Wonyeol Lee

Assistant Professor
Computer Science & Engineering, POSTECH
wonyeol.lee.cs@gmail.com  |  CV

I am an Assistant Professor at POSTECH, where I lead the Foundations of Programming & Computing (FPC) Lab. Before joining POSTECH, I was a Postdoctoral Associate at Carnegie Mellon University, working with Feras Saad. I received my PhD degree in Computer Science from Stanford University, advised by Alex Aiken. During my PhD, I also spent time at KAIST for military service, working with Hongseok Yang. Prior to that, I obtained my BS degree in Computer Science and Mathematics from POSTECH.

I am actively recruiting motivated and talented students, including undergraduate interns.

• Ideal students have strong backgrounds in CS and Math, and a strong interest in programs and computations.

• If you are interested in joining my group, I strongly recommend working with me for 3+ months beforehand.

Research

My research aims to make continuous computations more reliable and scalable. To this end, I work across several areas, such as programming languages (PL) and machine learning (ML), and study diverse continuous computations with a focus on their mathematical properties, such as correctness and efficiency.

Broadly, my interests fall into three directions:

• Analyze existing continuous computations from a theoretical perspective.

• Design new continuous computations with theoretical guarantees.

• Understand the fundamental limits of continuous computations.

Specifically, I explore topics such as:

• Continuous Computations

  • Continuous Computing [floating point | math library | neural network]

  • Differentiable Computing [non-differentiability | automatic differentiation | gradient estimation]

  • Probabilistic Computing [random variate generation | probabilistic inference]

• Mathematical Properties

  • Correctness [program analysis | real analysis]

  • Efficiency

  • Fundamental Limits [intractability | universal approximation]

Teaching

• CSED331: Algorithms (2025F)

• CSED490V: Continuous Computations (2025S)

Students

• MS Students: Bongseok Seon (2025.02–)

• Interns: Doyoung Kim (2025.02–), Donghyun Ahn (2025.06–)

• Alumni: Chiho Seong (2025, Intern)

Publications

1. Floating-Point Neural Networks Are Provably Robust Universal Approximators Geonho Hwang*, Wonyeol Lee*, Yeachan Park, Sejun Park, Feras Saad CAV 2025 (International Conference on Computer Aided Verification) [paper | slides | code]

2. Floating-Point Neural Networks Can Represent Almost All Floating-Point Functions Geonho Hwang, Yeachan Park, Wonyeol Lee, Sejun Park ICML 2025 (International Conference on Machine Learning) [paper]

3. Random Variate Generation with Formal Guarantees Feras Saad, Wonyeol Lee PLDI 2025 (ACM Conference on Programming Language Design and Implementation) [paper | slides | video | code]

4. Semantics of Integrating and Differentiating Singularities Jesse Michel, Wonyeol Lee^†, Hongseok Yang PLDI 2025 (ACM Conference on Programming Language Design and Implementation) [paper | slides | video | code]

5. What Does Automatic Differentiation Compute for Neural Networks? Sejun Park, Sanghyuk Chun, Wonyeol Lee ICLR 2024 (Spotlight) (International Conference on Learning Representations) [paper | code]

6. Expressive Power of ReLU and Step Networks under Floating-Point Operations Yeachan Park, Geonho Hwang, Wonyeol Lee, Sejun Park Neural Networks, 2024 [paper]

7. Reasoning About Floating Point in Real-World Systems Wonyeol Lee PhD Dissertation, 2023 [paper | slides]

8. On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters Wonyeol Lee, Sejun Park, Alex Aiken ICML 2023 (International Conference on Machine Learning) [paper | slides]

9. Training with Mixed-Precision Floating-Point Assignments Wonyeol Lee, Rahul Sharma, Alex Aiken TMLR, 2023 (Transactions on Machine Learning Research) [paper | code]

10. Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference Wonyeol Lee, Xavier Rival, Hongseok Yang POPL 2023 (ACM Symposium on Principles of Programming Languages) [paper | slides | video | code]

11. On Correctness of Automatic Differentiation for Non-Differentiable Functions Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang NeurIPS 2020 (Spotlight) (Annual Conference on Neural Information Processing Systems) [paper | slides | video]

12. Differentiable Algorithm for Marginalising Changepoints Hyoungjin Lim, Gwonsoo Che, Wonyeol Lee, Hongseok Yang AAAI 2020 (AAAI Conference on Artificial Intelligence) [paper]

13. Towards Verified Stochastic Variational Inference for Probabilistic Programs Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang POPL 2020 (ACM Symposium on Principles of Programming Languages) [paper | slides | video | code]

14. Reparameterization Gradient for Non-Differentiable Models Wonyeol Lee, Hangyeol Yu, Hongseok Yang NeurIPS 2018 (Annual Conference on Neural Information Processing Systems) [paper | slides | code]

15. On Automatically Proving the Correctness of math.h Implementations Wonyeol Lee, Rahul Sharma, Alex Aiken POPL 2018 (ACM Symposium on Principles of Programming Languages) [paper | slides (short) | video]

16. Verifying Bit-Manipulations of Floating-Point Wonyeol Lee, Rahul Sharma, Alex Aiken PLDI 2016 (ACM Conference on Programming Language Design and Implementation) [paper | slides | video]

17. A Proof System for Separation Logic with Magic Wand Wonyeol Lee, Sungwoo Park POPL 2014 (ACM Symposium on Principles of Programming Languages) [paper]

18. CT-IC: Continuously Activated and Time-Restricted Independent Cascade Model for Viral Marketing Wonyeol Lee, Jinha Kim, Hwanjo Yu ICDM 2012 (IEEE International Conference on Data Mining) [paper | journal | slides]

19. Edge Detection Using Morphological Amoebas in Noisy Images Wonyeol Lee, Seyun Kim, Youngwoo Kim, Jaeyoung Lim, Dong Hoon Lim ICIP 2009 (IEEE International Conference on Image Processing) [paper | journal]

trips | last updated: 08/2025