Computer, information science & engineering research

information science and engineering
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Here, Open Access Government charts the U.S. National Science Foundation’s priorities for upholding its leadership in computer, information science & engineering research

The U.S. National Science Foundation (NSF) is an independent federal agency set up in 1950(1) covering a remarkable array of research areas such as biological sciences, computer and information science and engineering, as well as mathematical and physical sciences(2). This article will look at the work of the Directorate for Computer and Information Science and Engineering (CISE) specifically, to illustrate the wider aim of the NSF to “to promote the progress of science”(1) and support research that transforms the future.

Computer and Information Science and Engineering

CISE is concerned with upholding its leadership in computing, communications, engineering and information science in the United States. Making sense of the uses and principles when it comes to using communications, advanced computing plus information systems in service to society are important aspects of CISE’s mission. Their vital work also concerns advanced cyberinfrastructure to accelerate and enable innovation plus discovery in all science and engineering disciplines. Contributing to affordable participation in an information-based society is also an element of CISE’s work.

But how is all this achieved? Here is a summary of how.

  • Supporting investigator-initiated research and education in all areas of computer and information science and engineering, fostering interdisciplinary collaboration.
  • Helping maintain and develop cutting-edge national cyberinfrastructure for education and research.
  • Contributing towards the skills of a computer and information technology workforce to ensure their success in an increasingly competitive global market.

As well as an Office of the Assistant Director, CISE is made up of four other units. A portfolio of proposal competitions and grants are managed by each unit, and while directors can be assigned as the point of contact for precise sub-disciplines, collaboration occurs within each program, across each unit plus between directorates and units.

We’ll now look at a few examples of research CISE is supporting.

Transformative computer research

In early June 2021, we learn that NSF-funded researchers developed BRAILS (Building Recognition using AI at Large-Scale), at SimCenter. The NSF-funded Natural Hazards Engineering Research Infrastructure program is a computational modelling and simulation centre for researchers in the field of natural hazards engineering at the University of California. Charles Wang, Lead Developer of BRAILS, says that the project arose from the requirement to “quickly and reliably characterize the structures in a city. We want to simulate the impact of hazards on all the buildings in a region, but we don’t have a description of the building attributes.”

The researchers used supercomputers at the Texas Advanced Computing Center, including Frontera and Maverick 2, the former by the way is the “fastest academic supercomputer in the world”. Manish Parashar, Director of NSF’s Office of Advanced Cyberinfrastructure shared his thoughts on new computational methods transforming engineering discoveries. “Frontera is a leadership computing resource that serves science and engineering research for the nation. We are excited about the new computational methods and techniques Frontera is enabling to transform how engineering discoveries are being made to make our lives safer.” (4)

Machine learning for cosmological simulations

In other news, we find out that the fields of astrophysics, high-performance computing and machine learning joined together to usher in a new dawn for high-resolution cosmology simulations, in NSF-supported research. Cosmological simulations are a crucial element of teasing out the mysteries of the universe, like dark energy and dark matter. While simulations could focus on a small area at high resolution or encompass a large volume of the universe at low resolution, this issue has been surmounted “by teaching a machine learning algorithm based on neural networks to upgrade a simulation from low resolution to super resolution”, the NSF website describes.

Going into a bit more detail about this, James Shank, a Program Director in the NSF’s Division of Physics explains: “It’s clear that AI is having a big effect on many areas of science, including physics and astronomy. Our AI Planning Institute program is working to push AI to accelerate discovery. This new result is a good example of how AI is transforming cosmology.” (5)

Deep learning networks research

Elsewhere, it is noted that a new NSF-funded study proves that artificial intelligence (AI) systems could attain higher levels of performance if programmed with sound files of human language as opposed to numerical data labels. “To understand why this finding is significant,” says Mechanical Engineer Hod Lipson, “it’s useful to understand how neural networks are usually programmed, and why using the sound of the human voice is a radical experiment.”(6)

Closing remarks

These examples of research from the CISE certainly fit in with the NSF’s overall mission to advance “the frontiers of science and engineering through exploration and innovation”, as described in the statement on the President’s FY 2022 discretionary budget request. “NSF stands ready to maximize the impact of this increase in funding and tackle critical challenges to bolster the U.S. economy and our leadership in critical and emerging areas of research and technological advancements,” the statement concludes.(7)



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