Archit Verma

PaSCient learns patient-level disease representations from scRNA-seq data, enabling disease classification and downstream analyses like dimensionality reduction and patient subgroup discovery.

This paper introduces a segmentation-free method to analyze live-cell imaging data of T cell-cancer cell interactions, revealing how T cell modifications affect aggregate formation dynamics.

This study uses explainable AI to analyze live-cell imaging data and identify behavioral changes in TCR T cells with specific genetic knockouts.

The paper develops tools for analyzing T cell behavior from live-cell imaging to improve T cell therapies by studying the effects of genetic modifications on T cell-cancer cell interactions.

The paper identifies differentially methylated regions and methylation QTLs associated with teen depression and early puberty using longitudinal DNA methylation data from the Fragile Families Child Wellbeing Study.

This paper introduces a robust nonlinear manifold model for scRNA-seq data to enable visualization and analysis of raw, unfiltered gene counts.

This paper introduces a Bayesian nonparametric semi-supervised model for integrating multiple single-cell experiments, showing its effectiveness on simulated and real data.