Taojiannan Yang
Applied Scientist at Amazon Web Services (AWS)
California, United States
Summary
🤩
Rockstar🎓
Top SchoolTaojiannan Yang is an Applied Scientist at AWS AI working on Amazon Nova foundation models, with six years of experience bridging cutting-edge research and production ML systems. He holds doctoral-level training in computer and electrical engineering and has a track record of research internships at ByteDance and video-understanding work during an AWS internship. An active open-source contributor, he improved the widely used AutoGluon library by enhancing FT-Transformer support, pre-trained weight loading, and usability through tutorials and presets. His background spans efficient neural architecture search, multimodal model engineering, and scalable ML tooling, enabling rapid experimentation to productionization. Based in California, he maintains an academic presence via a personal webpage and Google Scholar, signaling ongoing research engagement alongside product-focused model development.
6 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Electrical and Computer Engineering, Doctor of Philosophy - PhD, Electrical and Computer Engineering at University of North Carolina at CharlotteBachelor's degree, Electronics and Information Engineering, Bachelor's degree, Electronics and Information Engineering at University of Science and Technology of ChinaDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Central Florida
Github Skills (17)
pytorch10
python10
machine-learning10
hyperparameter-optimization10
automated-machine-learning10
ensemble-learning9
modeling8
deep-learning8
eval8
deeplearning-ai8
trainings8
data-science8
evaluation8
datatable7
tabular7
Programming languages (1)
Github contributions (5)
autogluon/autogluon
Sep 2023 - Jan 2024
Fast and Accurate ML in 3 Lines of Code
Role in this project:
ML Engineer
ML EngineerContributions:19 reviews, 7 PRs, 14 comments in 3 months
Contributions summary:Taojiannan contributed to the AutoGluon project, focusing on enhancements related to the FT-Transformer model and custom metrics. They added support for loading pre-trained weights into the FT-Transformer, refactored the FT-Transformer code, and added an input_keys property for late fusion. Further contributions include updating FT-Transformer hyperparameters, adding a tutorial, and fixing a test related to categorical MLP. The user also updated image backbone presets and tutorials, demonstrating a focus on improving the model's capabilities and usability.
forecastingimage-textmlppythonmeta-learning
taoyang1122/pytorch-SimSiam
Dec 2020 - Mar 2021
Contributions:42 commits, 20 pushes, 17 comments in 2 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.