Kenneth R. Koedinger is a professor of Human Computer Interaction and Psychology at Carnegie Mellon University. Dr. Koedinger has an M.S. in Computer Science, a Ph.D. in Cognitive Psychology, and experience teaching in an urban high school. His multidisciplinary background supports his research goals of understanding human learning and creating educational technologies that increase student achievement. Dr. Koedinger has created Cognitive Models, computer simulations of student thinking and learning that are used to guide the design of educational materials, practices, and technologies. These Cognitive Models provide the basis for an approach to educational technology called Cognitive Tutors that support learning within rich problem-solving environments. With his colleagues, he has developed Cognitive Tutors for mathematics, science, and language and has tested them in the laboratory and as part of real courses. Dr. Koedinger’s research has contributed new principles and techniques for the design of educational software and has produced basic cognitive science research results on the nature of mathematical thinking and learning. Dr. Koedinger has authored over 180 peer-reviewed publications, has received many best paper awards, and has been funded by over 30 grants. Dr. Koedinger is a co-founder of Carnegie Learning, Inc. and leads LearnLab, the Pittsburgh Science of Learning Center (see learnlab.org). The center leverages computational approaches to identify the instructional conditions that cause robust student learning.
Una-may O’Reilly leads the AnyScale Learning For All (ALFA) group. She has expertise in scalable machine learning, evolutionary algorithms, and frameworks for large-scale, automated knowledge mining, prediction and analytics. She educates the forthcoming generation of data scientists, teaching them how develop state of art techniques that address the challenges spanning data integration to knowledge extraction.
Philip I. Pavlik Jr is an assistant professor of experimental and cognitive psychology and a member of the Institute of Intelligent Systems at the University of Memphis. He completed his PhD dissertation research with John Anderson in Carnegie Mellon University's Psychology Department and has collaborated with Ken Koedinger in Carnegie Mellon's Human-Computer Interaction Institute. In his research, he primarily focuses on education software design, computational modeling of cognition, and learning. Specifically, his research interest lies in (1) concept and fact learning, (2) transfer of learning, and (3) strategies for learning.
Carolyn Rosé’s research program is focused on better understanding the social and pragmatic nature of conversation, and using this understanding to build computational systems that can improve the efficacy of conversation between people, and between people and computers. In order to pursue these goals, Carolyn invokes approaches from computational discourse analysis and text mining, conversational agents, and computer supported collaborative learning. She grounds her research in the fields of language technologies and human-computer interaction and works closely with students and post-docs from the Language Technologies Institute and the Human-Computer Interaction Institute, as well as to directing her TELEDIA lab. Her group’s highly interdisciplinary work, published in 160 peer reviewed publications, is represented in the top venues in 5 fields: namely, Language Technologies, Learning Sciences, Cognitive Science, Educational Technology, and Human-Computer Interaction, with awards or award nominations in 3 of these fields. An exciting current direction of her group's work is spearheading a satellite working group to support social interaction for learning in MOOCs with EdX called DANCE.
John Stamper is an assistant professor at the Human-Computer Interaction Institute at Carnegie Mellon University in Pittsburgh, PA. He is also the Technical Director of the Pittsburgh Science of Learning Center DataShop.
John earned his PhD in Computer Science at the University of North Carolina at Charlotte. His main area of research is focused on using "Big Data" from educational systems to improve learning, specifically in the areas of intelligent tutoring systems and educational data mining. He is also the lead researcher behind DataShop, the largest open repository of log data from learning systems. John’s PhD advisor was Dr. Tiffany Barnes. Prior to starting a PhD, John spent over ten years in the business world. His most recent major position was Vice President of Development for VSI Technologies, Inc.
Candace Thille is a senior research fellow in the Office of the Vice Provost for Online Learning and an assistant professor in the Graduate School of Education at Stanford University. She is the founding director of the Open Learning Initiative at Carnegie Mellon University and at Stanford University. Dr. Thille serves on the board of directors of the Association of American Colleges and Universities; as a fellow of the International Society for Design and Development in Education; on the Assessment 2020 Task Force of the American Board of Internal Medicine; on the advisory committee for the Association of American Universities STEM initiative; on the advisory council for the NSF Directorate for Education and Human Resources. She served on on the working group of the President's Council of Advisors on Science and Technology (PCAST) that produced the Engage to Excel report. She served on the U.S. Department of Education working group, co-authoring The 2010 National Education Technology Plan and is currently serving on the working group to co-author The 2015 National Education Technology Plan. She has a bachelor's degree from the University of California, Berkeley, a masters degree from Carnegie Mellon University, and a doctorate from the University of Pennsylvania.
Dr. Kalyan Veeramachaneni is a Principal Research Scientist at the Laboratory for Information and Decision Systems (LIDS) at MIT. He directs a research group called "Data to AI" in the new MIT Institute for Data Systems and Society (IDSS). The group is interested in Big data science and Machine learning, and solving foundational issues preventing artificial intelligence and machine learning solutions to reach their full potential for societal applications. His recent work focuses on making human interactions with data seamless and efficient.