Marissa A. Weis

PriMAT tracks and identifies primates in the wild using bounding boxes, achieving 84% accuracy in lemur identification with minimal labeled data.

The paper uses graph-based machine learning to analyze dendritic morphology of excitatory neurons in mouse visual cortex, finding a continuous morphological landscape rather than discrete types.

This paper trains a foundation model on neural activity data to predict responses to new stimuli and anatomical features, showing strong generalization across mice and tasks.

This paper introduces sparse reduced-rank regression for visualizing relationships between paired multivariate datasets, showing improved performance over existing methods in Patch-seq data.