Sumita Garai

This paper introduces UNITO, a deep learning framework for automated cytometric gating that matches human performance and provides interpretable results without requiring manual input.

This study introduces H-VIP to quantify vertex contributions to topological cycles in brain networks and finds a positive correlation with cognitive measures in both structural and functional connectomes.

This paper introduces QOT, a method for efficiently computing sample-level distance matrices from single-cell omics data, showing improved accuracy and robustness over existing OT-based approaches.

The paper introduces a homological formalism to analyze higher-order properties of human brain functional sub-circuits and identifies task-specific homological differences in brain connectivity.