Dhananjay Bhaskar

This paper curates a large corpus of mathematical oncology literature using AI, revealing its evolution from cancer biology to therapeutic modeling over 140 years.

The paper maps physical cell-cell contacts in mouse visual cortex, revealing a dense contactome that exceeds synaptic connectivity and highlights the role of astrocytes in shaping the neural microenvironment.

The paper develops a deep learning framework to classify first-episode psychosis using multimodal data from neuroimaging, EEG, and facial features.

The paper introduces NeuroSC, a platform that integrates graph analytics with connectome data to explore neurodevelopmental changes in C. elegans.

This paper quantifies spatial patterns of reactive glial cells after CNS injury using point pattern analysis and topological data analysis, revealing divergence in astrocyte and microglia distribution.

The paper introduces a probabilistic α-shape sampler to generate realistic 2D and 3D biological shapes from real data, demonstrating its use on neutrophil images and primate mandible CT scans.

Neurospectrum extracts interpretable spatiotemporal features from neural activity using a geometric and topological deep learning framework, outperforming traditional methods in simulations and fMRI data.

The paper presents an unsupervised machine learning method for cell shape classification from microscopy images using feature extraction and density-based clustering.