Computational Data Analytics

Highlights

Designing a neuromorphic computing system involves selection of several hyperparameters that not only affect the accuracy of the framework, but also the energy…

Neuromorphic architectures are designed and developed with the goal of having small, low power chips that can perform control and machine learning tasks. However, the…

In this work, we discuss an approach for that utilizes high-performance computing (HPC) to evolve the hyperparameters and topology of convolutional neural networks in…

We propose a new algorithm to improve the strong scalability of right-looking sparse LU factorization on distributed memory systems. Our 3D algorithm for sparse LU uses a three-dimensional MPI…

Non-negative matrix factorization(Nmf) is an important tool in high-performance large scale data analytics with applications ranging from community detection, recommender system, feature detection…

We present a novel distributed memory algorithm to improve the strong scalability of the solution of a sparse triangular system. This operation appears in the solve phase of direct methods for…

Corrosion studies in molten fluoride and chloride salts were surveyed, and key data were aggregated into a single dataset. Studies were graphed by salt…

A breakthrough in alloy design often requires comprehensive understanding in complex multi-component/multi-phase systems to generate novel material hypotheses. We…

The last few decades have witnessed a significant increase in the sophistication of scientific instruments. This sophistication has led to an…

EDEN is an interactive visual analytics system for multivariate data analysis that extends the parallel coordinates information visualization technique providing new statistical analysis…

Our deep learning framework is called Multinode Evolutionary Neural Networks for Deep Learning (MENNDL).  MENNDL relies on two optimization methods, genetic algorithms…