I am a Ph.D. student in computer science at Yale University (2022~) advised by Prof. Smita Krishnaswamy [Google Scholar]. I received my M.S. from Columbia University (2018~2020), and I did my undergraduate studies at Bucknell University, a liberal arts college featuring engineering education (2014~2018).

Research Areas (Plain)  I use machine learning to uncover hidden structure in complex high-dimensional data: images, videos, time series, genes, proteins, and spatial tissue maps. My works aims to unveil how diseases unfold over time, how cells organize in space, and how brain activity relates to perception, among many others.

Research Areas (Technical)  I primarily work on manifold learning, a subfield of deep learning that studies how to best organize the representations in the latent space of neural networks.

Most of my research involve direct applications to healthcare or medicine, via analysis of medical images and omics (genomics, transcriptomics, proteomics) data. I also work on fusing multiple modalities, modeling time-varying dynamics, and learning from limited or no labels.

In the remaining years of my Ph.D., I would like to spend more time on taming neural networks, including generative models, to better comply with constraints such as physical laws, with applications in modeling the natural progression of diseases in time-varying medical images.