Emmanuel J. Candès

This paper introduces a framework for identifying disease-associated features tailored to individual profiles using localized test statistics, applied to single-cell RNA sequencing data for Alzheimer's disease.

This paper introduces TxConformal, a framework that controls false discoveries in AI-driven therapeutic discovery by adjusting for distribution shifts and providing confidence measures for AI scores.

This paper introduces a method to identify causal variants from genome-wide summary statistics, outperforming existing methods and discovering additional loci for complex traits.

This paper introduces GhostKnockoff, a method to improve causal variant identification in GWAS by increasing statistical power and reducing proxy variants.

This paper introduces a statistical framework for GWAS that controls false discovery rate while accounting for population structure and relatedness, validated through simulations and UK Biobank data.

KnockoffZoom identifies causal variants for complex traits at multiple resolutions using artificial genotypes and linkage disequilibrium models, outperforming mixed effects models in the UK Biobank.