Shashata Sawmya

SmartEM accelerates electron microscopy image acquisition by using machine learning to intelligently allocate imaging time, achieving up to 7-fold speedup in connectomic samples.

MExConn is a multi-expert U-Net framework for segmenting multiple organelles in EM images, outperforming baselines and offering mechanistic interpretability.

The paper analyzes connectomics data from the mouse visual cortex to identify structural and functional differences between border regions, finding that the V1-RL border has unique connectivity and activity patterns.

PRIEST predicts SARS-CoV-2 mutations with immune escape potential using temporal evolutionary data and deep learning.

QT-GILD improves species tree estimation by imputing missing quartets from incomplete gene trees using deep learning, enhancing accuracy despite data gaps.

The paper develops a pipeline to classify SARS-CoV-2 genome sequences for virulence and predict mutations using deep learning.