I’m a rising third year PhD student in the Machine Learning Department at Carnegie Mellon University advised by Professor Virginia Smith. I’m broadly interested in two aspects of machine learning:
how to endow machine learning models with more human-like intelligence;
how to make machine learning models more practically useful and reliable.
Towards these two goals, I’ve worked on areas including meta-learning, out-of-distribution generalization/evaluation, federated learning, privacy protection, and model interpretability.
Before starting my graduate studies, I graduated Summa Cum Laude with double majors in Mathematics and Computer Science from Duke University where I worked with Professor Cynthia Rudin.