Jaehyeong Jo

Jaehyeong Jo

PhD student at KAIST AI

About me

Hi, I am Jaehyeong Jo.
Here is my CV (Curriculum Vitae) and Google Scholar.

Email: harryjo97 [at] kaist.ac.kr

Twitter: JaehyeongJo

Last updated: Sep 19, 2025

Introduction

I am a PhD candidate in Graduate school of AI at KAIST (MLAI lab), where I am fortunate to be advised by Prof. Sung Ju Hwang. Previously, I was a Research Intern at Meta.

My research focuses on exploring the physical world through the lens of geometry. In my previous work, I developed diffusion models that incorporate geometric principles to generate structured data such as graphs and data on Riemannian manifolds. These models have been applied to a variety of real-world problems, including drug discovery, language modeling, and neural architecture search.

Preprints

  • Frame Guidance: Training-Free Guidance for Frame-Level Control in Video Diffusion Models

    [project page] [paper] [code]
    ​Sangwon Jang*, Taekyung Ki*, Jaehyeong Jo, Jaehong Yoon, Soo Ye Kim, Zhe Lin, Sung Ju Hwang
    Preprint 2025

Conference Publications

  • Continuous Diffusion Model for Language Modeling

    [paper] [code]
    ​Jaehyeong Jo, Sung Ju Hwang
    NeurIPS 2025

  • Silent Branding Attack: Trigger-free Data Poisoning Attack on Text-to-Image Diffusion Models

    [project page] [paper]
    ​Sangwon Jang, June Suk Choi, Jaehyeong Jo, Kimin Lee^, Sung Ju Hwang^
    CVPR 2025

  • Conditional Synthesis of 3D Molecules with Time Correction Sampler

    [paper]
    ​Hojung Jung*, Youngrok Park*, Laura Schmid, Jaehyeong Jo, Dongkyu Lee, Bongsang Kim, Se-Young Yun, Jinwoo Shin
    NeurIPS 2024

  • Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models

    [project page] [paper] [code]
    ​Sangwon Jang*, Jaehyeong Jo*, Kimin Lee^, Sung Ju Hwang^
    NeurIPS 2024

  • Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes

    [paper] [code]
    ​Jaehyeong Jo, Sung Ju Hwang
    ICML 2024

  • Graph Generation with Diffusion Mixture

    [paper] [code]
    ​Jaehyeong Jo*, Dongki Kim*, Sung Ju Hwang
    ICML 2024

  • DiffusionNAG: Task-guided Neural Architecture Generation with Diffusion Models

    [paper] [code]
    Sohyun An*, Hayeon Lee*, Jaehyeong Jo, Seanie Lee, Sung Ju Hwang
    ICLR 2024

  • Text-Conditioned Sampling Framework for Text-to-Image Generation with Masked Generative Models

    [project page] [paper]
    Jaewoong Lee*, Sangwon Jang*, Jaehyeong Jo, Jaehong Yoon, Yunji Kim, Jin-Hwa Kim, Jung-Woo Ha, Sung Ju Hwang
    ICCV 2023

  • Exploring Chemical Space with Score-based Out-of-distribution Generation

    [paper] [code]
    Seul Lee, Jaehyeong Jo, Sung Ju Hwang
    ICML 2023

  • Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

    [paper] [code] [talk]
    ​Jaehyeong Jo*, Seul Lee*, Sung Ju Hwang
    ICML 2022

  • Edge Representation Learning with Hypergraphs

    [paper] [code]
    ​Jaehyeong Jo*, Jinheon Baek*, Seul Lee*, Dongki Kim, Minki Kang, Sung Ju Hwang
    NeurIPS 2021

*: Equal contribution, ^: Equal advising

Workshop Publications

  • Antibody-SGM: Antigen-Specific Joint Design of Antibody Sequence and Structure Using Diffusion Models

    [paper]
    Xuezhi Xie, Jin Sub Lee, Dongki Kim, ​Jaehyeong Jo, Jisun Kim, Philip M. Kim
    ICML 2023 Workshop on Computational Biology