Research Theme 1: Quantitative Neuroimaging Biomarkers
How can biological processes be quantified from neuroimaging data?
Research Theme 2: Computational Inference of Biological Dynamics
How can latent biological information be inferred from limited observations?
Research Theme 3: Data-Driven Modeling and Prediction
How can data-driven models reveal meaningful patterns and support prediction in complex systems?
My current research focuses on multiple sclerosis (MS), a heterogeneous neuroinflammatory disease characterized by diverse lesion phenotypes and disease trajectories. Neuroimaging with MRI and PET plays a central role in quantifying lesion pathology and inflammatory activity in vivo, yet these measurements are often indirect, noisy, and constrained by clinical data acquisition. Using multimodal imaging data, I investigate how quantitative and computational approaches can improve the measurement, interpretation, and biological understanding of lesion evolution and neuroinflammatory processes in MS.
More broadly, my research lies at the intersection of applied mathematics, computational neuroimaging, and machine learning. I am interested in developing quantitative representations and computational inference methods that extract biologically meaningful information from high-dimensional, noisy, and data-limited biomedical data.
A central theme of my work is integrating data-driven methods with modality-specific physical and biological knowledge to improve inference from imperfect observations. Through mathematical modeling, representation learning, and quantitative imaging, I study how computational models can reveal latent biological processes and how modeling choices influence both quantitative estimation and biological interpretation.