Erjia Cui

This paper introduces a fast generalized LFPCA method for analyzing longitudinal functional data, showing it is faster and scalable while applied to neuronal firing rate data under DBS.

The paper introduces pt-TWAS, a framework that identifies disease genes along cell developmental paths by modeling gene expression as a continuous function of pseudotime, improving resolution over discrete cell-type analyses.

INTACT harmonizes time-varying physical activity data from multiple sources by removing device-specific effects while preserving biological variation, improving cross-study comparisons.

The paper introduces concurrent functional linear mixed models (cFLMMs) to analyze neural signals and behavior in real-time, showing they reveal associations missed by traditional methods.

This paper introduces a statistical framework for analyzing trial-level temporal dynamics in fiber photometry experiments, revealing previously obscured effects in dopamine responses to reward.