Bio

Cynthia Bailey is a Senior Lecturer in the Computer Science Department at Stanford University. In 2023-2024, she worked as an AI Policy Fellow in the United States Senate, through the American Association for the Advancement of Science (AAAS). Her scholarship focuses on computer science education, broadening participation in computing, AI/machine learning, and social impacts of technology. This work includes creating the groundbreaking course, Race and Gender in Silicon Valley. Her teaching awards include the Lloyd W. Dinkelspiel Award for exceptional contributions to undergraduate education at Stanford, a "Top 10 Papers of All Time" award at the 50th anniversary of the ACM SIGCSE technical symposium, and the Stanford Society of Women Engineers' Professor of the Year. Her previous work experience includes consulting for Apple, NASA, and machine learning startups. She has a Ph.D. in Computer Science from UC San Diego.

Dr. Bailey lives in Palo Alto with her two children.

Academic Appointments

Honors & Awards

  • Teaching Honor Roll, Stanford Tau Beta Pi (2022)
  • Lloyd W. Dinkelspiel Award, Stanford University (2019)
  • Top 10 Papers of All Time (#4), ACM SIGCSE (2019)
  • Best Paper Award, ACM SIGCSE (2016)
  • Professor of the Year, Stanford Society of Women Engineers (2015)

Professional Education

  • Ph.D., University of California, San Diego, Computer Science (2009)
  • B.S., University of California, San Diego, Computer Science (2001)

Contact

Additional Info

  • Mail Code: 9015

Links

Current Research and Scholarly Interests

I have a PhD in Computer Science from the University of California, San Diego, in the area of High-Performance Computing (HPC), specifically market-based scheduling algorithms. My graduate research was done as part of San Diego Supercomputer Center (SDSC)'s Performance Modeling and Characterization Lab (PMaC), where I investigated economic models of scheduling on high performance computing systems. My adviser was Allan Snavely of SDSC.

My dissertation abstract is as follows: Effective management of Grid and HPC resources is essential to maximizing return on the substantial infrastructure investment these resources entail. An important prerequisite to effective resource management is productive interaction between the user and scheduler. My work analyzes several aspects of the user-scheduler relationship and develops solutions to three of the most vexing barriers between the two. First, users' monetary valuation of compute time and schedule turnaround time is examined in terms of a utility function. Second, responsiveness of the scheduler to users' varied valuations is optimized via a genetic algorithm heuristic, creating a controlled market for computation. Finally, the chronic problem of inaccurate user runtime requests, and its implications for scheduler performance, is examined, along with mitigation techniques.

My current research projects are in the area of Computer Science Education, with an emphasis on assessment and the use of Peer Instruction pedagogy in lecture. With colleagues Mark Guzdial, Leo Porter, and Beth Simon, I run the New CS Faculty Teaching Workshop, an annual "bootcamp" on how to teach effectively that draws attendees from dozens of the top CS programs in the country. The short-term goal is to give newly-hired faculty entering their first year of teaching the skills they need to succeed for themselves and their students. The long-term goal is to transform undergraduate education in CS by seeding our best rising stars with best practices so they can create communities of practice as their institutions and mentor their students in active learning strategies, creating a culture where these are the new norm.

Projects

  • New Faculty Workshop

    With colleagues Mark Guzdial, Leo Porter, and Beth Simon, I run the New CS Faculty Teaching Workshop, an annual "bootcamp" on how to teach effectively that draws attendees from dozens of the top CS programs in the country. The short-term goal is to give newly-hired faculty entering their first year of teaching the skills they need to succeed for themselves and their students. The long-term goal is to transform undergraduate education in CS by seeding our best rising stars with best practices so they can create communities of practice as their institutions and mentor their students in active learning strategies, creating a culture where these are the new norm.

    Location

    La Jolla, CA

    For More Information:

  • Instructor tips for creating inclusive CS classrooms, Stanford University

    Noticing a need for actionable, concrete advice on how to improve the welcome we offer all our students in Computer Science, I authored a list of straightforward tips on fostering greater inclusion. The list is designed for the busy faculty member who wants to be supportive of diversity but isn't sure where to begin. The bullets are organized in alignment with the academic calendar (e.g., beginning of the term, after a major exam, end of the term), and many of the tips can be implemented in just a few minutes. Although there is no silver bullet to creating a welcoming atmosphere, these tips are designed to make noticeable change to improve students' experience, and also encourage ongoing broader reflection on these themes.

    Location

    Stanford, CA

    For More Information:

2024-25 Courses

Stanford Advisees

All Publications

  • Evaluation of Peer Instruction for Cybersecurity Education Deshpande, P., Lee, C. B., Ahmed, I., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 720–25

    View details for DOI 10.1145/3287324.3287403

    View details for Web of Science ID 000575321600122

  • BDSI: A Validated Concept Inventory for Basic Data Structures Porter, L., Zingaro, D., Liao, S., Taylor, C., Webb, K. C., Lee, C., Clancy, M., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 111–19

    View details for DOI 10.1145/3291279.3339404

    View details for Web of Science ID 000520117800012

  • Developing Course-Level Learning Goals for Basic Data Structures in CS2 Porter, L., Zingaro, D., Lee, C., Taylor, C., Webb, K. C., Clancy, M., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 858–63

    View details for DOI 10.1145/3159450.3159457

    View details for Web of Science ID 000481890100163

  • Integrating Social Justice Topics into CS1 Lewis, C. M., Rackoff, E., Cao, E., Khan, S., Lee, C., Garcia, S., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 1056

    View details for DOI 10.1145/3159450.3162363

    View details for Web of Science ID 000481890100220

  • A multi-institutional study of peer instruction in introductory computing Porter, L., Bouvier, D., Cutts, Q., Grissom, S., Lee, C., McCartney, R., Zingaro, D., Simon, B. ACM Inroads. New York, NY, USA . 2016 ; Volume 7 (Issue 2): 76–81
  • Computer science concept inventories: past and future Computer Science Education Taylor, C., Zingaro, D., Porter, L., Webb, K., Lee, C. B., Clancy, M. 2014; 24 (4)

    View details for DOI 10.1080/08993408.2014.970779

  • Active Learning in Lecture with Peer Instruction Lee, C. B. AI Magazine. Association for the Advancement of Artificial Intelligence. 2014 ; Volume 35 (2):
  • Adapting to Pervasive Computing, and Making Great Pedagogy Pervasive Lee, C. B. National Science Foundation. 2013 ; Future Directions in Computing Education Summit white papers
  • Can peer instruction be effective in upper-division computer science courses? ACM Transactions on Computing Education (TOCE) Lee, C. B., Garcia, S., Porter, L. 2013; 13 (3)

    View details for DOI 10.1145/2499947.2499949

  • On the User–Scheduler Dialogue: Studies of User-Provided Runtime Estimates and Utility Functions The International Journal of High Performance Computing Applications Lee, C. B., Snavely, A. 2006; 20 (4)

    View details for DOI 10.1177/1094342006068414

  • Performance modeling of HPC applications Advances in Parallel Computing Snavely, A., Gao, X., Lee, C., Carrington, L., Wolter, N., Labarta, J., Gimenez, J., Jones, P. 2004; 13

    View details for DOI 10.1016/S0927-5452(04)80095-9

  • Parallel job scheduling algorithms and interfaces Lee, C. Department of Computer Science and Engineering. University of California, San Diego. La Jolla, CA. 2004
  • Detection and characterization of port scan attacks Lee, C. B., Roedel, C., Silenok, E. Univeristy of California, Department of Computer Science and Engineering. La Jolla, CA. 2003
  • Peer instruction: do students really learn from peer discussion in computing? ICER '11 Porter, L., Lee, C. B., Simon, B., Zingaro, D. 2011

    View details for DOI 10.1145/2016911.2016923

  • Are user runtime estimates inherently inaccurate? Workshop on Job Scheduling Strategies for Parallel Processing Lee, C. B., Schwartzman, Y., Hardy, J., Snavely, A.

    View details for DOI 10.1007/11407522_14

  • Applying an automated framework to produce accurate blind performance predictions of full-scale HPC applications Department of Defense Users Group Conference Carrington, L., Wolter, N., Snavely, A., Lee, C. B. 2004
  • New CS1 pedagogies and curriculum, the same success factors? SIGCSE '14 Alvarado, C., Lee, C. B., Gillespie, G. 2014

    View details for DOI 10.1145/2538862.2538897

  • Peer Instruction for Digital Forensics USENIX Workshop on Advances in Security Education (ASE17) Johnson, W., Ahmed, I., Roussev, V., Lee, C. B. 2017
  • Peer instruction in computing: the role of reading quizzes SIGCSE '13 Zingaro, D., Lee, C. B., Porter, L. 2013

    View details for DOI 10.1145/2445196.2445216

  • Development of Peer Instruction Questions for Cybersecurity Education Advances in Security Education (ASE'16) Johnson, W. E., Luzader, A., Ahmed, I., Roussev, V., Richard III, G. G., Lee, C. B. 2016
  • Precise and realistic utility functions for user-centric performance analysis of schedulers ICER '11 Lee, C. B., Snavely, A. E. 2007

    View details for DOI 10.1145/1272366.1272381

  • Halving fail rates using peer instruction: a study of four computer science courses SIGCSE '13 Porter, L., Lee, C. B., Simon, B. 2013

    View details for DOI 10.1145/2445196.2445250

  • Experience report: a multi-classroom report on the value of peer instruction ITiCSE '11 Porter, L., Lee, C. B., Simon, B., Cutts, Q., Zingaro, D. 2011

    View details for DOI 10.1145/1999747.1999788

  • Experience report: CS1 in MATLAB for non-majors, with media computation and peer instruction SIGCSE'13 Lee, C. B. 2013

    View details for DOI 10.1145/2445196.2445214