Dr. Pardos is an Assistant Professor at UC Berkeley in a joint position between the Graduate School of Education and School of Information. His focal areas of study are educational data mining and learning analytics concentrating on measurement of learning phenomena in digital environments. He earned his PhD in Computer Science at Worcester Polytechnic Institute in the Tutor Research Group in 2012. Funded by a National Science Foundation Fellowship (GK-12) he spent extensive time on the front lines of K-12 education working with teachers and students to integrate educational technology into the curriculum as a formative assessment tool. He was program co-chair of the 2014 conference on Educational Data Mining, on the organizing committee for the 2014 Learning Analytics and Knowledge conference, and serves on the executive committee for the Artificial Intelligence in Education Society. He has received numerous academic awards and honors for work on predictive models of learning including a top prize applying his educational analytics in the 2010 KDD Cup, an international big data competition on predicting student performance within an intelligent tutoring system. Pardos comes to UC Berkeley after a post-doc at MIT studying Massive Open Online Courses. At UC Berkeley he directs the Computational Approaches to Human Learning (CAHL) research lab and teaches courses on Data Mining and Analytics, Digital Learning Environments, and Machine Learning in Education. I am working on updating my personal webpage. In the mean time, please see my CV for an updated list of publications. Current Research (NSF IIS) Deep Learning in Higher Education Big Data to Explore Latent Student Archetypes and Knowledge Profiles. (NSF DRK-12) Personalizing Recommendations in a Large-Scale Education Analytics Pipeline. (BMGF) Next Generation Courseware Challenge: Inspark Science Network for Postsecondary Success in Entry Level Science for Disadvantaged Students. (Google) Scaling Cognitive Modeling to Massive Open Environments. General areas: - Digital Learning Environments (MOOCs and Intelligent Tutoring Systems) - Predictive models of student learning (Computational cognitive modeling) - Issues of ethics, privacy, and confidentiality in data sharing and research in education Other Research Select Service a Professional Activities: Director of Computational Approaches to Human Learning (CAHL) Research Lab (https://github.com/CAHLR) Artificial Intelligence in Education Executive committee Editorial Board – Journal of Educational Data Mining & Int. Journal of AI in Education Panelist - National Academy of Education: Big Data and Privacy (2016) Program co-chair of the 2014 Educational Data Mining Conference Program committee for the 2014,2016 Learning @ Scale Conference Program committee EDM and LAK 2009-2016 Community Liaison for the International Educational Data Mining Society Panelist - White House/OSTP: Big Data and Privacy Workshop, Berkeley (2014) Joint Campus Committee on Information Technology (JCCIT) Asiomar Highered Convention: http://asilomar-highered.info/ Teaching: INFO/EDU 290: Machine Learning in Education (every other Fall) INFO 290T: Data Mining and Analytics (every Spring) EDUC 161: Digital Learning Environments (every Fall) INFO/EDU 299: Learning Analytics (every other Fall)