Xiang is an Assistant Professor in the Department of Computer Science at the University of North Carolina (UNC) at Charlotte. Before joining UNC Charlotte, he was a postdoctoral fellow at Harvard University from March 2020 to July 2022. Xiang received his Ph.D. degree (in 2020) in Computer Science from the University of New South Wales (UNSW).
His research interests lie in data mining and machine learning with applications in pervasive healthcare, medical time series, and Brain-Computer Interfaces (BCIs). Xiang's work has published in prestigious conferences (such as ICLR, NeurIPS, and KDD) and journals (like Nature Computational Science).
Keywords: graph neural networks, irregular time series, sparse time series, incomplete time series, message passing, ICU, sepsis detection
Keywords: self-supervise, contrastive learning, representation learning, time series, frequency domain, pre-training, transfer learning
Keywords: pandemic, drug side effects, health inequity, demographics, FDA, FAERS
Keywords: EEG, brain-computer interface, non-invasive, person identification, attention mechanism