implementing and testing algorithms for various computer vision tasks, indicating practical application of learned concepts.
computer-vision
nerfstudio-project/nerfstudio
Jul 2022 - Dec 2022
A collaboration friendly studio for NeRFs
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:4 releases, 257 reviews, 29 commits in 5 months
Contributions summary:Brent primarily contributed to the improvement and stability of the NeRFstudio project. They fixed typos and type errors across multiple Python files and implemented code style fixes. They also addressed issues related to numerical stability in the Instant-NGP model, specifically related to truncated exponential functions. Furthermore, they updated dependencies and fixed a memory leak, demonstrating a focus on code quality and performance within the project's machine learning and rendering context.
pytorchphotogrammetrydeep-learningcomputer-vision3d-reconstruction
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