Chang Ye
Research Engineer at Google
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolChang Ye is a Research Engineer at Google with a decade of experience building machine learning and deep reinforcement learning systems, currently contributing to Gemini Applied Research. He has progressed through multiple engineering roles at Google, blending production software engineering with research-driven development. His open-source work includes implementing and refactoring intrinsic curiosity models and integrating Random Network Distillation into PPO within the popular cleanRL repository, demonstrating a strong focus on reproducible RL research. Trained at NYU (MS Computer Science) with earlier studies in Canada and China, he pairs academic rigor with practical engineering across game AI and applied ML. Based in New York, he brings hands-on expertise in data structures, visualization tools, and environment integration that help bridge research prototypes to scalable code. Colleagues describe him as someone who quietly moves projects from experimental ideas to clean, maintainable implementations.
9 years of coding experience
4 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at New York UniversityComputer Science, Computer Science at Dalhousie UniversityBachelor of Engineering - BE Computer Software Engineering, Bachelor of Engineering - BE Computer Software Engineering at Zhejiang University of Technology
English, Chinese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (13)
gymnasium10
openai-gym10
deep-reinforcement-learning10
pytorch10
machine-learning10
deeplearning-ai10
ppp10
deep-learning10
python10
reinforcement-learning10
wandb5
ata4
atari26004
Programming languages (5)
Github contributions (5)
vwxyzjn/cleanrl
Jul 2020 - Aug 2022
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
Role in this project:
ML Engineer
ML EngineerContributions:48 reviews, 30 commits, 5 PRs in 2 years 1 month
Contributions summary:Chang primarily contributed to the implementation and refactoring of intrinsic curiosity models within the cleanRL repository, focusing on Reinforcement Learning (RL) algorithms. Their work involved integrating Random Network Distillation (RND) into the Proximal Policy Optimization (PPO) algorithm, including the development of RND model components and integrating with the existing codebase. The contributions also included adding visualization tools and making updates to support environment interactions.
pythondeep-reinforcement-learninggomokutd3reinforcement
yooceii/Rotation-Translation-and-Cropping-for-Zero-Shot-Generalization
Nov 2019 - Apr 2022
Contributions:16 commits, 5 pushes, 5 comments in 2 years 6 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.