Jaewook Lee

OptiML Lab / KAIST AI

Jaewook Lee

Biography.

Hello, my name is Jaewook (David) Lee. I am an incoming PhD student at Stanford EE (starting at Sep 2025). I recently finished my Master's degree in Artificial intelligence at KAIST AI, where I was extremely fortunate to be advised by Prof. Chulhee Yun. Beforehand, I completed my B.S. in Electrical Engineering and Mathematical Sciences (Double Major) at KAIST.

I am interested in optimization theory including both convex/nonconvex & stochastic optimization algorithms and applications to practical settings in AI and/or deep learning theory. Recently I have been particularly interested in Wasserstein gradient flows. I am also interested in minimax optimization and similar topics like control/operator theory & variational inequalities, multi-player games & multi-agent learning, and block coordinate descent algorithms. I am also always eager to learn more about any other interesting optimization, ML/DL theory or math-related topics!

Curriculum Vitae

Education.

  • Feb 2025
    Korea Advanced Institute of Science and Technology, Seoul, South Korea
    M.S. in Artificial Intelligence

    GPA: 4.25/4.3

  • Feb 2023
    Korea Advanced Institute of Science and Technology, Daejeon, South Korea
    B.S. in Electrical Engineering & Mathematical Sciences (Double Major)

    GPA: 4.07/4.3, Summa Cum Laude

    Graduated with Excellence in Leadership & Volunteering

  • Feb 2018
    Graduated Sejong Science High School, Seoul, South Korea

Publications.

Topics of Interest.

Convex/Nonconvex Optimization

I am interested in theoretical analysis and design of deterministic/stochastic optimization algorithms for convex and nonconvex functions with faster convergence and/or computational efficiency.

Minimax Optimization

I am interested in minimax optimization algorithms, similar problem classes including fixed point problems or variational inequalities, and broader related topics including multi-player games and multi-agent learning.

Optmization for ML/DL

I am interested in applying optimization & theoretical perspectives to machine/deep learning problems, including theoretical analysis and the optimization dynamics of transformers or diffusion models.

Wasserstein GF

I am interested in optimal transport theory and Wasserstein gradient flows. In particular, I am studying optimization algorithms on Wasserstein spaces and applications to deep learning theory as in mean field neural networks.

Experience.

OptiML Lab
KAIST AI

Research Intern

Jun 2022 - Feb 2023

My main research topic in OptiML Lab was the investigation of worst-case convergence lower bounds of gradient-based optimization algorithms, which involves convergence analysis in pathological cases specifically designed for the algorithm to show its worst performance.

MLILAB
KAIST AI

Research Intern

Jul 2021 - Mar 2022

In MLILAB, I mainly studied and implemented visual data generation models based on 3D morphable face models and neural renderers, specifically aiming to achieve better-performing expression/identity swapping between different images or frames, such as talking head generation or face swapping.

Awards & Honors.

KAIST Math PoW: 3rd Prize - Fall 2021

Weekly math competition in KAIST, open to all undergraduate/graduate students

Academic Excellence Scholarship, KAIST - Fall 2020

Scholarship, awarded to the top 4 students in KAIST EE

Dean's List Award, KAIST - Fall 2019, Fall 2020, Spring 2021

Awarded to the top 2% students in KAIST EE

Freshman Dean's List Award, KAIST - Fall 2018

Awarded to the top 2% students among KAIST freshmen

Contact.

  • 99rma37@kaist.ac.kr
  • +82-10-3539-1857
  • 85 Hoegi-ro, Dongdaemun-gu, Seoul, South Korea