This program seeks fundamental research that potentially leads to scientific advancements in informatics and computation which can support processing and making sense of disparate information sources. Successes in addressing the research sub-areas stated below would give the U.S. Air Force new capabilities to: (1) shift emphasis from sensing to information; (2) understand the underpinning of autonomy; (3) relieve human's cognitive overload in dealing with the data deluge problem; (4) enhance human-machine interface in information processing. To accomplish the research objectives, this program focuses on, but is not limited to, new techniques in mathematics, computer science, statistics and logic which have potentials to: (1) cope with various disparate and complex data types; (2) construct expressive data structures for reasoning and computation; (3) bridge correlational with causal discovery; (4) determine solutions or obstructions to the local-to-global data-fusion problem; (5) mechanize reasoning and computing in the same computational environment; (6) yield provably efficient procedures to enable or facilitate data analytics; (7) deal with high-dimensional and massive datasets with provably guaranteed performance. 

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Proposals are expected to be for cutting-edge innovative research that could produce discoveries that would have a significant impact on enabling new and improved Army operational capabilities and related technologies. One of ARO's priority research areas is computing science, with a particular emphasis on visualization. Specific research areas of interest are, but not limited to, discrete mathematics, computational geometry, robust geometric computing, graph theory, geometric and solid modeling, interactive graphics, 3D visualization tools, verification & validation, and synthetic environments. Special emphasis is placed on making very large simulations and the visualization of massive data sets faster, more computationally efficient, and more interactive for the user while maintaining an appropriate level of fidelity and physical realism.

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Under its Topic Area 6: Predictive and Causal Modeling - Bridging the Gap, ONR is interested in receiving applications which address: Big data and computational power. While deep learning (DL) clearly benefited from more (training) data and more computational power, causal modeling was not as much affected by these new developments. Indeed, finding causal relations in big networks is much more difficult compared to small networks. How can we bring big data to causal modeling? Given the large amounts of data available, it is appealing to explore the benefits of natural experiments. Although they resemble random experiments, they are quite different since we don't have control over data generation. Can machine learning (ML) be used to make a bridge between natural and random experiments? 

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The Office of Naval Research (ONR) is interested in receiving proposals for Long-Range Science and Technology (S&T) Projects which offer potential for advancement and improvement of Navy and Marine Corps operations. ONR's Division of Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance invests in areas of science and their applications such as data science, mathematical and computational science, computer and information sciences, quantum information sciences, cyber security, electronics, command and control and combat systems, communications, cyber operations, electronic warfare, sensing and surveillance, and precision timing and navigation. One of this Division's specific thrusts and focused research areas is: Mathematics, Computers and Information Sciences, which sponsors basic and applied research, and advanced technology development efforts in mathematics, computer and information sciences that address Navy and Department of Defense needs in computation, information processing, information operation, information assurance and cybersecurity, decision tools, and command and control with specific focus on enabling rapid, accurate decision making. Specific scientific and technical areas include: a) Applied and computational analysis; b) Command and control; c) Computational methods for decisionmaking; d) Cyber security and complex software systems; e) Machine learning, reasoning, and intelligence; f) Mathematical data science; g) Mathematical optimization and operations research; h) Quantum information sciences.

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The Advanced Scientific Computing Research (ASCR) program's mission is to advance applied mathematics and computer science; deliver, in partnership with disciplinary science, the most advanced computational scientific applications; advance computing and networking capabilities; and develop, in partnership with the research community, including U.S. industry, future generations of computing hardware and tools for science. The strategy to accomplish this has two thrusts: developing and maintaining world-class computing and network facilities for science; and advancing research in applied mathematics, computer science and advanced networking.

The priority areas for ASCR include the following:

The mission of the Biological and Environmental Research (BER) program is to support fundamental research and scientific user facilities to achieve a predictive understanding of complex biological, climatic, and environmental systems for a secure and sustainable energy future.

Microsoft Research offers researchers the opportunity to apply for cloud computing resources to support their data intensive research projects. Microsoft recognizes that the current landscape of cloud based research makes heavy use of data that can take advantage of Azure services and tools to efficiently drive insights from data. Specifically, they seek proposals for projects that use, but are not limited to, Azure Analytics services such as Machine Learning, Stream Analytics, Data factory, Event hubs, Notification hubs, and HDInsight based services. Projects can be in any research discipline, as long as they clearly articulate the data that the proposed research relies on, and the Azure based analytics services that will be used.