Neal G. Ravindra

According to our database, Neal G. Ravindra authored at least 7 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Cross-device Zero-shot Label Transfer via Alignment of Time Series Foundation Model Embeddings.

Neal G. Ravindra, Arijit Sehanobish

CoRR, September, 2025

2023

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.

Neal G. Ravindra, Camilo Espinosa, Eloïse Berson, Thanaphong Phongpreecha, Peinan Zhao, Martin Becker, Alan L. Chang, Sayane Shome, Ivana Maric, Davide De Francesco, Samson Mataraso, Geetha Saarunya, Melan Thuraiappah, Lei Xue, Brice Gaudilliere, Martin S. Angst, Gary M. Shaw, Erik D. Herzog, David K. Stevenson, Sarah K. England, Nima Aghaeepour

npj Digit. Medicine, 2023

Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Martin Becker, Huda Nassar, Camilo Espinosa, Ina A. Stelzer, Dorien Feyaerts, Eloïse Berson, Neda Hajiakhoond Bidoki, Alan L. Chang, Geetha Saarunya, Anthony Culos, Davide De Francesco, Ramin Fallahzadeh, Qun Liu, Yeasul Kim, Ivana Maric, Samson Mataraso, Seyedeh Neelufar Payrovnaziri, Thanaphong Phongpreecha, Neal G. Ravindra, Natalie Stanley, Sayane Shome, Yuqi Tan, Melan Thuraiappah, Maria Xenochristou, Lei Xue, Gary M. Shaw, David K. Stevenson, Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour

Nat. Comput. Sci., 2023

2021

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

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Permutation invariant networks to learn Wasserstein metrics.

Arijit Sehanobish, Neal G. Ravindra, David van Dijk

CoRR, 2020

Self-supervised edge features for improved Graph Neural Network training.

Arijit Sehanobish, Neal G. Ravindra, David van Dijk

CoRR, 2020

Disease state prediction from single-cell data using graph attention networks.

Neal G. Ravindra, Arijit Sehanobish, Jenna L. Pappalardo, David A. Hafler, David van Dijk

Proceedings of the ACM CHIL '20: ACM Conference on Health, 2020