Combining machine learning with microbiome and metabolomic data from 4,000+ IBD patients, this project develops tools to predict disease flares, identify therapeutic targets, and enable personalized flare prevention.
Applying advanced quantitative MRI to detect pathology invisible to current clinical tools, this project builds an AI model to predict MS progression and enable earlier, more personalized diagnosis and treatment.
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.
Building a fluorescence-based cellular screening platform to identify small molecules targeting mutant UBA1, this project addresses an acute unmet need in VEXAS syndrome and lays groundwork for broader rheumatic disease therapies.
Building AI-powered digital avatars that simulate each patient's immune system to predict therapy response and accelerate precision immunotherapy development.
Characterizing a newly discovered IgE receptor to develop it into a drug that clears circulating IgE and treats allergic diseases.
Developing T cell-redirecting bispecific antibodies to selectively eliminate tuft cells and break the cycle of Type 2 inflammation in CRSwNP.
Using spatial transcriptomics to map distinct HS disease endotypes and identify targeted therapeutic strategies for this underserved inflammatory skin disease.
Identifying DNA methylation signatures in blood and urine as non-invasive biomarkers to classify lupus nephritis and guide treatment without repeated biopsies.
