Keynote Speakers

Universities continue to pack hundreds of students into a single classroom, despite the fact that this “hoarding” approach has little pedagogical value. Hoarding is particularly objectionable in STEM courses, where learning a complex idea is comparable to navigating a labyrinth. In the large classroom, once a student takes a wrong turn, the student has limited opportunities to ask a question, resulting in a learning breakdown, or the inability to progress further without individualized guidance.
A recent revolution in online education has largely focused on making low-cost equivalents of hoarding classes, as many MOOCs are mirror images of their offline counterparts. I propose to transform MOOCs into a more efficient educational product called a Massive Adaptive Interactive Text (MAIT) that can prevent individual learning breakdowns and even outperform a professor in a classroom. I argue that computer science is a unique discipline where this transition is about to happen and describe our first steps towards transforming a MOOC into a MAIT that has already outperformed me. In difference from MOOCs, MAITs will capture digitized individual learning paths of all students and will transform educational psychology into a digital science. I will argue that the future MAIT revolution, in difference from the ongoing MOOC revolution, will profoundly affect the way we all teach and will generate huge population-wide datasets containing individual learning paths through various MAITs.

Pavel Pevzner is Ronald R. Taylor Professor of Computer Science and Engineering and Director of the NIH Center for Computational Mass Spectrometry at University of California, San Diego. He holds Ph.D. from Moscow Institute of Physics and Technology, Russia. He was named Howard Hughes Medical Institute Professor in 2006. He was elected the Association for Computing Machinery Fellow in 2010, the International Society for Computational Biology Fellow in 2012, the European Academy of Sciences member (Academia Europaea) in 2016, and the American Association for Advancement in Science (AAAI) Fellow in 2018. He was awarded a Honoris Causa (2011) from Simon Fraser University in Vancouver, the Senior Scientist Award (2017) by the International Society for Computational Biology, and the Kanellakis Theory and Practice Award from the Association for Computing Machinery (2019). Dr. Pevzner authored textbooks "Computational Molecular Biology: An Algorithmic Approach", "Introduction to Bioinformatics Algorithms" (with Neal Jones), “Bioinformatics Algorithms: an Active Learning Approach” (with Phillip Compeau), and “Learning Algorithms through Programming and Puzzle Solving” (with Alexander Kulikov). He co-developed the Bioinformatics and Data Structure and Algorithms online specializations on Coursera as well as the Algorithms Micro Master Program at edX.

Former NSF program officer Mike Erlinger will start your morning with a general discussion of NSF. There will be some slides, but hopefully we can quickly move to an open discussion of relevant NSF solicitations, grant writing, opportunities for NSF/faculty interaction, and common misconceptions of NSF. Obviously, you need to bring your own coffee.