- Conducted research on Explainable Machine Learning.- Developed explanation algorithms for time-series ML models, focusing on characterizing changes as a form of explanation. Case studies with wind turbine failure predictions and MIMIC-IV datasets. - Developed Sibyl, a RESTful web application with Streamlit for ML explanations. Demo: https://sibylapp.streamlit.app - Developed Pyreal (https://github.com/sibyl-dev/pyreal), an explainable AI toolkit that provides easy-to-understand ML explanations, and designed user studies to evaluate Pyreal.

- Designed deep learning models with PyTorch to perform arterial calcification segmentation on CT images. - Worked with radiologists through active learning to efficiently label huge datasets and train computer vision models. - Designed, trained, and tested deep learning models using PyTorch to help predict the blood pressure of patients.- Implemented time-series signals machine learning models such as LSTM and attention models.

- Developed independent research on autonomous robots under the supervision of Professor Li-Chen Fu.- Used ROS to create a robotic system that integrates exploration, SLAM (Simultaneous Localization and Mapping), and object recognition algorithms. - Received 3rd place at the Taiwan International Science Fair and and Microsoft Special Award.