Zihan Ding
Research Scientist at ByteDance Seed
Princeton, New Jersey, United States
Summary
🤩
Rockstar🎓
Top SchoolZihan Ding is a research scientist and PhD candidate in Electrical and Computer Engineering at Princeton with eight years of experience building ML and multimodal foundation models. He brings applied research chops from internships at Meta (FAIR and GenAI), Adobe, Borealis AI, Tencent Robotics X and others, and now focuses on multimodal foundations at ByteDance Seed. His background spans rigorous academic training (Imperial College MSc in Machine Learning) and hands-on systems work, including concrete reinforcement-learning contributions to the popular TensorLayer library (A3C, SAC, TD3 for continuous control). Comfortable bridging theory and production, he tends to tackle practical robustness and optimization challenges in large models and robotics settings. An international technologist with roots in photoelectric engineering and CS, he combines cross-disciplinary depth with a track record of shipping research into industry.
8 years of coding experience
2 years of employment as a software developer
Master's degree Master of Science in Machine Learning Specialism, Master's degree Master of Science in Machine Learning Specialism at Imperial College LondonBachelor's Degree Major in Photoelectric Information Science and Engineering; Dual in Computer Science and Technology, Bachelor's Degree Major in Photoelectric Information Science and Engineering; Dual in Computer Science and Technology at University of Science and Technology of ChinaDoctor of Philosophy - PhD Electrical and Computer Engineering, Doctor of Philosophy - PhD Electrical and Computer Engineering at Princeton University
Github Skills (8)
sac10
deep-learning10
tensorflow10
tdd10
python10
reinforcement-learning10
machine-learning9
neural-network9
Programming languages (11)
Github contributions (5)
tensorlayer/TensorLayer
May 2019 - Sep 2021
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
Role in this project:
ML Engineer
ML EngineerContributions:80 commits, 13 PRs, 63 pushes in 2 years 4 months
Contributions summary:Zihan contributed to the reinforcement learning tutorial within the tensorlayer library, focusing on implementing and refining A3C, SAC, and TD3 algorithms for continuous action spaces, specifically for the BipedalWalker environment. Their contributions included modifying and debugging example code for these algorithms, which involved making changes to existing models, optimizers and loss functions. The commit history suggests the user was working on the structure and function of the code to be able to train and run the algorithms.
tensorflow-tutorialscientistspythongoogledeep-reinforcement-learning
quantumiracle/Soft-Decision-Tree
May 2020 - Aug 2020
Distilling a Neural Network Into a Soft Decision Tree., Nicholas Frosst, Geoffrey Hinton., 2017.
Contributions:281 pushes, 3 branches in 3 months
decision-treedistillingdecisiondeep-learningmachine-learning
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