Current Research Interests
My research generally falls into one of the following categories (or a combination thereof):
Functional and longitudinal data analysis
Much of my methodological work focuses on functional and longitudinal data, particularly when the data involve multiple components or are subject to time warping. I developed latent deformation models and cross-component registration methods for multivariate functional data with my PhD advisor Hans-Georg Müller; these projects were motivated by the study of human growth curves. These methods address systematic phase variation — like differences in the timing of growth spurts across individuals and body measurements. This line of work has appeared in Biometrics and contributed to the fdapace R package.
Conservation, citizen science, and wildlife
I collaborate extensively with conservation scientists, particularly at The Nature Conservancy and UC Davis. Current projects include validating participatory science data by comparing bird seasonality patterns across eBird and iNaturalist, developing satellite-based monitoring workflows for field flooding, and using large language models to accelerate groundwater sustainability plan reviews. I also maintain the NorCal Bird Dashboard, an interactive tool for exploring bird observation data across Northern California.
Statistical modeling of healthcare data
On the biomedical side, I work with ophthalmologists at Stanford Medicine on predicting progressive vision loss in glaucoma patients using electronic health records and statistical and machine learning methods. I also collaborate with auditory researchers at UC San Diego, where our team developed ABRA, an open-source deep learning toolbox for automated auditory brainstem response analysis.
A full list of my publications can be found on my Google Scholar profile.