Minseon Kim PhD @ KAIST (original) (raw)
Recent News!
🌟 Started as a senior researcher at Microsoft Research Montreal! Excited to join the team and work on advancing coding agents.
🚀 New safety paper! By extracting user intent and possible risks from prompts, we improve LLM safety across diverse models, without any additional training. Safety context generation
✍🏻 A short note for Korean grad students. 해외 인턴·취업 준비하면서 자주 받는 질문 정리. Q&A Blog Post
Keywords
Code Agent Safety Reasoning
Selected Publications
Learning to Extract Context for Context-aware LLM Inference
ArXiv 2025
M. Kim, L. Caccia, Z. Shi, M. Pereira, M.-A. Côté, X. Yuan, A. Sordoni
Align to Misalign: Automatic LLM Jailbreak with Meta-Optimized LLM Judges
ICLR 2026
H. Koo, M. Kim, J. Kim
Gistify! Codebase-Level Understanding via Runtime Execution
ICLR 2026
H. Lee, M. Kim, C. Singh, M. Pereira, A. Sonwane, I. White, E. Stengel-Eskin, M. Bansal, Z. Shi, A. Sordoni, M.-A. Côté, X. Yuan, L. Caccia
BugPilot: Complex Bug Generation for Efficient Learning of SWE Skills
ArXiv 2025
A. Sonwane, I. White, H. Lee, M. Pereira, L. Caccia, M. Kim, Z. Shi, C. Singh, A. Sordoni, M.-A. Côté, X. Yuan
BlurGuard: A Simple Approach for Robustifying Image Protection Against AI-Powered Editing
NeurIPS 2025
J. Kim, Y. Nam, M. Kim, S. Kim, J. Jeong
Learning to Solve Complex Problems via Dataset Decomposition
NeurIPS 2025
W. Zhao, L. Caccia, Z. Shi, M. Kim, X. Yuan, W. Xu, M.-A. Côté, A. Sordoni
Rethinking Safety in LLM Fine-tuning: An Optimization Perspective
CoLM 2025
M. Kim, J. M. Kwak, L. Alssum, B. Ghanem, P. Torr, D. Krueger, F. Barez, A. Bibi
Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts
CoLM 2025
S. Y. Arnob, Z. Su, M. Kim, O. Ostapenko, D. Precup, L. Caccia, A. Sordoni
MedRiskEval: Medical Risk Evaluation Benchmark of Language Models, On the Importance of User Perspectives in Healthcare Settings
EACL 2026
J.-P. Corbeil*, M. Kim*, M. Griot, S. Agarwal, A. Sordoni, F. Beaulieu, P. Vozila
Instilling Parallel Reasoning into Language Models
ICML AI for Math WS 2025
M. Macfarlane, M. Kim, N. Jojic, W. Xu, L. Caccia, X. Yuan, W. Zhao, Z. Shi, A. Sordoni
Enhancing Variational Autoencoders with Smooth Robust Latent Encoding
arXiv 2025
H. Lee*, M. Kim*, S. Jang, J. Jeong, S. J. Hwang
debug-gym: A Text-Based Environment for Interactive Debugging
arXiv 2025
X. Yuan, M. M. Moss, C. El Feghali, C. Singh, D. Moldavskaya, D. MacPhee, L. Caccia, M. Pereira, M. Kim, A. Sordoni, M.-A. Côté
Automatic Jailbreaking of the Text-to-Image Generative AI Systems
ICML Safety WS 2024
M. Kim, H. Lee, B. Gong, H. Zhang, S. J. Hwang
Optimizing Query Generation for Enhanced Document Retrieval in RAG
arXiv 2024
H. Koo, M. Kim, S. J. Hwang
Protein Representation Learning by Capturing Protein Sequence‑Structure‑Function Relationship
ICLR MLGenX WS 2024 (Spotlight)
E. Ko*, S. Lee*, M. Kim*, D. Kim, S. J. Hwang
Effective Targeted Attacks for Adversarial Self‑Supervised Learning
NeurIPS 2023
M. Kim, H. Ha, S. Son, S. J. Hwang
Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
NeurIPS 2023
H. Ha*, M. Kim*, S. J. Hwang
Language Detoxification with Attribute‑Discriminative Latent Space
ACL 2023
M. Kim*, J. M. Kwak*, S. J. Hwang
Context‑dependent Instruction Tuning for Dialogue Response Generation
arXiv 2023
J. M. Kwak, M. Kim, S. J. Hwang
Meta‑Prediction Model for Distillation‑aware NAS on Unseen Datasets
ICLR 2023 (Spotlight)
H. Lee*, S. An*, M. Kim, S. J. Hwang
Rethinking the Entropy of Instance in Adversarial Training
IEEE SaTML 2023
M. Kim, J. Tack, J. Shin, S. J. Hwang
Lightweight Neural Architecture Search with Parameter Remapping and Knowledge Distillation
AutoML WS 2022
H. Lee*, S. An*, M. Kim, S. J. Hwang
Learning Transferable Adversarial Robust Representations via Multi‑view Consistency
NeurIPS SafetyML WS 2022
M. Kim*, H. Ha*, D. B. Lee, S. J. Hwang
Consistency Regularization for Adversarial Robustness
AAAI 2022
J. Tack, S. Yu, J. Jeong, M. Kim, S. J. Hwang, J. Shin
MRI‑based classification of neuropsychiatric systemic lupus erythematosus patients with self‑supervised contrastive learning
Frontiers in Neuroscience 2022
M. Kim*, F. Inglese*, G. Steup‑Beekman, T. Huizinga, M. Van Buchem, J. Bresser, D. Kim, I. Ronen
Adversarial Self‑Supervised Contrastive Learning
NeurIPS 2020
M. Kim, J. Tack, S. J. Hwang
Progressive Face Super‑Resolution via Attention to Facial Landmark
BMVC 2019
D. Kim*, M. Kim*, G. Kwon*, D. Kim
T1 Image Synthesis with Deep Convolutional Generative Adversarial Networks
OHBM 2018
M. Kim, C. Han, J. Park, D.-S. Kim
Experience
Postdoctoral Researcher — Microsoft Research
Oct 2024-Feb 2026 • with MSR Montréal ML Team
Research Internship — ERA–KASL AI Safety Research, University of Oxford
Jun–Aug 2024 • with Philip Torr, David Krueger, Adel Bibi, Fazl Barez
Research Collaboration — Theory Center, Microsoft Research Asia
Jul 2023–May 2024 • with Huishuai Zhang
Talks
AI Seminar, UNIST
Oct. 2025 — "Designing Safety Systems for LLM-based Services”
Mila X MSR, Microsoft
Oct. 2025 — “Learning to Extract Context for Context-aware LLM Inference”
Women in MSR – Project Green, Microsoft
Mar. 2025 — “Unsupervised Context Understanding for Safer LLMs”
Tea Talk, Mila
Feb. 2025 — “Designing safety systems for LLM-based services”
RWE AI Journal Club, Microsoft
Nov. 2024 — “How to obtain safety effectively and efficiently”
Guest Lecture, Korea University
May. 2024 — “Automatic Jailbreaking of the Text-to-Image Generative AI Systems”
Academic services
Conference
NeurIPS, ICLR, ICML, ACL, AAAI, ACML, ICCV
Journal
TPAMI, IEEE TNNNLS, TMLR, IEEE T-IFS, IEEE CIM
Education
- Ph.D., Graduate School of AI, KAIST — Thesis: Towards Safe and Robust Representation with Self‑Supervised Learning (Advisor: Sung Ju Hwang)
- M.S., Electrical Engineering, KAIST — Thesis: Differential representation of face pareidolia (Advisor: Dae‑shik Kim)
- B.S., Bio & Brain Engineering; Computer Science, KAIST
Contact
minseon5113(at)gmail(dot)com