My research focuses on machine learning tools that help humans make better decisions, mainly interpretable machine learning and its applications. We work on decision trees, sparse linear models and scoring systems, variable importance measures, causal inference methods, interpretable deep learning, dimension reduction, and methods that can incorporate domain-based constraints and other types of domain knowledge into machine learning. These techniques are applied to critical societal problems in healthcare, criminal justice and energy grid reliability, as well as to materials science and computer vision. Many of our interpretable machine learning algorithms heavily rely on efficient discrete optimization techniques.