About Me

I’m a 3rd-year Ph.D. student in the Machine Learning and Artificial Intelligence (MLAI) lab at KAIST, under the supervision of Prof. Sung Ju Hwang. I’m also working as a research intern in the NVIDIA Fundamental Generative AI Research (GenAIR) team. I am fortunate to be a recipient of the NVIDIA Graduate Fellowship. My research interests include AI for science and generative models.

☕️ My email/DM are always open to anyone interested in discussion/collaboration!


Education

  • Ph.D. student, Graduate School of AI
    KAIST, Sep. 2022 - present.
  • M.S., Graduate School of AI
    KAIST, Mar. 2021 - Aug. 2022.
  • B.S., Aerospace Engineering / Biological Sciences (Double Major)
    KAIST, Mar. 2015 - Aug. 2019.


Research Experience

  • Research Intern, NVIDIA
    Feb. 2024 - Sep. 2025, Santa Clara, CA, US
    Research topic: Generative AI for science
    Mentors: Weili Nie, Karsten Kreis, and Arash Vahdat
  • Research Intern, AITRICS
    Jan. 2021 - Feb. 2021, South Korea
    Research topic: Docking-optimized molecule generation using RL


Publications

*: equal contribution

  • GenMol: A Drug Discovery Generalist with Discrete Diffusion
    Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach,
    Yuxing Peng, Saee Paliwal, Weili Nie†, and Arash Vahdat† (†: equal advising)
    ICML, 2025. [paper]
  • Molecule Generation with Fragment Retrieval Augmentation
    Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach,
    Saee Paliwal, Arash Vahdat†, and Weili Nie† (†: equal advising)
    NeurIPS, 2024. [paper][code][project page]
  • READRetro: Natural Product Biosynthesis Planning with
    Retrieval-Augmented Dual-View Retrosynthesis
    Seul Lee*, Taein Kim*, Min-Soo Choi, Yejin Kwak, Jeongbin Park, Sung Ju Hwang, and Sang-Gyu Kim
    New Phytologist, 2024 (IF=8.3). [paper][code][web]
  • Drug Discovery with Dynamic Goal-aware Fragments
    Seul Lee, Seanie Lee, Kenji Kawaguchi, and Sung Ju Hwang
    ICML, 2024. [paper][code]
    ICLR Machine Learning for Genomics Explorations Workshop (Spotlight), 2024.
  • Protein Representation Learning by Capturing Protein Sequence-Structure-Function Relationship
    Eunji Ko*, Seul Lee*, Minseon Kim*, Dongki Kim, and Sung Ju Hwang
    ICLR Machine Learning for Genomics Explorations Workshop (Spotlight), 2024. [paper]
  • A Simple and Scalable Representation for Graph Generation
    Yunhui Jang, Seul Lee, and Sungsoo Ahn
    ICLR, 2024. [paper]
    NeurIPS New Frontiers in Graph Learning Workshop, 2023.
  • Exploring Chemical Space with Score-based Out-of-distribution Generation
    Seul Lee, Jaehyeong Jo, and Sung Ju Hwang
    ICML, 2023. [paper][code]
    ICLR Machine Learning for Drug Discovery Workshop (Oral), 2023.
  • Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
    Jaehyeong Jo*, Seul Lee*, and Sung Ju Hwang
    ICML, 2022. [paper][code]
  • Edge Representation Learning with Hypergraphs
    Jaehyeong Jo*, Jinheon Baek*, Seul Lee*, Dongki Kim, Minki Kang, and Sung Ju Hwang
    NeurIPS, 2021. [paper][code]
  • Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation
    Soojung Yang, Doyeong Hwang, Seul Lee, Seongok Ryu, and Sung Ju Hwang
    NeurIPS, 2021. [paper][code]
  • Robotic Scanning Technology for Laser Pulse-Echo Inspection
    Seul Lee, Jong-min Hyun, Hasan Ahmed, and Jung-ryul Lee
    Electronics Letters, 2020. [paper]


Awards and Honors

  • NVIDIA Graduate Fellowship, 2025-2026.
  • KAIST Summa Cum Laude, Aug. 2019.
  • KAIST Dean’s List (College of Engineering), Aug. 2018.
  • KAIST Dean’s List (College of Engineering), Feb. 2018.
  • Boeing Undergraduate Scholarship, 2018-2019.
  • KAIST Presidential Fellowship (KPF), 2017-2019.
  • KAIST Dean’s List (College of Life Science & Bioengineering), Feb. 2017.
  • National Science and Engineering Undergraduate Scholarship, 2015-2019.