Developing an IFNγ-based molecular microscope to assess celiac disease activity and response to treatment without relying solely on diagnostic biopsies.
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.
Modernizing anti-nuclear antibody testing by combining AI-enhanced image analysis with multiplex autoantibody profiling to improve diagnostic precision and uncover novel autoimmune biomarkers.
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.
Investigating the autoimmune and immune dysregulation mechanisms underlying Long COVID to improve diagnosis, treatment, and biological understanding of persistent symptoms.
Building the largest cerebrospinal fluid immune profile dataset in pediatric autoimmune neurological diseases to enable faster, more accurate diagnosis of MS and related conditions.
Profiling deep immune changes before AAV relapse to identify predictive biomarkers and therapeutic targets for this difficult-to-manage autoimmune vasculitis.
Building a spatial single-cell atlas of healthy and diseased human kidneys to redefine immune-mediated kidney disease and identify precision diagnostic and therapeutic targets.
