Wonyeol Lee

Incoming Assistant Professor (from November 2024)
Computer Science Department, POSTECH

Postdoctoral Associate
Computer Science Department, CMU

wonyeol.lee.cs@gmail.com  |  CV

I am recruiting motivated and talented students at all levels. If you are interested,
please email me in advance with your CV, transcript, and research interests.

About Me

I am a Postdoctoral Associate at CMU, working with Feras Saad. I received my PhD in Computer Science from Stanford University, working under Alex Aiken. During my PhD, I also spent time at KAIST for military service, working with Hongseok Yang. Before that, I obtained my BS degree in Computer Science and Mathematics from POSTECH.

My research aims at making continuous computations more reliable and more scalable. Towards this goal, I consider a wide range of continuous computations arising in different areas (e.g., programming languages and machine learning), and study their correctness and efficiency in three main directions:

• Analyze existing, practical continuous computations from theoretical perspectives.

• Design new, practical continuous computations that have theoretical guarantees.

• Understand the fundamental limits of continuous computations.

Research Interests

• Continuous Computations

  • Continuous (floating point | math library | neural network)

  • Differentiable (non-differentiability | automatic differentiation | gradient estimation)

  • Probabilistic (random variate | variational inference)

• Correctness & Efficiency

  • Provable Guarantee (verification | program analysis)

Publications

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

2. What Does Automatic Differentiation Compute for Neural Networks? Sejun Park, Sanghyuk Chun, Wonyeol Lee ICLR 2024 (Spotlight) [paper | code]

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

4. On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters Wonyeol Lee, Sejun Park, Alex Aiken ICML 2023 [paper | slides]

5. Training with Mixed-Precision Floating-Point Assignments Wonyeol Lee, Rahul Sharma, Alex Aiken TMLR, 2023 [paper | code]

6. Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference Wonyeol Lee, Xavier Rival, Hongseok Yang POPL 2023 [paper | slides | video | code]

7. On Correctness of Automatic Differentiation for Non-Differentiable Functions Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang NeurIPS 2020 (Spotlight) [paper | slides | video]

8. Differentiable Algorithm for Marginalising Changepoints Hyoungjin Lim, Gwonsoo Che, Wonyeol Lee, Hongseok Yang AAAI 2020 [paper]

9. Towards Verified Stochastic Variational Inference for Probabilistic Programs Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang POPL 2020 [paper | slides | video | code]

10. Reparameterization Gradient for Non-Differentiable Models Wonyeol Lee, Hangyeol Yu, Hongseok Yang NeurIPS 2018 [paper | slides | code]

11. On Automatically Proving the Correctness of math.h Implementations Wonyeol Lee, Rahul Sharma, Alex Aiken POPL 2018 [paper | slides (short) | video]

12. Verifying Bit-Manipulations of Floating-Point Wonyeol Lee, Rahul Sharma, Alex Aiken PLDI 2016 [paper | slides | video]

13. A Proof System for Separation Logic with Magic Wand Wonyeol Lee, Sungwoo Park POPL 2014 [paper]

14. CT-IC: Continuously Activated and Time-Restricted Independent Cascade Model for Viral Marketing Wonyeol Lee, Jinha Kim, Hwanjo Yu ICDM 2012 [paper | journal | slides]

15. Edge Detection Using Morphological Amoebas in Noisy Images Wonyeol Lee, Seyun Kim, Youngwoo Kim, Jaeyoung Lim, Dong Hoon Lim ICIP 2009 [paper | journal]

trips | last updated: 04/2024