Healthcare is an area where data science and artificial intelligence have tremendous potential to improve lives and where significant methodological advances are needed to achieve that promise. In this talk, I will highlight clinical needs in which data science can help -- accurate diagnosis, long-term disease management, and personalized treatment -- and also the hard, interesting methodological challenges -- particularly in robust inference and interpretability -- that will be part of the solution. I will do so by sharing examples of work from our group, which focuses on learning timeseries and sequential decision-making models for health applications ranging from better understanding autism spectrum disorder to managing patients with HIV or in the ICU. ​ Dr. Finale Doshi-Velez from Harvard University describes how machine learning is optimizing treatment for HIV patients, and beyond.