John is a research scientist working to enable intelligent, data-driven mass transit at Bridj. He received his B.A. in Computer Science from Harvard University and his Ph.D. in Computer Science from Vanderbilt University. His research interests include machine learning, combinatorial optimization, autonomous agents, and coordination in multi-agent systems. Currently, he is developing and applying machine learning and AI optimization techniques to transportation modeling, prediction, and optimization for improving mass transit with Bridj. His previous research at Vanderbilt focused on the design of intelligent pedagogical agents and machine learning techniques to model important learning behaviors, including metacognition and self-regulated learning strategies, from activity traces of student interaction in educational systems.
John has developed novel data mining algorithms to identify differential patterns among multiple contexts within and across sequential data series (e.g., autonomously-identified phases of task performance within learning activity traces). He has also developed techniques to model the evolution of patterns over the course of long data series and refine the representation of patterns to appropriate levels of specificity in multi-dimensional, hierarchical sequential data. Further, he has applied these approaches to improve the design of pedagogical agents and their coordination mechanisms for dynamic personalization and adaptation to the user.