CANSSI Ontario Welcomes Postdoctoral Fellow Ruixuan Zhao

CANSSI Ontario is pleased to announce Dr. Ruixuan Zhao, has been awarded the 2025 CANSSI Ontario Postdoctoral Fellowship in Sciences for her project, “Discovering Causal Patterns in Network-Linked Data through Bayesian Networks.”

Dr. Zhao will be based in the Department of Computer and Mathematical Sciences at the University of Toronto Scarborough, where she will work under the supervision of Drs. Linbo Wang (University of Toronto) and Guowen Huang (Western University). Her research will focus on developing innovative causal discovery methods to address the challenges posed by complex dependencies in network-linked data—such as those found in social networks—which often violate the standard assumption of data independence in causal inference.

Ruixuan earned her PhD from the Department of Data Science at the City University of Hong Kong through the Hong Kong PhD Fellowship Scheme, a highly competitive award for outstanding PhD students worldwide.

Ruixuan’s research interests include graphical models and causal discovery. During her PhD studies, she focused on establishing identifiability conditions and developing learning methods for causal graphical models, such as directed acyclic graphical models and chain graph models. The identifiability condition guaranteeing that the joint distribution of the variables corresponding to a set of nodes only infers a unique causal graph is fundamental to causal discovery. She conducted both theoretical and numerical analyses of learning methods for reconstructing the exact causal structures from observed data. Particularly, she proposed a set of novel identifiability conditions for the Gaussian linear chain graph model, which led to the first asymptotic consistency results for exact graph recovery in this setting. Her research has been published in top-tier journals in the field of statistics and presented at several academic conferences.