David J. Lee


I am a second year computer science Ph.D. student at Cornell. I am fortunate to be advised by Kevin Ellis.

I am broadly interested in systems that integrate machine learning with symbolic reasoning. My recent work has focused on probabilistic program synthesis. In my current project, I am developing techniques for data-efficient neural program synthesis.

As an undergrad at Williams ('21), I worked on probabilistic data structures, concurrent program synthesis, and knot theory. My thesis was advised by Sam McCauley and Shikha Singh.


  • Telescoping Filter: A Practical Adaptive Filter (Paper, Code).
    David J. Lee, Samuel McCauley, Shikha Singh, and Max Stein.
    European Symposium on Algorithms (ESA), 2021.
  • A Practical Adaptive Quotient Filter (Thesis).
    David J. Lee. Undergraduate thesis, 2021. Advised by Shikha Singh and Sam McCauley.
  • Virtual Multicrossings and Petal Diagrams for Virtual Knots and Links (Paper).
    Colin Adams, Chaim Even-Zohar, Jonah Greenberg, Reuben Kaufman, David Lee, Darin Li, Dustin Ping, Theodore Sandstrom, and Xiwen Wang.
    Journal of Knot Theory and its Ramifications, 2021.