Chung Kim is an experienced software engineer and student researcher at UC Berkeley AUTOLAB with eight years of hands-on experience spanning robotics, systems engineering, and visualization for ML research. He combines a strong academic background (BS in EECS + Mechanical Engineering and an MS in Computer Science from UC Berkeley) with practical industry internships at Microsoft and Corelight where he built robust tooling for storage migrations, log processing, and test infrastructure. At nerfstudio-project he contributed ML-focused visualization and rendering features—improving viewer UX, depth displays, and PCA colormaps—demonstrating a knack for turning complex NeRF research into usable tools. His robotics pedigree includes leadership roles in competitive teams and founding a FIRST team, reflecting both technical depth and project management skills. Based in Cupertino, he brings a disciplined, data-driven approach to prototyping and debugging large codebases while staying focused on the user-facing aspects of research software. Colleagues would note his uncommon blend of systems engineering, visualization expertise, and hands-on robotics experience.
7 years of coding experience
5 years of employment as a software developer
De Anza College
Bachelor of Science - BS, EECS (Electrical Engineering & Computer Sciences) + Mechanical Engineering, 3.88/4.00, Bachelor of Science - BS, EECS (Electrical Engineering & Computer Sciences) + Mechanical Engineering, 3.88/4.00 at UC Berkeley College of Engineering
High School, 3.93/4.00 (Unweighted), High School, 3.93/4.00 (Unweighted) at Monta Vista High School
Contributions:19 reviews, 21 PRs, 43 pushes in 1 year 9 months
Contributions summary:Chung primarily contributed to the `nerfstudio` project by implementing and refining features related to the viewer and rendering capabilities. Their work includes adding a PCA colormap for images, integrating scene click functionalities, and improving the display of depth information, indicating involvement with visualization techniques common in NeRF research. They also addressed various bugs within the viewer and enhanced the user interface by adding features like rectangle selection and adding options for visible GUI elements. The user demonstrates a strong understanding of the visualization aspects of the NeRF framework and its related components.
Contributions:5 reviews, 10 PRs, 29 pushes in 4 months
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Chung Kim - Student Researcher at UC Berkeley AUTOLAB