Speaker: Lisa Yan
Abstract: Reproducing research is key to continued scientific progress, especially in fields that are engineering- and application-focused. In the past eight years, the graduate computer networking course at Stanford University has asked students to reproduce research results for a different reason: to teach students engineering rigor and critical thinking—qualities that are essential for careers in networking research and industry. In this talk, I share our experience with teaching over 500 students the art of reproducing results from over 50 networking papers. Over the past eight years, we have observed through many anecdotes that the process of reproducing research can both teach much-needed skills and provide students with a means to contribute to the networking community. I will close by discussing how to teach the importance of reproducibility through project-based learning and how to implement this project in different computing fields.
Bio: Lisa Yan is a Lecturer of Computer Science at Stanford University where she teaches probability and computer systems. She completed her PhD at Stanford, where she studied and implemented tools for understanding students in computer science classrooms. Her research interests are in facilitating teachers’ understanding of how their students complete programming assignments, through analyzing both in-progress snapshots and final submissions of student work. Lisa’s teaching interests are in building student confidence in sophomore-level CS courses, as well as training and inspiring the next generation of computer science instructors.