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.
Modernizing anti-nuclear antibody testing by combining AI-enhanced image analysis with multiplex autoantibody profiling to improve diagnostic precision and uncover novel autoimmune biomarkers.
Using temporal graph machine learning and deep immune profiling to predict which cancer immunotherapy patients will develop autoimmune adverse events before they occur.
Identifying the autoantigens and T cell clonotypes driving immune attack in aplastic anemia to enable more precise diagnostics and targeted therapies.
Identifying GSPT1 as an essential, targetable vulnerability of skin-resident memory T cells to enable durable remission in chronic inflammatory skin diseases without systemic immunosuppression.
Profiling deep immune changes before AAV relapse to identify predictive biomarkers and therapeutic targets for this difficult-to-manage autoimmune vasculitis.
Systematically identifying and validating repurposed FDA-approved drugs for over 160 autoimmune diseases using AI, knowledge graphs, and high-throughput experimental screening.
Identifying urine DNA methylation signatures as a non-invasive biomarker for lupus nephritis class and disease progression.
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