Changjian is a Postdoc Fellow at Vector Institute. He got his PhD at Laval University in 2022. His primary research interest is in machine learning under distribution and its applications in health. Particularly he is interested in the issue of reliability (e.g., robustness, fairness, uncertainty, and transparency) and data-efficient machine learning (e.g., transfer, meta and active learning). His long research objectives are to establish transparent AI models. His research has appeared in major AI venues such as NeurIPS, ICML, ICLR, JMLR, IJCAI, AAAI, AISTATS.