Ruilin Li

The paper develops a method to identify genetic predictors of cardiometabolic disease progression using time-to-event data from the UK Biobank, finding ten polygenic hazard scores that significantly predict disease progression.

The paper develops sparse polygenic risk scores for 813 traits using UK Biobank data, showing significant predictive performance and a correlation between variant count and performance.

The paper develops efficient solvers for large-scale regularized regressions on genetic data, reducing memory usage and improving computational speed.

The paper introduces a group-regularized Cox regression method for survival analysis of rare events using common responses, showing improved prediction performance on UK Biobank data.

The paper develops a scalable sparse reduced-rank regression method for high-dimensional multitask learning, applied to UK Biobank data to generate polygenic risk scores.