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Lecturer in Computer Science Email: yanlisa at stanford dot edu Winter 2021Office hours: TBD I'm teaching CS107 online! What exciting times we live in. |
I (she/her) was born and raised on the east coast of the United States, in a small commuter town called Holmdel, NJ. Enticed by all the nice people in California, I studied Electrical Engineering and Computer Science across the bay (UC Berkeley) before receiving my Masters and PhD in Electrical Engineering at Stanford. In the second half of my doctorate program, I decided that I want to transform people's lives for the better through education, and I set my research and career goal: to improve undergraduate CS education. I am so happy to be in my new role as a lecturer. Stop by, meet Poisson the Shark, and say hi!
CS 107: Computer Organization & Systems
Winter 2020, Winter 2021
CS 109: Intro to Probability for Computer Scientists
Autumn 2020, Spring 2020, Autumn 2019, Summer 2018
CS 144: Introduction to Networking
Autumn 2018
SSEA: Stanford Summer Engineering Academy
Summer 2019, Summer 2020
Istanbul, Turkey: Summer 2019, Summer 2017, Summer 2016
As classroom sizes grow, instructor workload also increases. Despite innovations in technology to scale education, little has been done to improve upon the most critical component of student learning: unsupervised work on assignments. In computer science education, learning process — the way in which students design, debug, and explore programming assignments — is instrumental to performance and mastery. Yet few studies have defined assignment-centric metrics to measure learning process, much less design systems that transform the way we think about unsupervised work today. My work explores how to improve student assignment work so that both the teacher and learner benefit. While many tools analyze only a student's final submission, I focus on a paradigm that collects in-depth snapshots of in-progress student work. By designing tools that reveal how a student learns, I give teachers the power to provide meaningful, personalized feedback.
The field of computer systems requires creativity and tenacity. We must develop new curricula for undergraduates and graduates so that they can excel in these traits and hit the ground running to tackle cutting-edge networking problems. I am involved in assessing the value of Stanford's current B.S- and M.S-level networking courses. The first course in the system is a flipped classroom, designed to maximize student networking intuition, expand student knowledge, and hone programming and debugging skills. The second course is designed around research, and the necessary critical mindset one must take towards existing work.
The nature of a computer scientist is changing — to be creative, collaborative, and socially-involved. A basic understanding of CS can open doors to a new workforce and a better standard of living worldwide. What we are seeing today is that there many students that want to learn CS, but there are not enough educators properly equipped to teach. Not only should we grow the pool of teaching resources available, but also we should increase the number of teachers at every level — from K-12 to higher education.
If you are an undergraduate interested in an academic teaching career, read my document on the academic teaching job search (coming soon).
Gili Rusak and Lisa Yan. “Unique Exams: Designing Assessments for Integrity and Fairness,” in Proceedings of The 52nd ACM Technical Symposium on Computer Science Education (SIGCSE), March 2021.
Chris Piech, Lisa Yan, Lisa Einstein, Ana Saavedra, Baris Bozkurt, Eliska Sestakova, Ondrej Guth, and Nick McKeown. “Co-Teaching Computer Science Across Borders: Human-Centric Learning at Scale,” in Proceedings of the Seventh ACM Conference on Learning @ Scale (L@S ’20), June 2020. Awarded Best Paper. [ paper, video ]
Lisa Yan. “Learning Networking by Reproducing Research Results,” in Practial Reproducible Evaluations of Systems, June 2020. Keynote talk. [ slides, video ]
Lisa Yan. “Tools to Understand How Students Learn.” PhD Thesis, Electrical Engineering Department, Stanford University, June 2019. [ pdf ]
Lisa Yan, Nick McKeown, and Chris Piech. “The PyramidSnapshot Challenge: Understanding student process from visual output of programs,” in Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE), February 2019. [ paper ]
Lisa Yan, Annie Hu, and Chris Piech. “Pensieve: Feedback on coding process,” in Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE), February 2019. [ paper ]
Lisa Yan, Nick McKeown, Mehran Sahami, and Chris Piech, “TMOSS: Using Intermediate Assignment Work to Understand Excessive Collaboration in Large Classes,” in Proceedings of The 49th ACM Technical Symposium on Computer Science Education (SIGCSE), February 2018. [ paper, code ]
Lisa Yan, Nick McKeown, and Chris Piech, “Deep Grade: A visual approach to grading student programming assignments.” Abstract accepted to Women in Computer Vision workshop (WiCV) at Conference on Computer Vision and Pattern Recognition (CVPR), July 2017. [ abstract, poster ]
Lisa Yan and Nick McKeown, “Learning Networking by Reproducing Research Results,” in SIGCOMM Computer Communication Review (CCR), April 2017. Awarded Best of CCR at SIGCOMM 2017. [ editorial, slides, website ]
Lavanya Jose, Lisa Yan, Mohammad Alizadeh, George Varghese, Nick McKeown, and Sachin Katti, “High Speed Networks Need Proactive Congestion Control,” in Proc. Workshop on Hot Topics in Networks (HotNets), November 2015. [ paper ]
Lavanya Jose, Lisa Yan, George Varghese, Nick McKeown, “Compiling Packet Programs to Reconfigurable Switches,” in Proc. Networked Systems Design and Implementation (NSDI), May 2015. [ webpage, paper ]
Lavanya Jose, Lisa Yan, Pat Bosshart, Dan Daly, George Varghese, Nick McKeown, “Mapping Match+Action Tables to Switches,” at Open Networking Summit, April 2013. [ poster ]
K. Lee, L. Yan, A. Parekh and K. Ramchandran, “A VoD System for Massively Scaled, Heterogeneous Environments: Design and Implementation”, IEEE 21st International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2013), San Francisco, CA, August, 2013. Best Paper Finalist. [ paper ]
Ph.D., Electrical Engineering, Stanford University, June 2019. Tools to Understand How Students Learn. Advised by Prof. Nick McKeown and Prof. Chris Piech.
M.S., Electrical Engineering, Stanford University, September 2015.
B.S., Electrical Engineering and Computer Science, University of California, Berkeley, May 2013.