He directs the Approximately Correct Machine Intelligence (ACMI) lab, whose research focuses include the theoretical and engineering foundations of robust and adaptive machine learning algorithms, applications to both prediction and decision-making problems in clinical medicine, natural language processing, and the impact of machine learning systems on society. A key theme in his current work is to take advantage of causal structure underlying the observed data while producing algorithms that are compatible with the modern deep learning power tools that dominate practical applications in the current age. He is the founder of the Approximately Correct blog (approximatelycorrect.com) and a co-author of Dive Into Deep Learning, an interactive open-source book drafted entirely through Jupyter notebooks that has reached millions of readers.