My current research interests focus on building machine learning models that are unbiased, widely generalizable, and easily adaptable to changing environments and tasks. Revolving around this goal, I recently study the following areas:

Machine learning: meta-learning, pre-training and fine-tuning, model debiasing and fairness, multi-task learning, continual learning, self-supervised learning, and any techniques related to building foundation models

Data science applications: AI for science (e.g., biology, chemistry), AI for social good (e.g., healthcare, education, transportation)