His research spans core machine-learning methods and their social impact, with a concentration on deep learning for time series data and sequential decision-making. This work addresses diverse application areas, including medical diagnosis, dialogue systems, and product recommendation. He completed several prolonged internships at Microsoft Research while completing his PhD at UCSD and continues to work part time as a machine-learning scientist at Amazon AI. He is the founding editor of the blog Approximately Correct and the lead author of Deep Learning: The Straight Dope, an open-source interactive book teaching deep learning through Jupyter notebooks.