Lynn Cai
Happy Retiree at Silicon Valley
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolLynn Cai is a seasoned engineering leader and AI specialist with over two decades of experience building and managing software teams across Silicon Valley and global sites, most recently overseeing DevOps and development infrastructure for Intel’s Data Center and AI Group. She combines hands-on expertise in software architecture, high-performance computing, and neural networks with deep product lifecycle and people-management skills, having led Agile transformations and CI/CD initiatives that improved developer productivity and release reliability. A published researcher with 7 awarded US patents and contributions to open-source computer-vision projects like a realtime multi-object-tracking repo, she brings rare technical depth in pattern recognition and image processing alongside practical systems engineering. Based in Palo Alto and holding a PhD in Artificial Intelligence, Lynn is known for bridging research and production, negotiating complex customer requirements, and scaling offshore and onsite teams to deliver measurable results.
10 years of coding experience
26 years of employment as a software developer
Tsinghua UniversityDoctor of Philosophy (Ph.D.) Artificial Intelligence, Doctor of Philosophy (Ph.D.) Artificial Intelligence at University of Windsor
Github Skills (7)
multi-object-tracking10
computer-vision10
pytorch10
machine-learning10
python10
image-processing9
algorithms8
Programming languages (3)
Github contributions (5)
Zhongdao/Towards-Realtime-MOT
Sep 2019 - Oct 2019
Joint Detection and Embedding for fast multi-object tracking
Role in this project:
ML Engineer
ML EngineerContributions:7 commits in 2 days
Contributions summary:Lynn primarily contributed to the project by modifying core model components and training-related code, as evidenced by changes in `models.py`, `track.py`, and `test.py`. The commits included adjustments to loss functions and model configurations. Furthermore, the user incorporated video processing functionality, which suggests a focus on computer vision aspects of the multi-object tracking project. The changes also involved updating data loading and processing pipelines.
pytorchdeep-learningobject-detectioncomputer-visiontracking
lyxlynn/DORN
Dec 2018 - Mar 2019
Contributions:6 commits, 4 pushes, 1 branch in 3 months
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