Assistant Professor of the Practice of Statistical Science
Currently, I am interested in statistical inference for high-dimensional longitudinal data. Data such as this appear in practice when a large number of variables are repeatedly measured for a relatively small number of experimental units. The number of repeated measurements can range from two to hundreds depending on the application. Under this framework, I am developing new procedures for change point detection and identification of covariance matrices.
The methodology is motivated by applications in genetics, neuroscience, and economics. Furthermore, the high-dimensional longitudinal data structures give rise to interesting and challenging computation problems that involve creating efficient algorithms and accurate approximation methods.
My research interests expand beyond theoretical statistics and statistical computing. I have a strong research interest in statistics pedagogy with regards to maximizing student engagement, developing computation driven courses, course design, and proxies for quantifying students’ statistical reasoning.