1 citations · 1 across the 7 of their papers we have counts for
7 papers
Fast distance computation of multivariate distributions via nonparanormal transport
Edward Shao, Junyoung Park, Naresh Punjabi +2
With the increasing availability of data objects in the form of probability distributions, there is a growing need for statistical methods tailored to distributional data. Distance…
Smooth tensor decomposition with application to ambulatory blood pressure monitoring data
Leyuan Qian, R. Nisha Aurora, Naresh M. Punjabi +1
Ambulatory blood pressure monitoring (ABPM) enables continuous measurement of blood pressure and heart rate over 24 hours and is increasingly used in clinical studies. However, ABP…
GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks
Renat Sergazinov, Elizabeth Chun, Valeriya Rogovchenko +3
The rising rates of diabetes necessitate innovative methods for its management. Continuous glucose monitors (CGM) are small medical devices that measure blood glucose levels at reg…
A spectral method for multi-view subspace learning using the product of projections
Renat Sergazinov, Armeen Taeb, Irina Gaynanova
Multi-view data provides complementary information on the same set of observations, with multi-omics and multimodal sensor data being common examples. Analyzing such data typically…
Fast variable selection for distributional regression with application to continuous glucose monitoring data
Alexander Coulter, Rashmi N. Aurora, Naresh M. Punjabi +1
With the growing prevalence of diabetes and the associated public health burden, it is crucial to identify modifiable factors that could improve patients' glycemic control. In this…
Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification
Renat Sergazinov, Mohammadreza Armandpour, Irina Gaynanova
Deep learning models achieve state-of-the art results in predicting blood glucose trajectories, with a wide range of architectures being proposed. However, the adaptation of such m…