About Me
I am a Ph.D. candidate at the NYU Visualization and Data Analytics Research (VIDA) Center, advised by Prof. Guido Gerig. My research heavily lies at the intersection of computer vision, deep learning, and medical image analysis, where I design advanced deep learning algorithms with a primary application on medical images such as structural MRI, diffusion MRI, and OCT.
Download my resumé.
Interests
- Visual synthesis
- Representation learning
- Longitudinal analysis
- Deformable registration
Education
PhD in Computer Science, 2018
New York University
BSc in Computer Science, 2014
East China Normal University
News
- 04-2022: I gave a guest lecture on Deep Learning for Computer Vision for NYU Tandon CS-GY 6643 Computer Vision.
- 03-2022: I will join Google as a student researcher this summer.
- 07-2021: Our paper (led by Neel Dey) titled “Generative Adversarial Registration for Improved Conditional Deformable Templates” has been accepted to ICCV2021. Project page.
- 06-2021: Our paper (jointly w/ Heejong Kim) titled “Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI” has been accepted to MICCAI2021 as oral presentation. Project page, paper and code are released.
- 05-2021: I will start a Machine Learning research summer internship at Siemens Healthineer.
- 04-2021: I gave a guest lecture on Deep generative models (focusing on VAE/GANs) for NYU Tandon CS-GY 6643 Computer Vision. Slides available here.
- 02-2021: My first journal paper (jointly led with Neel Dey) on improving image translation performance and robustness was accepted by IEEE Transactions on Medical Imaging!