My broad research interests are in deep learning and its applications. My time in both academia and industry has shaped my view and approach in research. The goal of my research is to enable transformative algorithms and practices towards trustworthy open-world machine learning, which can function safely and adaptively in the presence of evolving and unpredictable data stream. Our works explore, understand, and mitigate the many challenges where failure modes can occur in deploying machine learning models in the open world. Research topics that I am currently focusing on include:
Uncertainty estimation and out-of-distribution detection in deep learning;
Robustness to data irregularity and out-of-distribution generalization;
Applications of uncertainty-aware deep learning in healthcare and computer vision.