My story

I got into vision because it exposes failure quickly. A model can look good on clean data and still break on small shifts. I started caring less about benchmark scores and more about what changes in the world cause a miss.

In 2022 I joined Stanford Biomedical Informatics as a research intern. I worked on confounders in skin lesion diagnosis and on physiologically plausible augmentation for tumor segmentation. The core idea was to add structure to training, not noise.

On the systems side, I helped build Appraise AI. Shipping a model forces discipline. It makes you name failure modes, track drift, and communicate uncertainty without hiding behind averages.

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