Yuexin Wu
Member Of Technical Staff at Carnegie Mellon University
Kirkland, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolYuexin Wu is a Member of Technical Staff and quantitative researcher with 11 years of experience building and optimizing large language models and ML systems. With a PhD from Carnegie Mellon and roles at Google and OpenAI, Yuexin has practical expertise in LLM training, distillation, and alpha research that bridges cutting-edge research and production. Their open-source contributions to the widely used tensorflow/models repo include learning rate schedule enhancements, BLEU bug fixes, and improvements to Funnel Transformer implementations—signal work on model optimization and evaluation. Based in Kirkland, WA, they combine academic rigor from CMU with industry-scale engineering at top AI labs. Notably, they have a track record of improving low-level training components (e.g., decay schedules and pooling mechanisms) that materially affect model convergence and performance.
11 years of coding experience
8 years of employment as a software developer
Bachelor of Engineering (B.E.), Computer Science, Bachelor of Engineering (B.E.), Computer Science at Tsinghua UniversityDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Carnegie Mellon UniversityTTIC
Github Skills (7)
transformer-models10
machine-learning10
deep-learning10
tensorflow10
python10
model-optimization10
nlp9
Programming languages (3)
Github contributions (5)
tensorflow/models
May 2021 - Nov 2022
Models and examples built with TensorFlow
Role in this project:
ML Engineer
ML EngineerContributions:3 releases, 14 reviews, 54 commits in 1 year 6 months
Contributions summary:Yuexin primarily contributed to the official/modeling directory within the TensorFlow Models repository, focusing on learning rate scheduling and related optimization functionalities. They added support for step offsets in the PowerAndLinearDecay learning rate schedule. Furthermore, they fixed a bug in BLEU computation and made internal changes, indicating a focus on model optimization and evaluation. The user also contributed to the Funnel Transformer implementation, enhancing it with features like average pooling and ReZero support.
deep-learningtensorflow
CrickWu/active_graph
May 2019 - May 2021
Contributions:16 commits, 1 push in 2 years
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