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COMPUTER & INFORMATION
SCIENCE AND ENGINEERING |
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A Message from CISE Leadership
Dear CISE community.
In particular, we note that CRII is aimed at leveling the playing field to help early-career researchers launch their careers even in contexts where they may not be receiving essential startup resources. For the purposes of this program, CISE defines "essential resources" as sufficient funds for 48 months of graduate student support. Faculty at undergraduate and two-year institutions may use funds to support undergraduate students, and may optionally use the additional RUI designation. We continue to refine the CRII program FAQ and department chair’s template letters to clarify eligibility.
CAREER is a foundation-wide activity that offers the National Science Foundation's most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization. NSF encourages submission of CAREER proposals from early-career faculty at all CAREER-eligible organizations and especially encourages women, members of underrepresented minority groups, and persons with disabilities to apply. This year’s CAREER deadline is coming up on July 26.
We hope you enjoy this month’s newsletter.
Best,
 Margaret Martonosi
NSF Assistant Director for CISE
Joydip Kundu
Acting Deputy Assistant Director for CISE
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News & Announcements
"Using large-scale experiments and machine learning to discover theories of human decision-making."
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The new regional testbed features wireless research to enable high-throughput, universal and affordable rural broadband.
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The partnership paves the way for new collaborations between researchers in both countries to build inclusive partnerships at the frontiers of science and emerging technologies and fosters a shared commitment to equity, diversity and inclusion within the research enterprise.
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Get more CISE News
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Program Spotlight
Platforms for Advanced Wireless Research (PAWR)
Research platforms the size of a small U.S. city will foster use-inspired, fundamental research and development that will enhance advanced wireless networks. The platforms called, PAWR (pronounced "power") enable academic and industry researchers to experiment with approaches at scale, or “in the wild,” that cannot be studied in lab environments.
PAWR is a public-private partnership funded in equal parts by NSF and an industry consortium of over 30 industry leaders.
The program is managed through the NSF-funded PAWR Project Office (PPO) led by US Ignite, Inc., and Northeastern University to guide the design, development, deployment, and operations of the advanced wireless research platforms. NSF's PAWR program is currently supporting the deployment and initial operations of four advanced wireless research platforms conceived by the U.S. academic and industrial wireless research community. For more information, check out the PAWR platform website.
Last month was a very exciting moment for all of us at CISE and our PAWR program partners, as we announced the fourth testbed in a diverse portfolio of large scale wireless research platforms located throughout the U.S. Designated as ARA: Wireless Living Lab for Smart and Connected Rural Communities, the new platform in Ames, Iowa, complements the technical specialties of earlier PAWR platforms, adding a focus on technologies for rural broadband connectivity. To view this announcement and learn more about the awardees, please click here.
Researchers who wish to make use of PAWR testbeds should pursue supplemental funding as described here.
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SciComm Corner: How CISE is Impacting Your Community
Source: burnsvillemn.gov
Earlier this year, a team of researchers from NSF, the U.S. Geological Survey, University of Pittsburgh, and University of Minnesota published a study on the flow and temperature of river networks.
The research conducted utilizes a novel machine learning method in which an algorithm is ‘taught’ to model physical world properties to make better predictions and guide the algorithm toward physically significant correlations between input and output.
Even when data is scarce, the algorithm can make more accurate predictions regarding river and stream temperatures over different time periods than its predecessors. Researchers believe their approach may be scaled to predict the flow and temperatures of lakes in the future.
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Source: Andrew Kelly, NY Hall of Science
In 2020, CISE and the Academy of Finland (AoF) signed a Memorandum of Understanding (MOU) on Research Cooperation to encourage collaboration between U.S. and Finnish research communities. This new NSF-AoF collaborative research program expects to generate valuable discoveries and innovations, bringing experts from both countries in the areas of artificial intelligence (AI) and wireless communication technologies.
As with past partnerships with Finland (e.g., WIFIUS), the program provides for an international collaboration arrangement whereby US researchers may receive funding from NSF, and Finnish collaborators may receive funding from the Academy of Finland to pursue joint projects.
The partnership program invites researchers from both countries to submit a collaborative proposal. The collaborative proposal will go through a single review process at NSF CISE (Core Small project class). As a Small project class within the CISE Core Programs, this effort has moved to a “no-deadline” format. In general, proposal review is completed within six months of submission.
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Faces of CISE: Dr. Song Han
Dr. Song Han
Assistant Professor, EECS
Massachusetts Institute of Technology
Song Han is an assistant professor at the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT). He received his Ph.D. degree from Stanford University. His research focuses on efficient deep learning computing.
Dr. Han and his team of researchers at MIT have developed a system that could bring deep learning neural networks to new — and much smaller — places, like the tiny computer chips in wearable medical devices, household appliances, and 250 billion other objects that constitute the “internet of things” (IoT).
The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory (only 1MB) and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving user privacy without sending data to the cloud. The study suggests that the era of tiny machine learning on IoT devices has arrived.
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Office of Advanced Cyberinfrastructure (OAC)
OAC supports and coordinates the development, acquisition and provision of state-of-the-art cyberinfrastructure resources, tools and services essential to the advancement and transformation of Science and engineering.
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