Sherry Yang
Research Scientist at Google
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSherry Yang is a research scientist at Google in Mountain View with a decade of experience applying machine learning and reinforcement learning to research-grade problems. Trained at MIT (B.S. and M.S. in EECS/CS), she combines rigorous academic grounding with hands-on engineering—contributing optimized implementations to high-profile open-source projects like PAIR-code/saliency and Google Research’s TCAV. Her work often focuses on model interpretability and generative models, where she has implemented performance improvements (batching, multithreading) and expanded model-loading flexibility across formats. Comfortable moving between research prototypes and production-ready code, she brings a pragmatic focus on reproducibility and efficiency that speeds experimentation at scale.
10 years of coding experience
1 year of employment as a software developer
Bachelor's degree Electrical Engineering and Computer Science, Bachelor's degree Electrical Engineering and Computer Science at Massachusetts Institute of Technology
Chinese, English
Github Skills (15)
machine-learning10
interpretation10
deep-learning10
tensorflow10
python10
numpy9
unit-testing9
convolutional-neural-networks8
image-recognition8
ai8
protobuf7
protobuffer7
multiprocessing7
python-multiprocessing7
multi-process7
Programming languages (5)
Github contributions (5)
tensorflow/tcav
Jun 2019 - Oct 2019
Code for the TCAV ML interpretability project
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 2 PRs, 7 comments in 4 months
Contributions summary:Sherry contributed to the `tcav` project by implementing and improving the `activation_generator` module. They added a `normalize_image` parameter with a default value, and added documentation. Furthermore, the user updated the `model.py` and `model_test.py` files by adding the ability to load models from different formats (checkpoint, SavedModel, frozen graph) and creating a basic test setup to verify the functionality. The user added protobuf dependency and also defined default behavior of `id_to_label` and `label_to_id` functions.
pytorchtcavnlpinterpretabilitydeep-learning
google-research/google-research
May 2020 - Oct 2022
Google Research
Role in this project:ML Engineer
ML EngineerContributions:10 commits in 2 years 5 months
Contributions summary:Sherry appears to be focused on developing and implementing machine learning models within the Google Research repository. Their commits include changes to code related to generative models, specifically a Generator class and LVMBlock related to point cloud generation. Additionally, the commits incorporate the use of TensorFlow, sonnet, and numpy, indicating a focus on machine learning model development and training.
googlemachine-learningai
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.