
Template Infilling
Template Infilling is a conditioning method for diffusion language models that places structural anchors throughout the target response before filling masked segments.
Ph.D. Candidate · Seoul National University
Machine learning researcher at MIPAL, advised by Prof. Nojun Kwak. Ph.D. expected August 2026.
I work on diffusion language models, meta-learning, and methods for analyzing and attributing pretrained models. I also work on vision-language-action models.


Template Infilling is a conditioning method for diffusion language models that places structural anchors throughout the target response before filling masked segments.

CSF attributes fine-tuned text-to-image models to their base-model lineages using only query access and compositional, underspecified prompts.

DeepKKT adapts the Karush–Kuhn–Tucker condition to identify or generate Deep Support Vectors from trained deep classifiers.
, Seungyeon Kim, Nojun Kwak · Long Paper, Oral Presentation
, Mijin Koo, Nojun Kwak · Query-Only Attribution
Dongkwan Lee†, , Nojun Kwak · Representation Analysis
Hahyeon Choi, , Nojun Kwak · Audio-Visual Localization
, Hyunho Lee, Kyomin Hwang, Nojun Kwak · Isometry
, Yearim Kim, Hyunho Lee, Nojun Kwak · Few-Shot Learning
, Jayeon Yoo, Nojun Kwak · Optimization
Ph.D. Candidate in Intelligence and Information
Seoul National University · Advisor: Prof. Nojun Kwak
B.Sc. in Electrical and Computer Engineering
Seoul National University
BK21 Future Innovation Talent Bronze Prize
BK21 Outstanding Research Talent Fellowship
Yulchon AI Star Scholarship