Category Archives: ARC News

ARC and VT Libraries Sponsor Big Data Science Workshop

Featuring sessions on big data workflows, data visualization, data publishing, and reproducible research practices, the 2017 Big Data Science Workshop will also incorporate a brainstorming/strategy session aimed at improving research workflows, a networking breakfast, and lightning talks.

ARC's Nicholas Polys and Brian Marshall each presented. The event flyer is here:

Big Data Science Workshop

New ARC Cluster: DragonsTooth

ARC is happy to announce the release of a new cluster, named DragonsTooth, available at DragonsTooth is made up of 48 nodes, each equipped with:

  • 2 x Intel Xeon E5-2680v3 (Haswell) 2.5 GHz 12-core CPU (same CPU as NewRiver)
  • 256 GB 2133 MHz DDR4 memory for large-memory problems
  • 4 x 480 GB SSD Hard Drives for fast local I/O ($TMPDIR)
  • 806 GFlops/s theoretical double-precision peak

Continue reading New ARC Cluster: DragonsTooth

ARC presents at XSEDE16!

Dr. Srijith Rajamohan presented an Introduction to Python Pandas for Data Analytics tutorial. Pandas is a high-level open-source library that provides data analysis tools for Python. The audience was also introduced to relevant packages such as Numpy for fast numeric computation and Matplotlib/Bokeh for visualization to supplement the data analysis process. The slides for this tutorial can be found here.

Visualization GRA and Doctoral Candidate Ayat Mohammed presented a visualization showcase titled 'Insights into Alzheimer's Disease: Molecular Dynamics (MD) Simulations of Peptide-Membrane Interactions' at XSEDE16, Miami. Also from ARC, Alana Romanella chaired the session on Workforce Development and Diversity.

ARC Interdisciplinary collaboration for analysis of food marketing/branding

Dr. Srijith Rajamohan and Dr. Nicholas Polys in ARC and Assistant Professor, Vivica Kraak in the Department of Human Nutrition, Foods, and Exercise are collaborating on a research project to map the world of celebrity endorsement of food and beverage brands, products and groups in the United States. HNFE doctoral student, Mi Zhou, is part of the research team with ARC MS student, Faiz Abidi, to build, analyze and visually display in 2D and 3D a database of more than 550 unique celebrities used to market food, beverage and restaurant products to children, teens and adults. The ARC team had helped build an open-source analytics and visualization engine to help address these needs.

The results of this project will be used to inform the policies and actions of diverse stakeholders including industry, government and public health groups to use celebrity endorsement, along with other integrated marketing communications, to promote healthy nutrient-profile products and behaviors that support healthy food environments for American children, adolescents and their parents. Prof. Kraak and her work was recently featured on the VT news which can be found here:

HPC Day Poster Session Winners

Thank you to all the students who participated in the HPC Day Poster Session. Our 2016 top three finishers include:

First Place: Bobby Hollingsworth

Computational Insights into Binding of a Repeat Unit of an Antiviral Copolymer to Glycoprotein 120 in Four Strains of HIV

Second Place: Mariam Umar, Sand L. Correa, Kirk W. Cameron

Energy and Performance Modeling and Estimation for ASPEN Domain Specific Language 

Third Place: Megan Richardson

Ayat Mohammed, ARC Viz GRA, presented at the Doctoral Consortium and Poster Session at 2016 IEEE VR Conference in Greenville, SC


Scientific Visualization has proven to be an effective means for analyzing multivariate multidimensional data (MVMD). A variety of techniques combining statistical and visual analytic tools have been developed in the recent years to analyze MVMD. Visual differencing, or visual discrimination, is the ability to compare an attribute value between two or more objects in a visualization. In this research, we are examining humans’ predictable bias in interpreting visual-spatial information for comparison and inference. We will develop and evaluate new techniques of data representation that support multivariate multidimensional visual differencing. We will also address the trade-off between proximity and occlusion and evaluate users’ ability to explore MVMD across the immersive spectrum.