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mathematical sciences, Computational Sciences & Mathematics

PNNL’s Computing Research portfolio—spanning from basic to applied—includes data and computational engineering, high-performance computing, applied mathematics, semantic and human language technologies, machine learning, data and computing architectures, systems integration, and software and application development. At PNNL, scientists, engineers, programmers, and researchers work together to apply advanced theories, methods, algorithms, models, evaluation tools and testbeds, and computational-based solutions to address complex scientific challenges affecting energy, biological sciences, the environment, and national security.


Deep Learning Could Help Detect Nuclear Events Worldwide

Scientists Emily Mace and Jesse Ward, both from the National Security Directorate, teamed to explore the promise of deep learning to interpret signals from radioactive decay events, which could indicate underground nuclear testing. Their results showed the deep learning techniques separated signal events from instrument “noise” with nearly 100 percent accuracy and performed 25 times better than current computational methods. Mace presented their work at the 11th Methods and Applications of Radioanalytical Chemistry conference in Hawaii.


PNNL Thought Leaders Quoted in PC Magazine

Jonathan Cree and William Moeglein, both with the National Security Directorate, were interviewed for an article, “A Guide to Using BI Apps With Edge Computing,” recently featured in PC Magazine. The duo were asked for their take on the interconnections between deep neural networks, the cloud, and edge computing.


Tumeo Named Subject Area Editor by Parallel Computing

Antonino Tumeo, with PNNL’s High Performance Computing group, was named a Subject Area Editor for Parallel Computing, an international journal by Elsevier that centers on the practical use of parallel computer systems. In his role, Tumeo joins other international computer science experts who serve on the journals’s Editorial Board.


Rallo Joins Advanced Theory and Simulations Editorial Advisory Board

Recently, Robert Rallo, Data Sciences Group Lead with PNNL’s AMCD Division, joined the Editorial Advisory Board for Advanced Theory and Simulations. The interdisciplinary journal focuses on publishing research about theory, simulation, and modeling in materials and natural science areas. As a board member, Rallo will be among an international group of leading scientists who advise the journal’s editorial team.


#Flu. Research highlighted in Scientific American

Scientific American recently featured work led by Svitlana Volkova, a scientist with NSD’s Computing and Analytics Division, on using social media to predict influenza outbreaks. The article,“#Flu: Mining Social Media to Predict Outbreaks,” compared work from Volkova’s team, which used deep learning to correlate certain words in Twitter feeds with flu outbreak patterns, with previous research. The deep learning algorithm proved to be a good early predictor of outbreaks.

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Computing Research

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