My research develops physics-based and computational MRI methods to characterize the microscopic organization of biological tissue in vivo. In particular, I use diffusion MRI, which measures the motion of water molecules within tissue, to infer cellular-scale properties that cannot be observed with conventional clinical imaging. My work spans quantitative MRI, biophysical tissue modeling, acquisition design, inverse problems, and image reconstruction.

I combine advanced acquisition methods with mathematical modeling and computational algorithms to extract more specific information from MRI measurements. This includes designing non-standard diffusion encodings that probe tissue from complementary perspectives, combining diffusion and relaxation measurements to distinguish different tissue environments, and developing signal representations that summarize the most informative rotationally invariant features of complex signals. I also use experimental design to determine which measurements provide the greatest biological information within a limited scan time and develop reconstruction and estimation methods that exploit shared structure across spatial locations and diffusion measurements to enable faster, higher-resolution imaging.

Ultimately, I aim to transform MRI into a noninvasive microscope of tissue microstructure and translate these advances into quantitative imaging tools for clinical research and patient care.