Group items tagged

The Scalable Parallelism in the Extreme (SPX) program aims to support research addressing the challenges of increasing performance in this modern era of parallel computing. This will require a collaborative effort among researchers in multiple areas, from services and applications down to micro-architecture. SPX encompasses all five NSCI Strategic Objectives, including supporting foundational research toward architecture and software approaches that drive performance improvements in the post-Moore's Law era; development and deployment of programmable, scalable, and reusable platforms in the national HPC and scientific cyberinfrastructure ecosystem; increased coherence of data analytic computing and modeling and simulation; and capable extreme-scale computing. Coordination with industrial efforts that pursue related goals are encouraged.

There is a consensus across the many industries touched by our ubiquitous computing infrastructure that future performance improvements across the board are now severely limited by the amount of energy it takes to manipulate, store, and critically, transport data. While the limits and tradeoffs for this performance-energy crisis vary across the full range of application platforms, they have all reached a point at which evolutionary approaches to addressing this challenge are no longer adequate. Truly disruptive breakthroughs are now required, and not just from any one segment of the technology stack. Rather, due to the complexity of the challenges, revolutionary new approaches are needed at each level in the hierarchy. Furthermore, simultaneous co-optimization across all levels is essential for the creation of new, sustainable computing platforms. These simultaneous technical and organizational challenges have never been as complex or as critically important as they are now. The urgency of solving the multi-disciplinary technical challenges will require new methods of collaboration and organization among researchers. Therefore, a comprehensive and collaborative approach must be undertaken to maximize the potential for successfully identifying and implementing revolutionary solutions to break through the bottleneck of energy-constrained computational performance. Programmers, system architects, circuit designers, chip processing engineers, material scientists, and computational chemists must all explore these new paths together to co-design an optimal solution path.

The Chan Zuckerberg Initiative (CZI) seeks to support up to 10 Imaging Scientists who will work at the interface of biology, microscopy hardware, and imaging software at imaging centers across the United States. "Imaging Scientists" might be engineers, physicists, mathematicians, computer scientists, or biologists who have focused on technology development in either microscopy or data analysis fields. The primary goal of the program is to increase interactions between biologists and technology experts. The Imaging Scientists will have expertise in microscopy hardware and/or imaging software. A successful "Imaging Program" will employ an Imaging Scientist who: a) works collaboratively with experimental biologists on projects at the imaging center; b) participates in courses that disseminate advanced microscopy methods and analysis; c) trains students and postdocs in imaging technology; d) participates in a network of CZI Imaging Scientists to identify needs and drive advances in the imaging field; e) attends twice-yearly CZI scientific workshops and meetings in imaging and adjacent biomedical areas. Each grant will fund salary and fringe benefits for an Imaging Scientist at the center, a modest travel and teaching budget, plus 15% indirect costs. The award period is three years plus an additional two years if the Imaging Program passes a review at year three.

A grand challenge in computing is the creation of machines that can proactively interpret and learn from data in real time, solve unfamiliar problems using what they have learned, and operate with the energy efficiency of the human brain. While complex machine-learning algorithms and advanced electronic hardware (henceforth referred to as 'hardware') that can support large-scale learning have been realized in recent years and support applications such as speech recognition and computer vision, emerging computing challenges require real-time learning, prediction, and automated decision-making in diverse domains such as autonomous vehicles, military applications, healthcare informatics and business analytics.

In today's increasingly networked, distributed, and asynchronous world, cybersecurity involves hardware, software, networks, data, people, and integration with the physical world. Society's overwhelming reliance on this complex cyberspace, however, has exposed its fragility and vulnerabilities that defy existing cyber-defense measures; corporations, agencies, national infrastructure and individuals continue to suffer cyber-attacks. Achieving a truly secure cyberspace requires addressing both challenging scientific and engineering problems involving many components of a system, and vulnerabilities that stem from human behaviors and choices. Examining the fundamentals of security and privacy as a multidisciplinary subject can lead to fundamentally new ways to design, build and operate cyber systems, protect existing infrastructure, and motivate and educate individuals about cybersecurity.

This effort is an open 2 Step BAA soliciting innovative research concepts for the overall mission of the Human-Centered Intelligence, Surveillance, & Reconnaissance (ISR) Division (711 HPW/RHX). The overall RHX research objective is to develop human-centered S&T that enables the Air Force to more effectively execute the ISR mission. This research objective is dual natured: (1) improve the capability to identify, track and locate human targets in the ISR environment and (2) improve the performance of humans who process, exploit, analyze, produce, and disseminate the ISR data and information. Human-centered ISR research encompasses three major research areas: (1) human signatures, (2) human trust and interaction and (3) human analyst augmentation. The human signatures research develops technologies to sense and exploit human bio-signatures at both the molecular level and macro (anthropometric) level. The human trust and interaction research develops technologies to improve human-to-human interactions as well as human-to- machine interactions. The human analyst augmentation research develops technologies to enhance analyst performance and to test the efficacy of newly developed technologies within a simulated operational environment.

Computer systems support a broad range of applications and technologies that seamlessly integrate with human users. While many key building blocks of computer systems are today commercial technologies, the challenge ahead is to envision new technologies, as well as to combine existing technologies, software, and sensing systems into the computer systems of the future that will span wearable computing, the Internet of Things (IoT), "Smart Cities," intelligent transportation systems, personalized healthcare, and beyond. Such computer systems will require new, innovative, and visionary approaches to hardware, wired and wireless communications, consideration of human-computer interactions, and new programming languages and compilers that are limited only by the imagination. They will need to be reliable in the presence of unreliable components, adaptive to changing environments, capable of supporting high-throughput applications and large-scale data storage and processing, and able to meet performance and energy objectives for applications ranging from very low-power embedded systems to large high-performance computing systems. Furthermore, computer systems of the future will need to provide mechanisms for ensuring security and privacy.

In today's increasingly networked, distributed, and asynchronous world, cybersecurity involves hardware, software, networks, data, people, and integration with the physical world. However, society's overwhelming reliance on this complex cyberspace has exposed its fragility and vulnerabilities: corporations, agencies, national infrastructure and individuals have been victims of cyber-attacks. Achieving a truly secure cyberspace requires addressing both challenging scientific and engineering problems involving many components of a system, and vulnerabilities that arise from human behaviors and choices. Examining the fundamentals of security and privacy as a multidisciplinary subject can lead to fundamentally new ways to design, build and operate cyber systems, protect existing infrastructure, and motivate and educate individuals about cybersecurity.

The Cyberinfrastructure for Sustained Scientific Innovation (CSSI) umbrella program seeks to enable funding opportunities that are flexible and responsive to the evolving and emerging needs in cyberinfrastructure. This program continues the CSSI program by removing the distinction between software and data elements/framework implementations, and instead emphasizing integrated cyberinfrastructure services, quantitative metrics with targets for delivery and usage of these services, and community creation.

In today's increasingly networked, distributed, and asynchronous world, cybersecurity involves hardware, software, networks, data, people, and integration with the physical world. However, society's overwhelming reliance on this complex cyberspace has exposed its fragility and vulnerabilities: corporations, agencies, national infrastructure and individuals have been victims of cyber-attacks. Achieving a truly secure cyberspace requires addressing both challenging scientific and ... More at https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504709&WT.mc_id=USNSF_39&WT.mc_ev=click

With this DCL, ECR invites proposals on the development, application, and extension of formal models and methodologies for STEM education research and evaluation, including methods for improving statistical modeling, qualitative modeling, measurement, replication, and learning analytics. This includes research on methodological aspects of new or existing procedures for data collection, curation, and inference in STEM education. Similarly, ECR seeks proposals related to collection of unique databases with cross-boundary value, particularly when paired with innovative developments in measurement or methodology (standard statistical modeling, qualitative research, measurement, replication and learning analytics). Proposers must demonstrate how advances in the methodology will support important theoretical insights in STEM education research or evaluation. Proposers are encouraged to explore a wide range of fundamental research projects (in the areas of quantitative, qualitative, measurement, replication, and learning analytics methodologies)

Founded in 1996, the Marsha Rivkin Center for Ovarian Cancer Research provides funding for multiple efforts, including funding for innovative research pilot studies, scholar grants to up-and-coming investigators to encourage careers in ovarian cancer research, conducting public early detection screening for ovarian cancer, and producing nationally and internationally attended research symposia on ovarian cancer. To that end, the center is accepting applications for pilot study projects in ovarian cancer research. Through the center's Pilot Study Program, approximately ten one-year $75,000 grants will be awarded to support investigator-initiated projects in all areas of ovarian cancer research. Projects designed to analyze data from already funded clinical trials also will be considered. Priority will be given to proposals that are innovative, multidisciplinary, likely to lead to submission of grant applications for independently funded investigations, and have translational research potential.

The confluence of transistor scaling, increases in the number of architecture designs per process generation, the slowing of clock frequency growth, and recent success in research exploiting Thread Level Parallelism (TLP) and Data Level Parallelism (DLP) all point to an increasing opportunity for innovative microarchitecture techniques and methodologies in delivering performance growth in the future. The NSF/Intel Partnership on Foundational Microarchitecture Research will support transformative microarchitecture research targeting improvements in instructions per cycle (IPC). This solicitation seeks microarchitecture technique innovations beyond simplistic, incremental scaling of existing microarchitectural structures. Specifically, FoMR seeks to advance research that has the following characteristics: (1) high IPC techniques ranging from microarchitecture to code generation; (2) "microarchitecture turbo" techniques that marshal chip resources and system memory bandwidth to accelerate sequential or single-threaded programs; and (3) techniques to support efficient compiler code generation. Advances in these areas promise to provide significant performance improvements to continue the cadence promised by Moore's Law.

Career Development Award supports early-career, independent investigators to conduct impactful research under the mentorship of an experienced lung cancer researcher as an opportunity to obtain the funding, mentoring, and experience necessary for productive, independent careers at the forefront of lung cancer research. This award is intended to support impactful research projects with an emphasis on discovery. Submissions from and partnerships with investigators at military treatment facilities, military labs, and Department of Veterans Affairs (VA) medical centers and research laboratories are strongly encouraged.Preliminary data are not required. However, logical reasoning and a sound scientific rationale for the proposed research must be demonstrated.Key elements of this award are as follows.

Artificial Intelligence (AI) has advanced tremendously and today promises personalized healthcare; enhanced national security; improved transportation; and more effective education, to name just a few benefits. Increased computing power, the availability of large datasets and streaming data, and algorithmic advances in machine learning (ML) have made it possible for AI development to create new sectors of the economy and revitalize industries. Continued advancement, enabled by sustained federal investment and channeled toward issues of national importance, holds the potential for further economic impact and quality-of-life improvements.

Artificial Intelligence (AI) has advanced tremendously and today promises personalized healthcare; enhanced national security; improved transportation; and more effective education, to name just a few benefits. Increased computing power, the availability of large datasets and streaming data, and algorithmic advances in machine learning (ML) have made it possible for AI development to create new sectors of the economy and revitalize industries. Continued advancement, enabled by sustained federal investment and channeled toward issues of national importance, holds the potential for further economic impact and quality-of-life improvements.

A key focus of the design of modern computing systems is performance and scalability, particularly in light of the limits of Moore's Law and Dennard scaling. To this end, systems are increasingly being implemented by composing heterogeneous computing components and continually changing memory systems as novel, performant hardware surfaces. Applications fueled by rapid strides in machine learning, data analysis, and extreme-scale simulation are becoming more domain-specific and highly distributed. In this scenario, traditional boundaries between hardware-oriented and software-oriented disciplines increasingly are blurred. Achieving scalability of systems and applications will therefore require coordinated progress in multiple disciplines such as computer architecture, high-performance computing (HPC), programming languages and compilers, security and privacy, systems, theory, and algorithms. Cross-cutting concerns such as performance (including, but not limited to, time, space, and communication resource usage and energy efficiency), correctness and accuracy (including, but not limited to, emerging techniques for program analysis, testing, debugging, probabilistic reasoning and inference, and verification), security and privacy, robustness and reliability, domain-specific design, and heterogeneity must be taken into account from the outset in all aspects of systems and application design and implementation.