Yaochen Xie
Senior Applied Scientist at Amazon
Seattle, Washington, United States
Summary
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Rockstar🎓
Top SchoolYaochen Xie is a Senior Applied Scientist based in Seattle with nine years of experience bridging academic research and production ML at Amazon, where he leads the development of scalable, cost-efficient LLM-powered search and recommendation systems under tight latency budgets. He holds a Ph.D. in Computer Science from Texas A&M and has deep expertise in self-supervised learning, multimodality, causal representation, and model explainability applied to search and retrieval-augmented generation. His open-source work includes contributions to divelab/DIG, improving self-supervised graph learning pipelines and PyTorch Geometric integration—showing a practical focus on research-to-production efficiency. Known for combining rigorous research with hands-on engineering, he moves models from prototype to high-performance, latency-sensitive deployments.
8 years of coding experience
7 years of employment as a software developer
B.S. in Statistics School of the Gifted Young, B.S. in Statistics School of the Gifted Young at University of Science and Technology of ChinaPh.D. Computer Science, Ph.D. Computer Science at Texas A&M University
Github Skills (12)
pytorch10
machine-learning10
graph-neural-network10
python10
evaluation10
contrastive-learning10
gnn10
deep-learning7
deeplearning-ai4
3d-graphics4
graph4
3d4
Programming languages (4)
Github contributions (5)
divelab/DIG
Mar 2021 - Jan 2023
A library for graph deep learning research
Role in this project:
ML Engineer
ML EngineerContributions:65 commits, 1 PR, 71 pushes in 1 year 10 months
Contributions summary:Yaochen's commits focus on the development and evaluation of self-supervised learning models for graph-structured data. The primary contribution involves the implementation and refinement of methods within the `sslgraph` directory, including the definition of evaluation interfaces, modification of contrastive learning model training procedures, and the addition of example usages. The changes specifically involved integration with PyTorch Geometric (PyG) and the correction of code and potential efficiency improvements.
explainable-mlpytorchdataminingdeep-learninggraph-deep-learning
ycremar/awesome-coincidences-between-two-papers
Dec 2022 - Dec 2022
Contributions:34 commits, 26 pushes in 2 days
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