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people

Sharmishtaa Seshamani

Manager, Computer Vision

teams /
Allenite

At the Allen Institute for Brain Science, Sharmishtaa Seshamani is working in the Human Cell Types group. Her current research focuses on building a data processing pipeline to facilitate the imaging of neurons in the brains of mouse and human brain samples in order to reveal important details about the morphology and connectivity of neural circuits. She received her Ph.D. in Computer Science from Johns Hopkins University where she focused on developing meta registration and mosaicking techniques for endoscopic imaging. Prior to joining the Allen Institute for Brain Science, she was a Senior Fellow at the Biomedical Image Computing Group in the University of Washington. There, she was working on a pipeline for fetal fMRI analysis which included algorithms for MRI artifact correction (bias and spin history), motion estimation and reconstruction.

research focus

As a member of the synapse biology team, one of my primary research goals is to develop a framework for automated large scale processing of array tomography data. In particular, I am interested in developing tools for linear and elastic registration and 3D reconstruction optimized for large datasets. I am also interested in applications of machine learning techniques for automatic parameter selection and connectivity data analysis.

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featured publications

publication / 2025
The synaptic architecture of layer 5 thick tufted excitatory neurons in mouse visual cortex
Nature Neuroscience
publication / 2025
Inhibitory specificity from a connectomic census of mouse visual cortex
Nature
publication / 2025
Perisomatic ultrastructure efficiently classifies cells in mouse cortex
Nature
publication / 2024
Expansion-assisted selective plane illumination microscopy for nanoscale imaging of centimeter-scale tissues
eLife
publication / 2024
Ultrastructural differences impact cilia shape and external exposure across cell classes in the visual cortex
Current biology: CB
publication / 2022
Oligodendrocyte precursor cells ingest axons in the mouse neocortex
Proceedings of the National Academy of Sciences of the United States of America
publication / 2019
Single-cell transcriptomic evidence for dense intracortical neuropeptide networks
eLife
publication / 2018
Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy
Nature Methods
preprint / 2017
Three dimensional cross-modal image inference: label-free methods for subcellular structure prediction
publication
Connectomics of predicted Sst transcriptomic types in mouse visual cortex
Nature
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