I received my Ph.D. degree at the Hong Kong University of Science and Technology, where I was a member of HKUST VisLab, advised by Prof. Huamin Qu. My research focuses on Human-Centered AI with data visualization approaches. My research goal is to support people in understanding and interacting with machine learning models by developing scalable visual interfaces and explainable machine learning techniques.
Recently, I have focused more on 1) facilitating interpretable and trustworthy AI-informed decision-making in high-stakes scenarios and 2) supporting users in steering machine learning models to align with their knowledge.
Download my resumé.
Polyphony facilitates interactions between biologists and single-cell omics data annotation ML models with the integration of visualization and anchor-based interactive transfer learning.
VBridge incorporates ML explanations into clinicians' decision-making workflow by connecting the dots between ML features, data and explanations in healthcare models.
DECE supports exploratory visual analysis on ML models with counterfactual explanations. The system helps non-expert users propose, verify, and refine their hypotheses on ML predictions.