Sudip Sharma

This paper introduces a scalable phylogenomic framework that reduces computational costs while maintaining accuracy in evolutionary analyses.

This study uses evolutionary sparse learning to identify shared genetic bases for convergent traits in different species, finding common genes involved in C4 photosynthesis and echolocation.

MEGA12 adds filtered substitution-model selection, matching full-search choices in all 240 simulated nucleotide alignments, and adaptive bootstrapping with an 81% average speed-up, accelerating maximum-likelihood phylogenetics.

The paper introduces metrics to identify fragile clades and causal sequences in phylogenomics using evolutionary sparse learning, validated across fungi, plant, and animal datasets.