ORFE Professor Jason Klusowski has received a National Science Foundation CAREER grant which is the "most prestigious award from the NSF in support of early-career faculty who have potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization".  Professor Klusowski's proposal is titled "Statistical Learning with Recursive Partitioning: Algorithms, Accuracy, and Applications".  

Professor Klusowski describes his 5-year project as follows:  As data-driven technologies continue to be adopted and deployed in high-stakes decision-making environments, the need for fast, interpretable algorithms has never been more important. As one such candidate, it has become increasingly common to use decision trees, a hierarchically organized data structure, for building a predictive or causal model. This trend is spurred by the appealing connection between decision trees and rule-based decision-making, particularly in clinical, legal, or business contexts, as the tree structure mimics the sequential way a human user may think and reason, thereby facilitating human-machine interaction. To make them fast to compute, decision trees are popularly constructed with an algorithm called recursive partitioning, in which the decision nodes of the tree are learned from the data in a greedy, top-down manner. The overarching goal of this project is to develop a precise understanding of the strengths and limitations of decision trees based on recursive partitioning, and, in doing so, gain insights on how to improve their performance in practice.