Assistant professor, statistics
Fang’s research interests are in statistics, machine learning, and their applications to studying complex (high dimensional, heteroscedastic, noisy, of spatial-temporal structure) data. The tools Fang exploits and develops are commonly categorized in: (i) high dimensional statistics; (ii) robust statistics; (iii) nonparametric and semiparametric regression models; (iv) time series analysis (with related probabilistic models and techniques).
Fang also works on imaging and sequencing data analysis. Research specializations include statistical methodology, statistical and probability theory, biostatistics, neuroscience, and genomics.