Center for Computing Research
Scalable Analysis and Vis, 01461
The scalable analysis and visualization department specializes in the research and development of capabilities to enable an understanding of large, complex data in direct support of the broader national-security missions of Sandia National Laboratories. We develop state-of-the-art techniques for visualization and predictive analytics through deep expertise and experience in statistical methods, machine learning, data mining, high-performance computing, computational geometry and graphics. Our department also maintains a robust external presence through strategic partnerships with universities, industry, and other national laboratories; and is committed to developing and contributing to open-source technologies.
|Ron A. Oldfield|
Manager, Scalable Analysis and Vis
Sandia National Laboratories
P.O. Box 5800, MS 1327
The Scalable Analysis and Visualization Department is involved with the projects listed below.
- Warren Davis Earns National Honors in Leadership and Technology
Warren Davis, received his award during the conference in Washington, D.C., Feb. 7-9, 2019. The annual meeting recognizes black scientists and engineers and is a program of the national Career Communications Group, which advocates for corporate diversity....
Warren Davis Earns National Honors in Leadership and Technology
Warren Davis, received his award during the conference in Washington, D.C., Feb. 7-9, 2019. The annual meeting recognizes black scientists and engineers and is a program of the national Career Communications Group, which advocates for corporate diversity.
This scientist wants to help you see like a computer.
If you saw all the aquariums that fill Davis’ home, you might think he was a pet lover. But you’d be wrong. Davis just has a passion for recreating things.
“I’ve got a sand bed that does denitrification in a certain layer,” mimicking a natural aquatic ecosystem, Davis said. “I’ve got animals that sift the sand bed so it doesn’t become anoxic. I have things that eat uneaten food particles that get trapped under the rocks.” It’s not a perfect model, he said, but it’s close.
Davis is also adept at recreating natural, mechanical processes to solve problems in engineering. In these cases, he takes natural phenomena — like air flowing over a surface or a person taking a step — and uses machine learning to explain them mathematically with an equation, also called a function. Machine learning can approximate complex processes much faster than they can be numerically solved, which saves companies time and resources if, for example, they want to predict how well a proposed aircraft design would hold up in flight. These savings compound when designers want to simulate multiple iterations.
“That’s what I do. I try to learn the functions that we care about,” Davis said.
He also has taken a leadership role helping Sandia and its business partners incorporate machine learning into their own research and development programs. On multiple occasions, he says, this addition has transformed the way they work, making their research more efficient and agile long after his project with them has ended.
The technique sometimes delivers unexpected solutions, too.
“When I’m able to take a data set and come up with something people haven’t seen before or some underlying function it is truly an amazing, almost magical feeling,” he said.
Davis’ work earned him a Research Leadership award.
Sandia news media contact: Troy Rummler, firstname.lastname@example.org
Paper was published in the Journal of Policy and Complex Systems. L. W. E. Epifanovskaya, K. Lakkaraju, J. Letchford, M. C. Stites, J. C. Reinhardt, and J. Whetzel. Modeling economic interdependence in deterrence using a serious game. Journal on Policy and Complex Systems, 4(SAND-2018-7419J), 2018. http://www.ipsonet.org/publications/open-access/policy-and-complex-systems/policy-and-complex-systems-volume-4-number-2-fall-2018
Contact: Oldfield, Ron A.