Education data scientist & computer science educator

Hi! I’m a Ph.D. candidate in Education Data Science at Stanford University. My research uses a policy lens to promote equitable access to advanced STEM coursework for secondary students. I apply causal inference methods developed in economics, political science, and machine learning to problems in education. Recently, I showed that a new Advanced Placement computer science course, designed to attract students from underrepresented groups, successfully caused huge jumps in participation among Black, Hispanic, and female students. Currently, through a research-practice partnership with a local high school district, I’m investigating whether a science class detracking initiative was able to positively influence academic and behavioral outcomes. My work is supported by the Stanford Interdisciplinary Graduate Fellowship, Stanford Graduate Fellowship in Science & Engineering, George P. Shultz Dissertation Support Fund at SIEPR, and Emerging Education Policy Scholars program.

Prior to Stanford, I taught math and AI at an international school in Italy and then led the curriculum and instruction team at a startup offering AI courses to secondary students. I continue to work on curriculum development and teacher preparation in AI. I received a B.S. in Computer Science and Math, M.Eng. in Computer Science, and teaching license in secondary math from MIT.

Publications

D. Ganelin & T. Dee (in press). New Advanced Placement Course Designed to Broaden Access Promotes Participation and Demographic Diversity in Computer Science Education. Proceedings of the National Academy of Sciences.

D. Ganelin & I. Chuang (2019). IP geolocation underestimates regressive economic patterns in MOOC usage. Proceedings of the 11th International Conference on Education Technology and Computers.

D. Chamberlain, R. Kodgule, D. Ganelin, V. Miglani, & R.R. Fletcher (2016). Application of semi-supervised deep learning to lung sound analysis. Proceedings of the 38th International Conference of the IEEE Engineering in Medicine and Biology Society.

Teaching

  • Mini Courses in Methodology: Stata, Stanford University, 2022-24 (Instructor)
  • Curiosity in Artificial Intelligence, Stanford University, 2024 (Teaching Assistant)
  • AI in Healthcare, Designing Deep Learning Systems, and AI Pioneers for Middle-School Students, Inspirit AI, 2020-21 (Curriculum Developer)
  • AI and Society, Marymount International School Rome, 2019-20 (Teacher & Curriculum Developer)
  • Precalculus and AP Statistics, Cambridge Rindge and Latin School, 2017-18 (Student Teacher)
  • Mathematics for Computer Science, MIT, 2016 (Teaching Assistant)