Mechanistic interpretability of how models decide what to trust, and clinical ML where those methods are tested.
The first two projects study language and vision-language models directly. The UCSF and UMSOM projects are empirical and representational work on clinical models: external validation and subgroup analysis of MRI classifiers, and a new representation of somatic mutations for cancer typing.
How does a vision-language model decide whether to trust an image or its own language prior when the two disagree? Using a semantic image pair protocol on CLIP-LLaVA models, activation patching, and logit lens, we showed that models follow whichever input they can discern more reliably, and located where in the network that decision forms. First paper at ECCV 2026 (eXCV); full-length extension under review at ICLR 2027.
Linear probes on hidden activations detect hallucination in medical LLMs at AUROC 0.77 to 0.86, yet steering the same neurons does not reliably change behavior. The project established this detection-control gap across four open-weight models and 36K free-generation answers. Presented at COLM 2026.
External validation of three published glioma subtype classifiers (3D CNN, ViT with ClinicalBERT, CNN with radiomics) on an IRB-approved cohort of 3,380 UCSF patients. An LLM pipeline labeled 9,377 pathology reports at F1 0.96 to 0.97, giving every subgroup enough samples to test. Errors concentrate by age: specificity for IDH-wildtype patients under 40 is 54%, against 80% at 55 and older.
A compute-matched comparison of mutation representations for cancer typing. We designed Bio MAF v4, 948 interpretable per-tumor features of mutation consequence, driver genes, hotspots, and allele fraction, rebuilt the MuAt mutation transformer from scratch, and benchmarked both on 8,800 TCGA tumors. The feature table outperformed the transformer for cancer typing (0.738 vs 0.528 balanced accuracy) and HRD prediction (AUROC 0.91 vs 0.75), and transferred to 913 PCAWG tumors.
Clinical ML at an NSF-funded startup since 2021: a multimodal depression detection model on interview audio and transcripts (F1 0.87 on DAIC-WOZ), imaging classifiers for lung and cardiac scans, and BERT-based clinical documentation tools, deployed on AWS to partner institutions.