Rundi Wu
Research Scientist at Google DeepMind
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRundi Wu is a research scientist at Google DeepMind with eight years of experience bridging academic rigor and production-scale ML research. She completed a PhD in Computer Science at Columbia after earning a BS from Peking University, and has interned or done research at top labs including Google, SenseTime, Tencent America, and Face++. Her open-source work includes notable contributions to the TensorLayer deep learning library, where she implemented and refactored core initializers and layer functionality using TensorFlow primitives. Based in San Francisco, she blends deep learning research with hands-on ML engineering, often focusing on low-level implementations that improve model reliability and initialization. A less obvious strength is her repeated ability to move between academia and industry research internships, suggesting she excels at translating theoretical advances into practical tooling.
8 years of coding experience
1 year of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Peking UniversityDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Columbia University
Github Skills (6)
neural-network10
machine-learning10
deep-learning10
tensorflow10
python10
keras4
Programming languages (3)
Github contributions (5)
tensorlayer/TensorLayer
Jan 2019 - May 2020
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
Role in this project:
ML Engineer
ML EngineerContributions:161 commits, 15 PRs, 11 pushes in 1 year 4 months
Contributions summary:Rundi primarily focused on implementing and integrating machine learning initializers, including zero, one, constant, uniform, normal, and truncated normal distributions, using TensorFlow primitives. They implemented these initializers, which are wrapped over `tf.random` to initialize the tensor values. Additionally, the user refactored and implemented the functionality for different layers, including batch normalization and deformable layers, replacing the original initializers with the implemented ones.
tensorflow-tutorialscientistspythongoogledeep-reinforcement-learning
ChrisWu1997/DeepCAD
Jul 2021 - Oct 2022
Contributions:13 commits, 3 PRs, 12 pushes in 1 year 2 months
pytorchiccv3d-shapesdeep-learningcomputer-aided-design
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