Daniel J. McDonald

Prof. Daniel J. McDonald is a Professor in the Department of Statistics at the University of British Columbia (UBC), Faculty of Science. His research focuses on statistical methodology, epidemiology modeling, machine learning, and time-series analysis. He has contributed to the development of R packages for statistical computing and has advised students like Wakeel Adekunle Kasali and Jiaping(Olivia) Liu. His work spans applications in public health, neuroscience, and econometrics.

Education details are not explicitly provided, but his career trajectory suggests advanced training in statistics. Research interests emphasize statistical methods for complex data, including fMRI analysis, epidemic forecasting, and structural brain connectomics. He has published extensively on topics such as time-varying reproduction number estimation, sparse group lasso algorithms, and trend filtering techniques.

His articles reflect a blend of methodological innovation and applied problem-solving in public health crises (e.g., COVID-19), neuroscience, and macroeconomic modeling. Collaborations likely involve interdisciplinary teams given the scope of his work in health informatics and computational neuroscience.