Research Interests
My research focuses on the Human-centered Artificial Intelligence (AI) and Human-AI Collaboration. In particular, my research aims to explicitly utilize the human intelligence from crowdsourcing systems (i.e., crowd intelligence) to effectively design, configure, and troubleshoot the AI models to improve the performance, fairness, explainability, and accountability of the AI models in real-world applications.
I am delighted to design principled solutions that integrate the state-of-the-art crowdsourcing techniques (e.g., Amazon MTurk), AI models (e.g., convolutional/recurrent/graph neural network, generative adversarial network, transfer/contrastive learning), and estimation theoretical approaches (e.g., maximum likelihood estimation (MLE), subjective logic) to jointly model the diversified yet complementary AI and crowd intelligence to address a rich set of complex real-world problems including public health misinformation detection, urban infrastructure monitoring, disaster damage assessment, traffic risk prediction, etc.