About

I’m a PhD student at the Harvard-MIT Division of Health Sciences and Technology, advised by Polina Golland. Previously, I did my undergrad in Electrical Engineering and Computer Science at MIT and interned at Google.

I am interested in machine learning, computer vision, and inverse problems. My PhD focuses on medical image reconstruction, and I am broadly interested in any applications involving biology or physics-based modeling.

My work is supported by the NIH Neuroimaging Training Program, an NSF Graduate Research Fellowship, and a Google PhD Fellowship.

 

Selected Projects

For full publication list, see Google Scholar.

Data Consistent Deep Rigid MRI Motion Correction
Using test-time model-based optimization on neural network outputs to produce fast, high quality, physics-consistent motion-corrected MRI reconstructions.
Medical Imaging with Deep Learning, 2023 (Oral).
Paper Code

Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis
A general purpose neural network layer that combines frequency and image space features for correcting artifacts in Fourier imaging.
Machine Learning for Biomedical Imaging, 2022.
Paper Code Video