My research goal is to democratize AI development by making both data and model scaling more efficient and effective in today’s large-scale environment, based on four complementary areas of work tackling different aspects of data and model scaling challenges. On data side, I study (1) how to efficiently curate large datasets, and (2) how to effectively align model behavior through data. On model side, I tackle (3) how to efficiently deploy large models, and (4) how to effectively adapt large models to downstream applications.