7 citations · 8 across the 5 of their papers we have counts for
5 papers
Cross-device Zero-shot Label Transfer via Alignment of Time Series Foundation Model Embeddings
Neal G. Ravindra, Arijit Sehanobish
High-quality, medically validated labels exist for clinical actigraphy data but not for ubiquitous consumer wearables like the Apple Watch. Manually labeling wearables data is expe…
Permutation invariant networks to learn Wasserstein metrics
Arijit Sehanobish, Neal Ravindra, David van Dijk
Understanding the space of probability measures on a metric space equipped with a Wasserstein distance is one of the fundamental questions in mathematical analysis. The Wasserstein…
Self-supervised edge features for improved Graph Neural Network training
Arijit Sehanobish, Neal G. Ravindra, David van Dijk
Graph Neural Networks (GNN) have been extensively used to extract meaningful representations from graph structured data and to perform predictive tasks such as node classification…
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks
Arijit Sehanobish, Neal G. Ravindra, David van Dijk
A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic. We sought…
Disease State Prediction From Single-Cell Data Using Graph Attention Networks
Neal G. Ravindra, Arijit Sehanobish, Jenna L. Pappalardo +2
Single-cell RNA sequencing (scRNA-seq) has revolutionized biological discovery, providing an unbiased picture of cellular heterogeneity in tissues. While scRNA-seq has been used ex…