Dara Rouholiman

I'm an ML Research Engineer at Stanford AIM Lab, where I build and evaluate machine learning models for clinical medicine — from novel deep learning architectures for time-series forecasting to large language model evaluation in perioperative settings. Our recent work showing how tool-augmented LLMs can reduce clinical calculation errors from 64% to below 5% was published in Nature's npj Digital Medicine.

I'm a physical chemist by training, which gave me a habit of thinking from first principles — not just what works, but why it works. I taught myself ML by building things: co-founding Telesphora (opioid overdose prediction, deployed with the Connecticut Department of Public Health), leading ML at COR (at-home blood analyzer with 1,000+ users, 3 patents), and serving as PI on a DoD SBIR grant. Along the way I've published 12 papers, filed 3 patents, and learned that the best health ML comes from sitting close to clinicians.

I also teach — I'm Lead Instructor for Stanford SASI's Healthcare Innovation Internship, where I run seminars on clinical data structures and the ethics of health AI for pre-med and medical students.

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