Sasha Rush Portfolio
NLP researcher and ML researcher at Cursor, previously a professor at Cornell and Harvard with a concurrent research role at Hugging Face; created "The Annotated Transformer," a line-by-line PyTorch walkthrough of the original Transformer paper that became one of the most widely used resources for understanding the architecture.
What makes it work
A breakdown of the choices that make this portfolio stand out.
A paper re-implemented and explained line by line
The Annotated Transformer pairs the original paper's text with runnable code for every component, turning a dense academic paper into something a reader can execute and modify — a format many later "annotated paper" projects copied.
A publication record that tracks each shift in the field
Papers spanning OpenNMT (2017) through masked diffusion language models (2024) and post-training for coding agents (2026) show someone who moved with the field's actual paradigm shifts rather than specializing in one and stopping.
Academic and industry research held simultaneously
Concurrent research affiliations across a university, a company (Hugging Face), and now a startup (Cursor) show a research practice that stayed connected to production systems rather than purely theoretical work.
What AI Researchers can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
If a foundational paper in your field is hard to parse, re-implement it with inline explanation — it can become more widely used than most original commentary.
Let your publication list show real range across the field's shifts over time, not one narrow specialty frozen at one moment.
Keep at least one foot in applied or industry work alongside academic research — it keeps theoretical work grounded in what production systems actually need.
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