Zachary Pardos, an Associate Professor at UC Berkeley in the Graduate School of Education, studies adaptive learning and AI. His research focuses on knowledge representation and recommender system approaches to using behavioral and semantic data to map out paths to cognitive and career achievement in K-16. He earned his PhD in Computer Science at Worcester Polytechnic Institute with a dissertation on computational models of cognitive mastery. After completing his PhD in 2012, he spent one year as a Postdoctoral Associate at the Massachusetts Institute of Technology applying adaptive learning paradigms to online learning. At UC Berkeley, he directs the Computational Approaches to Human Learning research lab, teaches in the Graduate School of Education and the Division of Computing, Data Science, and Society, and is an affiliated faculty in Cognitive Science.