배준현 · Jun-Hyun Bae · Junhyun Bae

AI/ML PhD Candidate @ Kyungpook National University

Research Interests

My research investigates the robustness and interpretability of deep learning models, with the goal of making their behavior more reliable and controllable. My current focus is mechanistic interpretability, which seeks to characterize how neural networks encode semantic concepts within their internal components. A representative direction is the analysis of cross-attention OV circuits in text-to-image diffusion models, where semantic concepts are represented within low-dimensional spectral subspaces, enabling targeted interventions such as concept removal without retraining. This work builds on my earlier research on robust generalization, including modular architectures for systematic and compositional reasoning and methods grounded in causal inference and invariance for out-of-distribution generalization and debiasing. My broader objective is to connect interpretability with the design of safe and trustworthy models.

Education

Publications

Competitions

  • NeurIPS 2023 Machine Unlearning Challenge — 8th place (out of 1,121 teams), Google
  • AI Hackathon for Fashion Coordination (2020) — 3rd place, ETRI
  • AI Hackathon for Speech Recognition (2019) — 10th place, NAVER

Scholarships & Fellowships

  • CMU Visiting Scholar Program (2022) — Fully funded by Korean Government (IITP)
  • Full-Ride Scholarship (2019 – 2023) — Graduate, Academic Excellence, KNU
  • KNU+ Honors Scholarship (2015 – 2019) — Merit-based full-tuition award with semester stipend for outstanding entering students, KNU

Academic Service

Reviewer

  • ICML 2026 (Gold Reviewer)
  • ICML 2026 Mechanistic Interpretability Workshop
  • AAAI 2026

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