1 citations · 1 across the 3 of their papers we have counts for
5 papers
deFOREST: Fusing Optical and Radar satellite data for Enhanced Sensing of Tree-loss
Julio Enrique Castrillon-Candas, Hanfeng Gu, Caleb Meredith +4
In this paper we develop a deforestation detection pipeline that incorporates optical and Synthetic Aperture Radar (SAR) data. A crucial component of the pipeline is the constructi…
Stochastic tensor space feature theory with applications to robust machine learning
Julio Enrique Castrillon-Candas, Kaili Shi, Dingning Liu +4
In this paper we develop a Multilevel Orthogonal Subspace (MOS) Karhunen-Loeve feature theory based on stochastic tensor spaces, for the construction of robust machine learning fea…
Distribution-Free Stochastic Analysis and Robust Multilevel Vector Field Anomaly Detection
Julio E Castrillon-Candas, Michael Rosenbaum, Mark Kon
Massive vector field datasets are common in multi-spectral optical and radar sensors, among many other emerging areas of application. We develop a novel stochastic functional (data…
Uncertainty quantification of receptor ligand binding sites prediction
Nanjie Chen, Dongliang Yu, Dmitri Beglov +2
Recent advancements in protein docking site prediction have highlighted the limitations of traditional rigid docking algorithms, like PIPER, which often neglect critical stochastic…
Spatial best linear unbiased prediction: A computational mathematics approach for high dimensional massive datasets
Julio E. Castrillon-Candas
With the advent of massive data sets much of the computational science and engineering community has moved toward data-intensive approaches in regression and classification. Howeve…