About

Representation Analysis & Application Lab. @ Inha University

We study how to learn better representations—and how to understand what those representations encode.

Our research develops learning algorithms and architectures that enable machine learning models to acquire effective representations for diverse data domains and tasks. We also investigate how learned representations are organized and evolve during training, with information theory serving as a key analytical framework.

Our current research focuses on:

• Tabular Learning — prediction and generative modeling for tabular data
• Multimodal Foundation Models — learning across tabular, text, and time-series modalities
• Information-Theoretic Analysis of Representation Learning
• Representation Learning for Medical AI and Physical AI

📌 Prospective Students
Students interested in joining the lab as undergraduate researchers or MS/Ph.D. students may contact me via email. (학부 연구생 또는 석·박사 과정 진학에 관심 있는 학생은 메일로 연락 바랍니다.)