Associate Professor of Education and Information at UC Berkeley
Dr. Pardos is an Assistant Professor at UC Berkeley in the Graduate School of Education with a joint appointment in the iSchool. His research focuses on knowledge representation and personalized supports leveraging big data in education. His current projects are centered around increasing upward mobility in public post-secondary systems and using behavioral and semantic data to map out paths to cognitive and career achievement. He has published over 50 peer-reviewed papers relating to learning analytics and given talks on big data, ethics, and education before the National Academy of Education and the White House Office of Science and Technology Policy.
He holds several academic leadership positions in AI and Education, including serving on the executive committee of the Artificial Intelligence in Education Society, the editorial boards of the Education Data Mining and AI in Education journals, and serving as program committee member for the 2019 conferences: ACM Learning Analytics and Knowledge, ACM Learning @ Scale, ACM Recommender Systems, Educational Data Mining, and AI in Education. He earned his PhD in Computer Science at Worcester Polytechnic Institute. Funded by a National Science Foundation fellowship, he spent extensive time with K-12 educators and students working to integrate educational technology into the curriculum as a formative assessment tool. In 2013, he completed his post-doc studying massive open online courses at the Massachusetts Institute of Technology. At UC Berkeley, he directs a research lab on Computational Approaches to Human Learning (CAHL) and teaches courses on data mining and analytics, digital learning environments, and machine learning in education. Recently, he received an AI educator award from the AAAI conference meant to support teachers with ideas for making AI relevant to the social sciences.
Riiid AI Applied Research Team Leader
Minsam Kim (Sam) leads a team of researchers focusing on AI models put into production. He majored in computer science, mathematics and physics at the Hong Kong University of Science and Technology, and holds a MPhil in Computer Science and Engineering from the same institution. Before joining Riiid, he worked as a quantitative researcher / trader at Credit Suisse and Symmetry Investments in Hong Kong.
Associate Professor in the Institute for Intelligent Systems and the Dept. of Psychology at the University of Memphis
Philip Pavlik is Associate Professor in the Institute for Intelligent Systems and Psychology Dept. at the University of Memphis (UM). He focuses on the application of adaptive learning technology based in learning science and cognitive modeling. His research uses the MoFaCTS (Mobile Fact and Concept Training System) which is an open source system for controlled and adaptive learning experiments. This system selects practice according to a student model that is continuously updated, this results in a personalized schedule of practice.
Director of the Center for Learning Analytics and Big Data at the University of Pennsylvania
Ryan Baker is Associate Professor at the University of Pennsylvania, and Director of the Penn Center for Learning Analytics. His lab conducts research on engagement and robust learning within online and blended learning, seeking to find actionable indicators that can be used today but which predict future student outcomes. Baker has developed models that can automatically detect student engagement in over a dozen online learning environments, and has led the development of an observational protocol and app for field observation of student engagement that has been used by over 160 researchers in 6 countries. Predictive analytics models he helped develop have been used to benefit hundreds of thousands of students, over a hundred thousand people have taken MOOCs he ran, and he has coordinated longitudinal studies that spanned over a decade. He was the founding president of the International Educational Data Mining Society, is currently serving as Editor of the journal Computer-Based Learning in Context, is Associate Editor of two journals, was the first technical director of the Pittsburgh Science of Learning Center DataShop, and currently serves as Co-Director of the MOOC Replication Framework (MORF). Baker has co-authored published papers with over 300 colleagues, and his work has been cited over 15,000 times.
CEO of Korbit
Iulian Serban is the co-founder of Korbit Technologies (www.korbit.ai), an EdTech startup aiming to democratize education through intelligent tutors powered by artificial intelligence. The startup's vision is to provide a personalized, active learning experience to millions by developing a personalized AI tutor for everyone, which will help professionals and students alike to learn faster and better in a cost-effective way. The startup has already helped teach tens of thousands and is actively partnering with enterprises and universities to reach more students. The startup has raised over $4,000,000 USD to date and is the recipient of multiple international awards.
Iulian completed his Ph.D. in machine learning, natural language processing and personal assistants under Yoshua Bengio and Aaron Courville at the Mila research lab, University of Montreal. He completed his Master's degree at University College London and his Bachelor's degree at the University of Copenhagen majoring in theoretical mathematics and statistics. He has been a teacher and spent a year serving as full-time volunteer and board member for the non-profit education organization Operation A Day's Work.