Currently, our interests lie broadly in machine learning, pattern recognition, computer vision, and medical image analysis.

Specifically, we have been focusing on developing theories, models, and methods for semi-supervised learning, weakly-supervised learning, domain adaptation, and domain generalization. Our goal is to create efficient and effective solutions to these challenges. We have also been developing machine learning-based tools to help address data analysis problems in medical imaging and clinical data. We hope that these tools can contribute towards building real-world systems for use in clinical scenarios.