Yixiang Gao
Postdoctoral Researcher at Missouri University of Science and Technology
Columbia, Missouri, United States
Summary
🤩
Rockstar🎓
Top SchoolYixiang Gao is a Postdoctoral Researcher and AI/ML scientist with a Ph.D. in Electrical and Computer Engineering and a decade of experience applying deep learning, computer vision, and pattern recognition to real-world problems. His interdisciplinary work at University of Missouri–Columbia bridged engineering and clinical domains, contributing to two NIH-funded projects and publishing on confounding bias in sEMG-based voice pathology and vocal effort studies. He combines hands-on systems experience—deploying YOLO on embedded platforms and integrating ROS for quadruped robots—with rigorous methodological research into confounding detection and mitigation. An active open-source contributor, he improved CIFAR-10 training accuracy and performance in the tinygrad project, demonstrating practical ML engineering chops alongside academic rigor. Based in Columbia, Missouri, he is eager to translate AI research into deployable solutions that serve both clinical and robotics applications.
10 years of coding experience
Bachelor's Degree, Electrical, Electronics and Communications Engineering, Bachelor's Degree, Electrical, Electronics and Communications Engineering at Shanghai UniversityUniversity of Missouri
English, Chinese
Github Skills (7)
pytorch10
machine-learning10
deep-learning10
data-augmentation10
python10
model-optimization10
image-classification10
Programming languages (8)
Github contributions (5)
tinygrad/tinygrad
Jun 2023 - Jan 2024
You like pytorch? You like micrograd? You love tinygrad! ❤️
Role in this project:
ML Engineer
ML EngineerContributions:36 reviews, 74 PRs, 11 pushes in 6 months
Contributions summary:Yixiang made significant contributions to the performance and accuracy of the CIFAR-10 image classification model. They implemented and refined model training techniques, including whitening, random cropping, and cutmix data augmentation. Furthermore, they optimized and refactored the training pipeline, resulting in performance improvements and a final reported accuracy of 94.04%. They also addressed code quality by addressing the use of `.cpu().numpy() -> .numpy()` to simplify the code.
deep-learningpytorchmicrograd
g1y5x3/homepage
Jan 2019 - May 2023
Contributions:67 pushes, 2 branches in 4 years 4 months
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