Charles Qi
Member Of Technical Staff at OpenAI
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolCharles Qi is an AI researcher and engineering leader with 11 years of experience building state-of-the-art 3D perception and deep learning systems for autonomous vehicles and robotics. He holds a PhD from Stanford and is a core contributor to foundational point-cloud research (PointNet/PointNet++, Frustum PointNets) with high-impact open-source work such as VoteNet that advanced 3D object detection. Charles has led perception foundation model efforts and productionized large video-to-action models at Waymo, Tesla and now OpenAI, driving robotaxi-scale deployments and real-world GenAI for driving. Equally at home in research and production, he blends novel algorithmic contributions with hands-on engineering—e.g., implementing critical data pipelines, training utilities and evaluation optimizations that enabled academic results to run on cars. Based in the Bay Area, he also mentors the next generation through nonprofit leadership, reflecting a commitment to translating cutting-edge research into real-world impact.
11 years of coding experience
12 years of employment as a software developer
Bachelor of Engineering - BE, Electronic Engineering, Top 5%, Bachelor of Engineering - BE, Electronic Engineering, Top 5% at Tsinghua UniversityHangzhou No.2 High SchoolExchange Program, Exchange Program at Aalto UniversityVisiting Student Researcher, Visiting Student Researcher at Carnegie Mellon UniversityDoctor of Philosophy (PhD), Electrical Engineering, Doctor of Philosophy (PhD), Electrical Engineering at Stanford University
English, Chinese
Github Skills (22)
pytorch10
python10
machine-learning-models10
machine-learning10
3d-object-detection10
data-preprocessing10
image-segmentation10
deeplearning-ai10
deep-learning10
segmentation10
tensorflow10
geometry-processing10
neural-network10
computer-vision10
point-cloud10
Programming languages (4)
Github contributions (5)
charlesq34/pointnet2
Sep 2017 - Nov 2018
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:43 commits, 33 pushes, 1 branch in 1 year 2 months
Contributions summary:Charles contributed significantly to the development of the PointNet++ project, focusing on part segmentation tasks. Their contributions include adding a new model (`pointnet2_part_seg_msg_one_hot.py`) used to produce results reported in the paper. The commits also introduced data augmentation techniques and utilities, specifically for pre-processing ScanNet data, indicating a focus on model training and data handling within the point cloud domain. Furthermore, the user appears to have reorganized the grouping folder and optimized project dependencies.
pytorchautoencoderdeep-learningcomputer-visionhierarchical
charlesq34/pointnet
Jan 2017 - Sep 2019
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:38 commits, 2 PRs, 19 pushes in 2 years 8 months
Contributions summary:Charles primarily contributed to the core code and utilities of the point cloud deep learning project. Their work involved the implementation of the PLY file parsing, data loading, and point cloud preprocessing techniques. The user also fixed TF-related bugs and added semantic segmentation code. Their commits demonstrate a focus on the underlying data handling and model infrastructure.
classificationpytorchpoint-cloudsdeep-learningcomputer-vision
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