Wonyeol Lee
Assistant Professor
Foundations of Programming & Computing Lab
Computer Science & Engineering,
POSTECH
I am actively recruiting motivated students,
including undergraduate interns
Ideal students have backgrounds in Programming Languages or Mathematics, and interests in programs or computations.
If you are interested, please feel free to send me an email. I would be happy to chat with you!
Education
2014–2023: Ph.D. in Computer Science, Stanford University
(3-year leave for military service) 2010–2014: B.S. in Computer Science and Mathematics, POSTECH
Employment
2024–Present: Assistant Professor, POSTECH
2023–2024: Postdoctoral Associate, Carnegie Mellon University
2017–2020: Research Scientist, KAIST
(for military service) 2017: Research Intern, Microsoft Research India
2016: Research Intern, Microsoft Research Redmond
Research
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Overview Slides
My research spans Programming Languages, Machine Learning, and Continuous Computations. In particular, I aim to make continuous computations more reliable and scalable. To this end, I study a wide range of fundamental computations across diverse areas (e.g., machine learning and scientific computing), with a focus on their mathematical properties (e.g., 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:
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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]
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Mathematical Properties
Correctness [program analysis | real analysis]
Efficiency
Fundamental Limits [intractability | universal approximation]
Students
MS Students: Bongseok Seon (2025.02–)
Interns: Doyoung Kim (2025.02–), Donghyun Ahn (2025.06–)
Alumni: Chiho Seong (2025, Intern)
Publications
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) [포스텍 첫 번째 CAV 정규논문] [ | | ]
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) []
Random Variate Generation with Formal Guarantees Feras Saad, Wonyeol Lee PLDI 2025 (ACM Conference on Programming Language Design and Implementation) [포스텍 첫 번째 PLDI 논문] [ | | | ]
Semantics of Integrating and Differentiating Singularities Jesse Michel, Wonyeol Lee†, Hongseok Yang PLDI 2025 (ACM Conference on Programming Language Design and Implementation) [포스텍 첫 번째 PLDI 논문] [ | | | ]
What Does Automatic Differentiation Compute for Neural Networks? Sejun Park, Sanghyuk Chun, Wonyeol Lee ICLR 2024 (Spotlight) (International Conference on Learning Representations) [ | ]
Expressive Power of ReLU and Step Networks under Floating-Point Operations Yeachan Park, Geonho Hwang, Wonyeol Lee, Sejun Park Neural Networks, 2024 []
Reasoning About Floating Point in Real-World Systems Wonyeol Lee PhD Dissertation, 2023 [ | ]
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) [ | ]
Training with Mixed-Precision Floating-Point Assignments Wonyeol Lee, Rahul Sharma, Alex Aiken TMLR, 2023 (Transactions on Machine Learning Research) [ | ]
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) [ | | | ]
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) [ | | ]
Differentiable Algorithm for Marginalising Changepoints Hyoungjin Lim, Gwonsoo Che, Wonyeol Lee, Hongseok Yang AAAI 2020 (AAAI Conference on Artificial Intelligence) []
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) [카이스트 세 번째 POPL 논문] [ | | | ]
Reparameterization Gradient for Non-Differentiable Models Wonyeol Lee, Hangyeol Yu, Hongseok Yang NeurIPS 2018 (Annual Conference on Neural Information Processing Systems) [ | | ]
On Automatically Proving the Correctness of math.h Implementations Wonyeol Lee, Rahul Sharma, Alex Aiken POPL 2018 (ACM Symposium on Principles of Programming Languages) [ | | ]
Verifying Bit-Manipulations of Floating-Point Wonyeol Lee, Rahul Sharma, Alex Aiken PLDI 2016 (ACM Conference on Programming Language Design and Implementation) [ | | ]
A Proof System for Separation Logic with Magic Wand Wonyeol Lee, Sungwoo Park POPL 2014 (ACM Symposium on Principles of Programming Languages) [포스텍 두 번째 POPL 논문] []
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) [ | | ]
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) [ | ]
Professional Activities
Program Committee: POPL (2026)
External Reviewer: POPL (2025, 2022), PLDI (2023), CAV (2019), ESOP (2020)
Reviewer: NeurIPS (2025, 2022, 2021), ICML (2024, 2023, 2022)
Teaching
CSED331: Algorithms (Fall 2025)
CSED490: Continuous Computations (Spring 2025)