My research focuses on developing statistical and machine learning methods for large-scale and complex data. Driven by key areas including causal discovery from observational studies, neuroimaging analysis, and multi-view data analysis for personalized healthcare, I work on real-world data that features high-dimensionality, massive volume, complex correlation structures, unmeasured confounders, and incompleteness. Recently, I’ve also become interested in the application of deep learning methods to brain imaging for disease detection and monitoring.
WEBSITE(S)| Website | Ken Kennedy Institute
Research Areas
- Statistical and computational challenges of complex data - Causal inference - High dimensional statistics, post-selection inference, latent variable models - Applications in neuroimaging, genetics, health sciences, and precision healthcare - Application of deep learning methods to medical imaging - Alzheimer's disease
Education
Ph.D. in Statistics, University of Toronto 2018 - 2023
M.S. in Statistics, University of California, Berkeley 2017 - 2018
B.Sc. in Mathematical Application in Economics and Finance, University of Toronto 2014 - 2017
Honors & Awards
OntarioTrillium Scholarship 2018 - 2022
Statistical Society of Canada (SSC) Annual Meeting Student Travel Grant 2022
School of Graduate Studies (SGS) Conference Grant 2020
Department Citation Award in the Statistics Master's Program, UC Berkeley 2018
ASA Nonparametric Statistics Section Student Paper Awards Finalist 2018
Dean's list Scholar, University of Toronto 2015 - 2017
In-course scholarship, New College, University of Toronto 2017