Jun Zhu
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJun Zhu is a Master's student in Applied Economics at UCLA with eight years of cross-functional experience spanning project management, market consulting, data analysis, and field research. With strong academic credentials from Xiamen University and UCLA, Jun blends rigorous quantitative training (3.7+ GPAs) with practical exposure to marketing analytics and engineering consultation. They have hands-on data work experience extracting insights from large product databases to inform promotion strategy and have presented results to stakeholders to guide decision-making. Unusually for an economist, Jun also contributed to the Apollo autonomous driving open-source project as an ML engineer, improving lidar segmentation code and removing dependencies to streamline builds. Seeking full-time roles in public policy, data analysis, or marketing, Jun brings a pragmatic mix of technical problem-solving and stakeholder-focused communication. Based in Los Angeles, they offer a rare combination of field-level consulting experience and machine learning code contributions.
8 years of coding experience
Master of Science (M.S.), Applied Economics, 3.738/4.0, Master of Science (M.S.), Applied Economics, 3.738/4.0 at 美国加州大学洛杉矶分校Bachelor of Arts (B.A.), Economics, 3.81/4.0, Bachelor of Arts (B.A.), Economics, 3.81/4.0 at 厦门大学Certificate of Completion, Summer School, 4.0, Certificate of Completion, Summer School, 4.0 at Colgate University
Chinese, English, Korean
Github Skills (8)
mask-rcnn10
faster-rcnn10
caffe10
lidar10
autonomous-driving10
cprogramming-language9
c-language9
computer-vision9
Programming languages (1)
Github contributions (5)
ApolloAuto/apollo
Jul 2017 - Apr 2018
An open autonomous driving platform
Role in this project:
ML Engineer
ML EngineerContributions:186 commits, 28 PRs, 2 pushes in 8 months
Contributions summary:Jun contributed significantly to the perception module, specifically focusing on the implementation and refinement of a CNN-based segmentation method for lidar data. Their work involved adding the CNNSegmentation method, modifying caffe build files to support the bazel build, and fixing shared pointer bugs within the segmentation code. Furthermore, they were involved in enhancing and debugging the existing code by removing openCV dependency, and added some code style adjustments.
autonomousmachine-learningautonomyautonomous-drivingapollo
jzhuucla/apollo
Jul 2017 - Jul 2017
Contributions:1 push in 1 day
autonomousdrivingmachine-learningautonomous-driving
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