Tae Choe
Distinguished Engineer at NVIDIA
United States
Summary
🤩
Rockstar🎓
Top SchoolTae Choe is a distinguished engineer and perception architect with over eight years of industry experience focused on autonomous driving, sensor calibration, 3-D reconstruction, and scene understanding. He has led perception teams at Baidu’s Apollo and now drives advanced AR/VR-based simulation for vehicle perception at NVIDIA, blending research-grade rigor with production deployments. His open-source contributions to the widely used Apollo platform include implementing the CIPV module—demonstrating deep expertise in camera-based perception, lane reasoning, and sensor fusion. Tae’s background spans academia and defense-funded research on geo-registration, Image2Text, and multi-camera tracking, giving him rare breadth across medical imaging, surveillance, and automotive vision. Known for turning complex sensing math into robust systems, he pairs a PhD-level foundation with hands-on architecture and team leadership.
8 years of coding experience
21 years of employment as a software developer
MS, Computer Science, MS, Computer Science at University of Southern CaliforniaMS, Computer Science, MS, Computer Science at Pohang University of Science and TechnologyBE, Computer Engineering, BE, Computer Engineering at Pusan National University
Korean, Japanese, English
Github Skills (5)
computer-vision10
c-language10
cprogramming-language10
autonomous-driving10
sensor-fusion9
Programming languages (1)
Github contributions (5)
ApolloAuto/apollo
Feb 2018 - Sep 2019
An open autonomous driving platform
Role in this project:
Back-end Developer
Back-end DeveloperContributions:218 commits, 141 PRs, 122 pushes in 1 year 7 months
Contributions summary:Tae primarily contributed to the development of the CIPV (Closest-in-Path Vehicle) module within the Apollo autonomous driving platform, implementing and refining its core functionalities. Their commits involved adding, modifying, and fixing code related to calculating distances, determining the ego lane, and identifying the closest edge of objects, especially lane lines. They also integrated the CIPV module with existing systems and enabled its use through the camera process subnode. The user's contributions show a focus on perception algorithms and sensor fusion.
autonomousmachine-learningautonomyautonomous-drivingapollo
techoe/apollo
Feb 2018 - Sep 2018
An open autonomous driving platform
Contributions:119 pushes, 32 branches in 6 months
autonomousmachine-learningautonomous-drivingautopilotmavlink
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