The latent deformation model assembles multivariate curves from a shared latent process, warping each component onto its own internal clock before the curves are observed. From C. and Müller, Biometrics (2023).
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, motivated by the study of human growth curves. These methods separate systematic phase variation, such as differences in the timing of growth spurts across individuals and across body measurements, from differences in amplitude. During the COVID-19 pandemic, I applied functional data methods to characterize how case trajectories evolved across countries and to forecast case growth rates in the United States.
Key works Latent Deformation Models (Biometrics, 2023) · Cross-component Registration (Biometrics, 2021) · Time Dynamics of COVID-19 (Scientific Reports, 2020) · Learning Delay Dynamics for COVID-19 Growth (J. Math. Anal. Appl., 2022) · fdapace R package
Seasonality of four bird species reconstructed separately from eBird and iNaturalist records. The two platforms largely agree on the timing of each species. From C. et al., Citizen Science: Theory and Practice (2025).
Conservation, citizen science, and biology
I work with conservation scientists 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. My collaborations in biology include neonatal survival in non-domestic Caprinae with veterinarians at the San Diego Zoo, the comparative biology of a groundwater isopod, and the determinants of success and survival for Mount Everest mountaineers.
Key works eBird vs. iNaturalist Seasonality (Citizen Science: Theory and Practice, 2025) · LLMs for Conservation Efficiency · Neonatal Survivability in Non-Domestic Caprinae (Journal of Zoo and Wildlife Medicine, 2022) · Embryology and Transcriptomics of a Groundwater Isopod (Evolution & Development, 2025) · Mountaineers on Mount Everest (PLoS One, 2020) · NorCal Bird Dashboard · Satellite Monitoring of Field Flooding (The Nature Conservancy, 2023)
ABR waveforms analyzed across sound levels, with automated peak detection and threshold estimation. The hearing threshold is the lowest level at which a response is still detectable. From Erra et al., Scientific Reports (2026).
Statistical modeling of healthcare data
On the biomedical side, I work with ophthalmologists at Stanford Medicine on predicting progressive vision loss in glaucoma patients from electronic health records, using statistical and machine learning methods. I also collaborate with auditory researchers in the Manor Lab at UC San Diego, where our team developed ABRA, an open-source deep learning toolbox for automated auditory brainstem response analysis. At UCSF, I work with Hui Lin and Jean-Philippe Coppé on kinase signaling in cancer, using graph neural networks and protein language model embeddings to identify the kinases that drive resistance to treatment. Separately, I collaborate with the Capra Lab in epidemiology and biostatistics. I also work with the Ketamine Research Foundation, characterizing longitudinal change in depression and anxiety among adolescents and young adults receiving ketamine-assisted psychotherapy.
Key works ABRA Deep Learning Toolbox (Scientific Reports, 2026) · FPCA for Glaucoma Progression (Frontiers in Ophthalmology, 2025) · Knowledge-Based DVH Prediction (J. Appl. Clin. Med. Phys., 2021)
A full list of my publications can be found on my Google Scholar profile.