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Posts Tagged ‘computational science’

Update: SIAM Committee on Science Policy

SIAM’s Committee on Science Policy (CSP) held its regular Spring meeting on March 28 and 29 to meet with key decision-makers at federal agencies and in Congress to better understand the environment for research and to influence congressional legislation and federal programs related to applied mathematics and computational science.  The committee met with representatives from various units within the National Science Foundation (NSF), the Department of Energy (DOE), the Department of Defense (DOD), and the National Institute of Standards and Technology (NIST) to learn more about the fiscal year (FY) 2017 budget request, discuss new research initiatives, and provide input on issues of concern to the SIAM community.   Read the rest of this entry »

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Visual analysis of big data

We live in an era in which the creation of new data is growing exponentially such that every two days we create as much new data as we did from the beginning of mankind until the year 2003. One of the greatest scientific challenges of the 21st century is to effectively understand and make use of the vast amount of information being produced. Visual data analysis will be among our most important tools to understand such large and often complex data. In this talk at the 2014 SIAM Annual Meeting, Christopher Johnson of the University of Utah presented state-of-the-art visualization techniques, including ways to visually characterize associated error and uncertainty, applied to Big Data problems in science, engineering, and medicine.

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Computational Biology in the 21st Century

The last two decades have seen an exponential increase in genomic and biomedical data, which will soon outstrip advances in computing power to perform current methods of analysis. Extracting new science from these massive datasets will require not only faster computers; it will require smarter algorithms. At the 2014 SIAM Annual Meeting, Bonnie Berger of the Massachusetts Institute of Technology (MIT), showed how ideas from cutting-edge algorithms, including spectral graph theory and modern data structures, can be used to attack challenges in sequencing, medical genomics and biological networks.

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Careers in computational science and engineering [Video]

At the SIAM Conference on Computational Science and Engineering held in Boston in February, mathematicians from academia, industry and government labs discussed and answered questions about the various career options students and those in their early careers could pursue in the fields of computational science and engineering. Watch video highlights!

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Why present a poster at a meeting?

Watch this video on tips, advice and benefits of presenting research posters at conferences. Attendees at ICIAM 2011 spoke with us about the benefits of presenting posters at professional and academic conferences, and about how poster sessions enable one to interact and network with other attendees, providing a more casual setting to learn about research in varied areas in the field:

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Computer Predictions with Quantified Uncertainty, Part I

Recently, a fresh look at how computer predictions are made has combined with several old philosophical ideas to bring about a revolution in computational science. The resulting panorama of formidable new challenges and research opportunities have to do with what computer models were always intended to do: make predictions of physical reality. Today, however, the phenomena and processes we ask computer models to predict are of enormous importance to critical decisions that affect our welfare and security—concerning, for example, climate change, the performance of energy and defense systems, the biology of diseases, and the outcome of medical procedures. With such high stakes, we must insist that the predictions include concrete, quantifiable measures of uncertainty. In other words, we must know how good the predictions are. The term “predictive simulation” has thus taken on a special meaning: the systematic treatment of model and data uncertainties and their propagation through a computational model to produce predictions of quantities of interest with quantified uncertainty.

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