I am a postdoctoral research associate at Princeton University, and I have completed my PhD in Applied Mathematics at the University of Washington in 2024. I have also held visiting research roles at the Allen Institute, Mila - AI Institute, and MIT.
My research lies at the intersection of artificial and natural intelligence. I leverage deep learning frameworks to model biological systems — explain how biological neural networks learn using current theoretical deep learning knowledge — and analyze neural data to uncover the principles of brain learning. I was named a 2024 Rising Star in Computational and Data Sciences as well as a 2024 Rising Star in Electrical Engineering and Computer Sciences (EECS). I have published as the lead author in NeurIPS, ICLR, PNAS, and IEEE, and my work has been supported by fellowships such as the NSERC PGS-D, FRQNT B2X, Pearson, and NSF AccelNet IN-BIC. I have also taught and mentored extensively, earning a departmental teaching award.