Shangyin Tan

I am an undergraduate student at Purdue University, working with Guannan Wei and Tiark Rompf. I am interested in programming languages, program analysis, and compilers. We are applying compiler technologies on traditional program analysis tools for flexibility and modularity while achieving same or better performance. Stay tuned for more publications on this topic!

Besides programming, I enjoy playing competitive video games, travelling, and good food. I also plan to share some random notes about my life.

You can find me at \(\text{tan279}\ at\ \text{}\), Twitter, or Github. Here is my CV.

Papers Under Review
  1. The Essence of Compiling Symbolic Execution.
    Shangyin Tan, Guannan Wei, Tiark Rompf.
    Submitted to USENIX Security Symposium 2022
  1. Partially Evaluating Symbolic Interpreters for All.
    Shangyin Tan, Guannan Wei, Tiark Rompf.
    The ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation (PEPM 2022) (to appear)
  2. LLSC: A Parallel Symbolic Execution Compiler for LLVM IR.
    Guannan Wei, Shangyin Tan, Oliver Bračevac, Tiark Rompf.
    Proceedings of The 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021)
    [acm dl]     [tool]
  3. Compiling Symbolic Execution with Staging and Algebraic Effects.
    Guannan Wei, Oliver Bračevac, Shangyin Tan, Tiark Rompf.
    Proceedings of the ACM on Programming Languages, Volume 4 (OOPSLA 2020).
    [acm dl]     [code]
Teaching - Purdue University
"Simplicity is prerequisite for reliability."
-- Edsger W. Dijkstra