Shourya Verma

SPEAR predicts cellular responses to perturbations by adaptively repositioning genes in a co-expression network using spectral encoding and generative modeling.

The paper introduces a hybrid framework combining mechanistic models and machine learning to solve high-dimensional population balance equations for cell-state dynamics.

Pert2Mol generates molecular structures from paired transcriptomic and morphological data using a multi-modal framework, achieving better performance than single-modality or diffusion-based methods.

PertFlow jointly predicts gene expression and cellular morphology from drug perturbations, achieving high correlation and expert-rated image similarity.

AnnotateAnyCell reduces annotation time for histopathological images by 25% using a semi-supervised framework with active contrastive learning and human-in-the-loop feedback.