His research interests center on the use of big data in education and the role of online assessment data in accurately determining student learning. Specifically, Charles studies innovative methodologies for understanding student learning through predictive analytics, personalization and graphical models. His doctoral thesis proposed a novel algorithm for predicting student performance within an electronic tutor based. Charles was previously a Postdoctoral Associate in Learning Analytics at Steinhardt School of Education, Culture & Human Development, NYU and received his Doctorate in Human Development and Education from the Harvard Graduate School of Education where he also taught Data Science in Education. He received his Bachelor of Science in biochemistry and Bachelor of Arts in political science from the University of Melbourne.