I am a PhD candidate in the Department of Applied Mathematics & Statistics at Johns Hopkins University. I am very fortunate to be co-advised by Professor Soledad Villar and Professor Carey Priebe.
I have no special talent. I am only passionately curious. In particular, I am passionate about understanding learning — including machine learning and natural learning — from mathematical and statistical principles.
My research interests are in the areas of representation learning and deep learning. My current work focuses on expressivity and generalization properties of graph neural networks.
Ph.D. in Applied Math & Stat, 2024
Johns Hopkins University
M.S. in Data Science, 2020
New York University
B.S. in Statistics and Economics, 2016
University of Hong Kong
A CNN encoder-decoder model to navigate traffic environment using bird's eye view images.
An interative visualization showing results of topic modelling on 33k Wikipedia articles
An interactive jupyter notebook implementing live chatbot using N-gram blocking
A study of three matrix imputation methods on different types of missing data patterns
A data visualization showing global inequities of water usage and conservation suggestions
A data-driven graphic story. Collaboration with Yue Qiu, Justina Lee and Adrian Leung in Bloomberg.