Building AI-powered digital avatars that simulate each patient's immune system to predict therapy response and accelerate precision immunotherapy development.
Using DIPS, a non-invasive skin sampling technique, to build cytokine-based biomarker panels that predict biologic treatment response in psoriasis and atopic dermatitis.
Using wearable sensors and machine learning to analyze real-world movement and sleep data, this project aims to predict treatment response earlier and enable more personalized care for inflammatory arthritis.
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.
Exploiting a newly discovered PD-1 dimerization mechanism, this project engineers monoclonal antibodies to more effectively suppress overactive T cells — offering a novel immune tolerance strategy for autoimmune diseases.
Profiling immune responses in severe asthma patients before and after biologic treatment to identify biomarkers of response and novel drug targets.
Using temporal graph machine learning and deep immune profiling to predict which cancer immunotherapy patients will develop autoimmune adverse events before they occur.
Profiling deep immune changes before AAV relapse to identify predictive biomarkers and therapeutic targets for this difficult-to-manage autoimmune vasculitis.
Developing CART4-34, a novel CAR T cell therapy that selectively eliminates IGHV4-34+ pathogenic B cells in lupus while sparing healthy B cells and avoiding broad immunosuppression.
Advancing engineered CAR and CAAR T cell therapies across multiple autoimmune diseases through clinical trials, preclinical development, and industry partnerships.
