About Me

I am an assistant professor at University of California - Riverside. I joined UCR in Fall 2025. My primary research interest is in developing fundamental principles to build a more robust software systems. I am especially interested in widely used systems that people use assuming its correctness spanning anywhere from compilers, open-source libraries, or even tools that is acceptable to produce some error (e.g. math.h). My goal is to ensure these existing systems are indeed correct and develop techniques to build systems that are correct-by-construction.

My research on building correctly rounded math library have been recognized with two distinguished paper awards (PLDI 2021, POPL 2022) and 2022 ACM SIGPLAN John C. Reynolds Doctoral Dissertation award . Several elementary functions developed through our RLibm project is now implemented in LLVM's libc.

Prior to UCR, I was a lecturer at Yale University since Fall 2021. I received my Ph.D. at Rutgers University on 2021, advised by Santosh Nagarakatte.

Students

I am always looking for PhD students. If you are a motivated students with strong CS background, and are excited to make computer systems robust and correct, please reach out and introduce yourself!

Awards

• 2022 ACM SIGPLAN John C. Reynolds Doctoral Dissertation Award: Novel Polynomial Approximation Methods for Generating Correctly Rounded Elementary Functions. PDF
• Distinguished Paper Award at POPL 2022: One Polynomial Approximation to Produce Correctly Rounded Results of an Elementary Function for Multiple Representations and Rounding Modes. PDF DOI
• Distinguished Paper Award at PLDI 2021: High Performance Correctly Rounded Math Libraries for 32-bit Floating Point Representations. PDF DOI
• Rutgers University Rizvi Family Graduate Fellowship
• Student Research Competition Gold Medal at PLDI 2018: Automatic Verification of Assembly Implementation of Cryptographic Algorithms

Publications

• Progressive Polynomial Approximations for Fast Correctly Rounded Math Libraries.
  Mridul Aanjaneya, Jay P. Lim, and Santosh Nagarakatte.
  Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation (PLDI), 2022.
  PDF DOI
• Distinguished paper award
  One Polynomial Approximation to Produce Correctly Rounded Results of an Elementary Function for Multiple Representations and Rounding Modes.
  Jay P. Lim and Santosh Nagarakatte.
  9th ACM SIGPLAN Symposium on Principles of Pro- gramming Languages (POPL), 2022.
  PDF DOI
• Distinguished paper award
  High Performance Correctly Rounded Math Libraries for 32-bit Floating Point Representations.
  Jay P. Lim and Santosh Nagarakatte.
  Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation (PLDI), 2021.
  PDF DOI
• An Approach to Generate Correctly Rounded Math Libraries for New Floating Point Variants.
  Jay P. Lim, Mridul Aanjaneya, John Gustafson, and Santosh Nagarakatte. 2021.
  48th ACM SIGPLAN Symposium on Principles of Programming Languages (POPL 2021).
  PDF DOI
• Approximating trigonometric functions for posits using the CORDIC method.
  Jay P. Lim, Matan Shachnai, and Santosh Nagarakatte. 2020.
  In Proceedings of the 17th ACM International Conference on Computing Frontiers (CF 2020).
  PDF DOI
• Debugging and Detecting Numerical Errors in Computation with Posits.
  Sangeeta Chowdhary, Jay P Lim, and Santosh Nagarakatte. 2020.
  In Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2020).
  PDF DOI
• Automatic Equivalence Checking for Assembly Implementations for Cryptography Libraries.
  Jay P Lim and Santosh Nagarakatte. 2019.
  Proceedings of the International Symposium on Code Generation (CGO 2019).
  PDF DOI
• Compiler Optimizations with Retrofitting Transformation: Is there a Semantic Mismatch?
  Jay P Lim, Santosh Nagarakatte, and Vinod Ganapathy. 2017.
  ACM SIGSAC Workshop and Programming Languages and Analysis for Security (PLAS 2017).
  PDF DOI

PhD Dissertation

• ACM Sigplan John C. Reynolds Docotral Dissertation Award
  Novel Polynomial Approximation Methods for Generating Correctly Rounded Elementary Functions.
  PhD Dissertation, Rutgers, the State University of New Jersey, August 2021
  PDF

Technical Reports

• RLibm-Prog: Progressive Polynomial Approximations for Fast Correctly Rounded Math Libraries.
  Mridul Aanjaneya, Jay P Lim, and Santosh Nagarakatte.
  Department of Computer Science, Rutgers University, Technical Report DCS-TR-758, Nov. 2021
  PDF ArXiv
• RLIBM-ALL: A Novel Polynomial Approximation Method to Produce Correctly Rounded Results for Multiple Representations and Rounding Modes.
  Jay P. Lim and Santosh Nagarakatte.
  Department of Computer Science, Rutgers University, Technical Report DCS-TR-757, August 2021
  PDF ArXiv
• RLIBM-32: High Performance Correctly Rounded Math Libraries for 32-bit Floating Point Representations.
  Jay P. Lim and Santosh Nagarakatte.
  Department of Computer Science, Rutgers University, Technical Report DCS-TR-754, April 2021
  PDF ArXiv
• A Novel Approach to Generate Correctly Rounded Math Libraries for New Floating Point Representations.
  Jay P. Lim, Mridul Aanjaneya, John Gustafson, and San- tosh Nagarakatte.
  Department of Computer Science, Rutgers University, Technical Report DCS-TR-753, July 2020
  PDF ArXiv

Professional Activities

• University and Departmental Activities
• Fall 2021 - 2025: Yale University CS Academic Honesty Committee
• Summer 2022: Camp Yale - Build Program Advisor
• Program Committee Member
• 2024: PLDI'25, ARITH'25
• 2022: OOPSLA'23, SIGCSE TS'23
• 2021: OOPSLA'22

Teaching @ UCR

• Fall 2025: CS260 - Seminar in Computer Science
• See CV for previous courses I taught