Chris Wymant

The paper shows that slightly deleterious mutations, rather than a few large-effect mutations, explain slow viral load changes and heritability in HIV evolution.

This paper develops a multi-task learning model to predict HIV-1 resistance to multiple broadly neutralizing antibodies, showing it reduces subtype bias and identifies co-resistance patterns.

The paper evaluates two tools for HIV transmission direction prediction using short and long-read sequencing data from serodiscordant couples, showing long reads improve phylogenetic resolution and transmission prediction accuracy.

This paper introduces veSEQ-HIV, a cost-effective, high-throughput sequencing method for HIV that recovers complete genomes and quantifies viral load, enabling drug resistance monitoring and transmission analysis.

The paper develops a tool to accurately reconstruct whole HIV genomes from short-read data by using tailored references, showing improved accuracy over standard references.