6 citations · 9 across the 7 of their papers we have counts for
7 papers
Learning transferable human physiology from two million hours of sleep with SleepFM-2
Rahul Thapa, Christopher Sun, William Theodor Lehn-Schioler +14
Sleep provides a nightly window into health by capturing coordinated activity across the brain, heart, muscles and respiratory system. We introduce SleepFM-2, a sleep foundation mo…
Deep Learning Approaches for Blood Disease Diagnosis Across Hematopoietic Lineages
Gabriel Bo, Justin Gu, Christopher Sun
We present a foundation modeling framework that leverages deep learning to uncover latent genetic signatures across the hematopoietic hierarchy. Our approach trains a fully connect…
Improving Machine Learning Based Sepsis Diagnosis Using Heart Rate Variability
Sai Balaji, Christopher Sun, Anaiy Somalwar
The early and accurate diagnosis of sepsis is critical for enhancing patient outcomes. This study aims to use heart rate variability (HRV) features to develop an effective predicti…
Predicting Future Mosquito Larval Habitats Using Time Series Climate Forecasting and Deep Learning
Christopher Sun, Jay Nimbalkar, Ravnoor Bedi
Mosquito habitat ranges are projected to expand due to climate change. This investigation aims to identify future mosquito habitats by analyzing preferred ecological conditions of…
A Deep Learning Ensemble Framework for Off-Nadir Geocentric Pose Prediction
Christopher Sun, Jai Sharma, Milind Maiti
Computational methods to accelerate natural disaster response include change detection, map alignment, and vision-aided navigation. Current software functions optimally only on nea…
Analyzing Multispectral Satellite Imagery of South American Wildfires Using Deep Learning
Christopher Sun
Since frequent severe droughts are lengthening the dry season in the Amazon Rainforest, it is important to detect wildfires promptly and forecast possible spread for effective supp…
Investigating the Relationship Between Dropout Regularization and Model Complexity in Neural Networks
Christopher Sun, Jai Sharma, Milind Maiti
Dropout Regularization, serving to reduce variance, is nearly ubiquitous in Deep Learning models. We explore the relationship between the dropout rate and model complexity by train…