Richard Platania

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We present the latest development and experimental simulation studies of Statistical Temperature Molecular Dynamics (STMD) and its parallel tempering version, Replica Exchange Statistical Temperature Molecular Dynamics (RESTMD). Our main contributions are i) introduction of newly implemented STMD in LAMMPS, ii) use of large scale distributed cyber(More)
In recent years, big data analysis has been widely applied to many research fields including biology, physics, transportation, and material science. Even though the demands for big data migration and big data analysis are dramatically increasing in campus IT infrastructures, there are several technical challenges that need to be addressed. First of all,(More)
We present Hadoop-based replica exchange (HaRE), a Hadoop-based implementation of the replica exchange scheme developed primarily for replica exchange statistical temperature molecular dynamics, an example of a large-scale, advanced sampling molecular dynamics simulation. By using Hadoop as a framework and the MapReduce model for driving replica exchange,(More)
High-performance analysis of big data demands more computing resources, forcing similar growth in computation cost. So, the challenge to the HPC system designers is providing not only high performance but also high performance at lower cost. For high performance yet cost effective cyberinfrastructure, we propose a new system model augmenting Amdahl's second(More)
ESR spectroscopy is one of the physicochemical techniques used to characterize archaeological white marbles and obtain information about their quarries of provenance. This is done by measuring selected spectral features of the Mn(2+) impurity ubiquitously present in marbles and developing a statistical classification rule from the variable vectors measured(More)
Genome sequencing technology has witnessed tremendous progress in terms of throughput as well as cost per base pair, resulting in an explosion in the size of data. Consequently, typical sequence assembly tools demand a lot of processing power and memory and are unable to assemble big datasets unless run on hundreds of nodes. In this paper, we present a(More)
Recent advances in deep learning have enabled researchers across many disciplines to uncover new insights about large datasets. Deep neural networks have shown applicability to image, time-series, textual, and other data, all of which are available in a plethora of research fields. However, their computational complexity and large memory overhead requires(More)
The size of high throughput DNA sequencing data has already reached the terabyte scale. To manage this huge volume of data, many downstream sequencing applications started using locality-based computing over different cloud infrastructures to take advantage of elastic (pay as you go) resources at a lower cost. However, the locality-based programming model(More)
Detection of suspicious regions in mammogram images and the subsequent diagnosis of these regions remains a challenging problem in the medical world. There still exists an alarming rate of misdiagnosis of breast cancer. This results in both over treatment through incorrect positive diagnosis of cancer and under treatment through overlooked cancerous masses.(More)
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