I am a machine learning researcher at AITRICS, a healthcare AI startup in Korea, where I am fulfilling my alternative military service under Prof. Eunho Yang. Before joining AITRICS, I obtained my master’s degree in Artificial Intelligence from KAIST, also under the guidance of Prof. Eunho Yang. I completed my bachelor’s degree in Computer Science and Mathematical Sciences at KAIST as well. I am open to research collaborations globally, including remote opportunities. If you are interested in my work, please feel free to reach out!

My long-term research objective is to enhance the out-of-distribution generalization capability of machine learning models, thereby creating trustworthy AI systems that can be reliably deployed in new environments. This multifaceted goal involves ensuring robustness to distribution shifts (domain adaptation and generalization), handling unseen labels (zero-shot learning), and managing unseen tasks (cross-task generalization). During my master’s program, I focused on test-time adaptation to distribution shifts across various tasks, including 3D point cloud recognition, zero-shot transfer of vision-language models, automatic speech recognition, tabular learning, and time series classification. These experiences have also enabled myself to quickly adapt to new modalities and tasks.

At AITRICS, my research centers on improving the generalizability, robustness, and explainability of early prediction models for critical clinical outcomes such as cardiac arrests and in-hospital mortality. I am also interested in parameter- and data-efficient adaptation of deep generative models, such as diffusion models and large multimodal models, to downstream tasks. To achieve these goals, I work on developing practical algorithms that are empirically well-grounded or theoretically provable. Additionally, I am passionate about providing theoretical insights into machine learning models through the lens of probabilistic (Bayesian inference) and statistical (generalization bounds) frameworks.