Hyosoon Jang profile photo

Hyosoon Jang

Ph.D student

hyosoon.jang@kaist.ac.kr

About Me

I'm a Ph.D. student advised by Sungsoo Ahn at KAIST (2025.09~; previously at POSTECH, 2023.03~2025.09). My research focuses on drug discovery, with recent work on foundation models for molecular learning [P2] and prior work on generative modeling approaches including Large Language Models (LLMs) [P1,C4], Generative Flow Networks (GFlowNets) [C2,C3], Diffusion Probabilistic Models (DPMs) [C1], and Variational Autoencoders (VAEs) [J2]. I am also interested in machine learning for medical domains [J1].

Publications

indicates equal contribution.

[P2] Boltz is a Strong Baseline for Atom-level Representation Learning thumbnail

[P2] Boltz is a Strong Baseline for Atom-level Representation Learning

Hyosoon Jang, Hyunjin Seo, Yunhui Jang, Seonghyun Park, Sungsoo Ahn

Preprint

[C4] Self-Training Large Language Models with Confident Reasoning thumbnail

[C4] Self-Training Large Language Models with Confident Reasoning

Hyosoon Jang, Yunhui Jang, Sungjae Lee, Jungseul Ok, Sungsoo Ahn

EMNLP 2025 (Findings)

[P1] Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity thumbnail

[P1] Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity

Hyosoon Jang, Yunhui Jang, Jaehyung Kim, Sungsoo Ahn

Preprint

[C3] Pessimistic Backward Policy for GFlowNets thumbnail

[C3] Pessimistic Backward Policy for GFlowNets

Hyosoon Jang, Yunhui Jang, Minsu Kim, Jinkyoo Park, Sungsoo Ahn

NeurIPS 2024

[C2] Learning Energy Decomposition for Partial Inference in GFlowNets thumbnail

[C2] Learning Energy Decomposition for Partial Inference in GFlowNets

Hyosoon Jang, Minsu Kim, Sungsoo Ahn

ICLR 2024 (oral presentation, 85/7262=1.16%)

[C1] Diffusion Probabilistic Models for Structured Node Classification thumbnail

[C1] Diffusion Probabilistic Models for Structured Node Classification

Hyosoon Jang, Seonghyun Park, Sangwoo Mo, Sungsoo Ahn

NeurIPS 2023

[J2] De novo Drug Design through Gradient-based Regularized Search in Information-theoretically Controlled Latent Space thumbnail

[J2] De novo Drug Design through Gradient-based Regularized Search in Information-theoretically Controlled Latent Space

Hyosoon Jang, Sangmin Seo, Sanghyun Park, Byung Ju Kim, Geon-Woo Choi, Jonghwan Choi, Chihyun Park

Journal of Computer-Aided Molecular Design 38 (1)

[J1] Machine learning algorithms using systemic inflammatory markers for predicting the oncological outcomes of colorectal cancer after surgery thumbnail

[J1] Machine learning algorithms using systemic inflammatory markers for predicting the oncological outcomes of colorectal cancer after surgery

Songsoo Yang, Hyosoon Jang, Inkyu Park, Sunhye Lee, Gaeul Oh, Chihyun Park*, Jeonghyun Kang*

Annals of Surgical Oncology 30 (13)

Invited Talks

UMICH, Stella group, GFlowNets, Jan 2025
ICLR, Oral session, Learning Energy Decompositions for Partial Inference in GFlowNets, May 2024
MILA, GFlowNet reading group, Learning Energy Decompositions for Partial Inference in GFlowNets, Apr 2024

Honors and Awards

Postechian Fellowship, 2024
Presidential Science Scholarship, 2024
National Excellence Scholarship (Natural Sciences and Engineering), 2021-2022
Merit-based Scholarship, 2019-2020