Developing a cell-labeling tool to map immune cell interactions in living tissue, this project identifies the drivers of skin-resident T cell persistence in psoriasis and potential targets for disease prevention.
Applying self-supervised AI to multi-modal electronic health records — integrating clinical notes, labs, and imaging — this project builds scalable diagnostic models to detect autoimmune diseases earlier and more precisely.
Identifying a novel molecular regulator of tolerogenic dendritic cell function, this project uncovers how its loss triggers spontaneous multiorgan autoimmunity and exacerbates lupus — revealing a clinically relevant pathway in immune tolerance.
A self-replicating RNA platform delivers anti-inflammatory cytokines directly to the airways, offering targeted local immune suppression without systemic toxicity — a mechanistically distinct approach to treating lupus lung disease.
Using cancer patients experiencing immunotherapy-triggered autoimmunity as a unique human model, this project uncovers the molecular and epigenetic mechanisms by which self-reactive T cells escape immune tolerance.
Using spatial transcriptomics to map distinct HS disease endotypes and identify targeted therapeutic strategies for this underserved inflammatory skin disease.
Engineering HLA-DQ–specific CAR Tregs to selectively suppress anti-donor immune responses at sites of graft inflammation, this project seeks a more precise, durable alternative to broad immunosuppression in transplantation.
Developing novel multivalent small molecules that selectively silence or eliminate the autoantibody-producing B cells driving autoimmune disease.
Building AI-powered digital avatars that simulate each patient's immune system to predict therapy response and accelerate precision immunotherapy development.
Modernizing anti-nuclear antibody testing by combining AI-enhanced image analysis with multiplex autoantibody profiling to improve diagnostic precision and uncover novel autoimmune biomarkers.
