Yongzhe Wang
Software Engineer at Google
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolYongzhe Wang is a software engineer with 12 years of experience building Cloud AI and video/vision systems at Google, currently contributing to AutoML Video. He combines deep academic training in electrical and computer engineering—including doctoral work at USC and a Dipl.-Ing. from TU Berlin—with hands-on experience in video codecs, 3D video, and graph-based image processing from internships at MERL, Disney Research, and Fraunhofer HHI. At Google he has moved research models toward deployment, notably integrating TFLite Mobile SSD/LSTD models and EdgeTPU support in the high-profile tensorflow/models repo. Based in Palo Alto, he blends research rigor with production-grade engineering, focusing on efficient on-device inference and scalable model pipelines. An understated strength is his ability to bridge low-level codec and depth-processing expertise with cloud-scale AutoML workflows, accelerating real-world video AI deployment.
12 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy (Ph.D.) ABD, Electrical Engineering, Doctor of Philosophy (Ph.D.) ABD, Electrical Engineering at University of Southern CaliforniaShanghai Weiyu High SchoolDipl. -Ing., Computer Engineering, Dipl. -Ing., Computer Engineering at Technische Universität BerlinBS, Electrical Engineering, BS, Electrical Engineering at Shanghai Jiao Tong University
Chinese, English, German
Github Skills (7)
tflite10
tensorflow10
cprogramming-language9
machine-learning9
c-language9
continuous-deployment8
ml-deployment8
Programming languages (2)
Github contributions (5)
tensorflow/models
Jul 2019 - Feb 2021
Models and examples built with TensorFlow
Role in this project:
ML Engineer
ML EngineerContributions:11 commits, 21 PRs, 9 pushes in 1 year 7 months
Contributions summary:Yongzhe primarily worked on developing and integrating TFLite models within the TensorFlow models repository, specifically focusing on mobile object detection. Their contributions include implementing and refining TFLite clients for Mobile SSD and LSTD models, integrating EdgeTPU API, and addressing bugs related to model initialization and state management. The user was also responsible for preparing and testing the TFLite models for deployment.
deep-learningtensorflow
yongzhe2160/automl-video-ondevice
Aug 2019 - Sep 2019
Contributions:2 pushes in 29 days
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