Amy Zhao

MIT CSAIL PhD Candidate

computer vision, machine learning

About Me

I am a PhD student at MIT working on computer vision and machine learning. I am advised by Professors John V. Guttag and Fr├ędo Durand. I am interested in modeling the spatial and appearance transformations that we observe in realistic images, including 3D rotations of objects, complex lighting effects and even artistic effects. These models have a variety of applications including data augmentation and image synthesis.

Prior to graduate school, I worked for 3 years as a Software Engineer at Microsoft and Caradigm. I did my undergraduate studies at the University of Toronto in Engineering Science and Biomedical Engineering.

Recent Publications

Data augmentation with spatial and appearance transforms for one-shot medical image segmentation

A. Zhao, G. Balakrishnan, F. Durand, J. Guttag, A. Dalca.

In submission.

ArXiV link coming soon.

Our model learns spatial and appearance transforms that are used to synthesize new training examples, improving a supervised segmentation network.
Synthesizing Images of Humans In Unseen Poses

G. Balakrishnan, A. Zhao, A. Dalca, F. Durand, J. Guttag.

CVPR 2018 (oral and poster)

Project page Paper

We use a modular neural network architecture to simplify the image synthesis problem, producing high-quality images that preserve the input person's appearance.
An Unsupervised Learning Model for Deformable Medical Image Registration

G. Balakrishnan, A. Zhao, F. Durand, J. Guttag, A. Dalca.

CVPR 2018 (poster)

Project page Paper

We train an unsupervised model called Voxelmorph to perform fast, deformable registration of medical images.
A Video-Based Method for Automatically Rating Ataxia

R. Jaroensri*, A. Zhao*, G. Balakrishnan, D. Lo, J. D. Schmahmann, F. Durand, J. Guttag.

MLHC 2017


We use pose estimation and feature engineering to produce ratings of neurological movement disorder severity from videos of motor exams.