About

I’m a PhD Candidate studying computational neuroscience at Duke University, jointly advised in the Pearson Lab and the Field Lab. I am focusing on computational understanding of retinal information processing using deep neural networks and information theory. During my PhD, I worked at Meta Reality Labs CTRL team as a research scientist intern where I built machine learning models for EMG-based neural interface tools for human interactions. In the future, I hope to apply the understanding of neural information processing to the general brain-machine interface and develop prosthetics.

In the past, I received my Master’s degree in Biomedical Engineering at Yale University in the Cardin Lab, where I studied distinct patterns of network activity caused by Channelrhodopsin variants. After graduation, I worked on retinal circuitry in the Demb Lab at Yale.

Recent News

Sep 14 2022: My recent work Efficient coding, channel capacity and the emergence of retinal mosaics has been accepted to NeurIPS 2022!

May 9 2022: I joined the CTRL team at Meta Reality Labs as a research scientist intern. I am super excited to be a part of the team building a neural interface for billions of users!

Mar 17 2022: I will present my recent work Efficient Coding of Natural Movies Predicts the Optimal Number of Receptive Field Mosaics at Cosyne2022.

Oct 1 2021: National Eye Institute spotlighted our research story! Thanks for supporting our research.

Sep 28 2021: I am excited and honored that my research is featured in Duke Today, Science Daily, EurekAlert, MedicalXPress, and many other news sources.

Sep 28 2021: Our recent work Bubblewrap: Online tiling and real-time flow prediction on neural manifolds has been accepted to NeurIPS 2021!