Yi Yang
Research Scientist at DeepMind
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolYi Yang is a research scientist at DeepMind with 11 years of experience specializing in AI, machine learning, deep learning, and computer vision. He holds a PhD in Computer Science from UC Irvine and brings a track record of research-to-engineering impact from roles at Baidu USA and internships at Google and Microsoft. At DeepMind he contributes to cutting-edge vision research and has hands-on experience improving data pipelines for high-profile projects like TapNet’s Tracking Any Point, including Kinetics dataset integration and visualization. Comfortable moving between prototype research and production-ready engineering, he combines rigorous academic training with practical system-building in large-scale ML environments. Based in London, he blends classical robotics/mechatronics foundations with modern deep learning research, making him adept at bridging physical intuition and data-driven models.
12 years of coding experience
5 years of employment as a software developer
Hong Kong University of Science and Technology (HKUST)Bachelor's Degree Mechatronics Robotics and Automation Engineering, Bachelor's Degree Mechatronics Robotics and Automation Engineering at Tsinghua UniversityUniversity of California, Irvine
Chinese, English
Github Skills (6)
data-loading10
computer-vision10
deep-learning10
python10
numpy10
tensorflow3
Programming languages (4)
Github contributions (5)
google-deepmind/tapnet
Oct 2022 - Feb 2023
Tracking Any Point (TAP)
Role in this project:
ML Engineer
ML EngineerContributions:32 commits, 6 PRs, 96 pushes in 4 months
Contributions summary:Yi's commits primarily involve modifications to the TapNet codebase, focusing on data loading and dataset integration for the Kinetics dataset. The user added a third-party data loader, updated the generate_tapvid.py script, and directly visualized the Kinetics dataset from a CSV file. These changes suggest a focus on expanding the dataset capabilities and potentially improving the data handling pipeline for the point-tracking model.
benchmarkpoint-trackingroboticscomputer-visiondeep-learning
caochunshui/Feedback-CNN
May 2018 - Jul 2018
The code is an implementation of Feedback Convolutional Neural Network for Visual Localization and Segmentation.
Contributions:22 commits, 10 pushes in 2 months
convolutional-neural-networkslocalizationsegmentation
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