He is interested in the science of AI and AI for science, and he works on reproducibility and efficiency in AI research. His research has highlighted the growing computational cost of AI systems, including the environmental impact of AI and inequality in the research community. He has worked extensively on improving transparency in AI research, including open sourcing and documenting datasets, data governance, and measuring bias in data. He has also worked on developing efficient methods, including model compression and improving efficiency of training large language models.