Amazon Scholar In Agentic AI Org Of AWS at University of Virginia
Charlottesville, Virginia, United States
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Summary
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Rockstar
🎓
Top School
Yanjun Qi is a machine learning researcher and leader with two decades of experience spanning deep learning, NLP, reinforcement learning, responsible AI, and computational biology, currently shaping agentic and generative AI at AWS while holding a tenured associate professorship at the University of Virginia. She has driven production GenAI solutions and high-profile AWS RFP wins, led teams building Bedrock features like guardrails and model customization, and previously served as an NIH National Scholar and senior researcher at NEC Labs. An NSF CAREER and multiple best-paper award recipient with 100+ publications, she has graduated six Ph.D. students and mentored dozens more, combining rigorous academia with product-focused impact. Yanjun is also an active open-source contributor and documentation maintainer for influential projects such as TextAttack and SpacetimeFormer, improving accessibility for adversarial NLP and long-range transformer forecasting tools. Off-duty she reads widely—curating a public reading list—reflecting a curiosity-driven approach that informs her interdisciplinary research and applied AI product work.
12 years of coding experience
11 years of employment as a software developer
B.S. (graduation with the highest honors) , (and M.S. in the accelerated 1year program), computer science, B.S. (graduation with the highest honors) , (and M.S. in the accelerated 1year program), computer science at Tsinghua University
Ph.D., Computer Science, Ph.D., Computer Science at Carnegie Mellon University
Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal Forecasting."
Role in this project:
Technical Writer
Contributions:12 commits, 6 pushes, 1 branch in 3 months
Contributions summary:Yanjun primarily focused on enhancing the documentation for the `spacetimeformer` repository. Their commits added a `docs` folder, set up the necessary configuration for Read the Docs, and provided basic tutorials within Jupyter notebooks. They also updated the main documentation files, including the index and API references, and created a demo notebook showcasing basic usage.
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