AI Research Scientist at Lunit. M.S. in Artificial Intelligence from KAIST.
I study how foundation models generalize, adapt to new information, and use evidence to guide reliable behavior. My work spans representation learning, post-training and controlled evaluation, with biomedical systems as a demanding application domain.
Entity-attribution failures in clinical retrieval-augmented generation: genuine evidence about one medical entity can be used to answer a question about another. First-author study connecting controlled experiments with evaluation in a clinical system.Entity-attribution failures in clinical retrieval-augmented generation: genuine evidence about one medical entity can be used to answer a question about another. First-author study connecting controlled experiments with evaluation in a clinical system.
Separating routing divergence from its contribution to model behavior through exact decompositions and controlled analyses. The repository includes frozen research artifacts, deterministic reductions and CPU-based replay.
Paper · Code · Project page
Cross-dataset representation learning for Cell Painting, combining source-context tokens with self-supervised and contrastive learning.
Paper · Code · Project page
An independent experimental platform built on the MazeBench engine, with trajectory replay, context-compaction logs and branchable continuations. I use it to study agent behavior through paired-history diagnostics and controlled changes to observations and memory.
Latest experimental write-up · Code
At Lunit, I lead medical language-model post-training and evaluation, including training-data synthesis, self-distillation, distributed execution, task-specific benchmarks and hospital collaborations. I also work on self-supervised Vision Transformers for whole-slide pathology.
Independently, I am investigating when an agent's correct explanation changes its next action, and what must be preserved when its context is compressed.
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In the fields of observation, chance favors only the prepared mind.