ITL MSE Grant Application Process

To find instructions and submit an application go to www.grants.gov.

Questions concerning the ITL program may be directed to:
Patricia Wilburg
Information Technology Laboratory
100 Bureau Drive, Stop 8900
Gaithersburg, MD 20899-8420
301-975-6994
Email:  patricia.wilburg [at] nist.gov (patricia[dot]wilburg[at]nist[dot]gov)

Evaluation Criteria

The evaluation criteria that will be used in evaluating applications considered by the ITL Grant Program and assigned weights are as follows, for a total maximum of 30 points:

1. Technical Quality and Intellectual Merit. The extent to which the proposed activities are innovative, original, or potentially transformative; whether the research plan is well-reasoned, well-organized and based on a sound rationale; and whether the plan incorporates a reasonable mechanism to assess success. (0-10 points)
2. Potential Impact of the Results. The probability of achieving technical application of the results and the likelihood for the project to exert a sustained, powerful influence on the research field(s) involved. The extent to which the applicants approach aligns with ITL's programs and mission (See Section I.7. of this NOFO). (0-10 points)
3. Capability to Perform the Work. The extent to which the applicant organization, any proposed partner organizations, and key personnel, have the qualifications (e.g., training, experience, accomplishments) and resources (e.g., facilities, equipment) needed to support the proposed project and successfully achieve the stated objectives. (0-5 points)
4. Match of Budget to Proposed Work. Assessment of the budget compared to the proposed work to ascertain the reasonableness of the request. (0-5 points)

Program Description 

Program Description: The mission of the Information Technology Laboratory (ITL) is to cultivate trust in information technology (IT) and metrology and is accomplished using its world-class measurement and testing facilities and encompassing a wide range of areas of computer science, mathematics, statistics, and systems engineering.

The ITL Grant Program provides financial assistance to support the conduct of research or a recipient’s portion of collaborative research consistent with the ITL’s missions to support research in the following fields: Applied and Computational Mathematics, Artificial Intelligence, Big Data Analytics, Biometrics, Cloud Computing, Cyber-Physical Systems, Cybersecurity, Forensic Science, Health Information Technology, High-Performance Computing, Human Factors and Usability, Information Access, Information Processing and Understanding, Internet of Things (IoT), Metrology Infrastructure for Modeling and Simulation, Privacy Engineering, and Statistical Design, Analysis, and Interpretation.

Additional information about the ITL and ITL Programs may be obtained at www.nist.gov/itl.

Financial support may be provided to attend education and outreach programs, conferences, workshops, or other technical research meetings that are relevant to the mission of the ITL. Financial support may also be provided to organizations sponsoring conferences, workshops, or other technical events that are relevant to the mission of the ITL. However, NIST cannot be an official sponsor or cosponsor for any event funded through this program.

All applications submitted to the ITL Grant Program must be in accordance with the program objectives listed below. The contact person for the ITL Grant Program, who may be contacted for clarification of program objectives, is Patricia Wilburg and she may be reached at 301-975-6994 or by e-mail at patricia.wilburg [at] nist.gov (patricia[dot]wilburg[at]nist[dot]gov).

NIST Artificial Intelligence Innovation Lab (NAIIL): NAILL develops measurement science capabilities to accelerate innovation in providing a foundation for the community to evaluate and test AI in ways that will improve its functionality and trustworthiness. Specific topics of interest include evaluating safe and trustworthy AI systems, and foundational research on examining, measuring, and understanding the tradeoff among various aspects of trustworthy AI systems, which include valid and reliable; safe, secure; and resilient; accountable; and transparent; explainable; and interpretable; privacy enhanced; and fair with biases managed.

Applied and Computational Mathematics Division (ACMD) nurtures trust in metrology and scientific computing through the development and application of advanced mathematical and computational techniques and tools. Current topics of interest include: the Mathematics of Special Functions; Quantum Information Science and Engineering; Combinatorial Methods; and Modeling of Internet of Things for Health Applications.

Applied Cybersecurity Division (ACD) implements practical cybersecurity and privacy through outreach and effective application of standards, guidelines, and technologies. ACD establishes cybersecurity and privacy standards and practices in an open, transparent, and collaborative way, and conducts research in applied cybersecurity and privacy. Specific objectives of interest in these areas of research include education and workforce; risk management; identity and access management; Internet of Things cybersecurity, hardware cybersecurity, privacy risk management; cybersecurity and privacy of genomics data, and cybersecurity and privacy considerations for emerging technologies.

Computer Security Division (CSD) develops cybersecurity standards, guidelines, tests, data references and metrics to protect federal information systems. CSD helps to develop innovative security technologies that enhance the nation’s ability to address current and future computer and information security challenges. CSD’s research focuses on cryptography, automation, identity and access management, supply chain risk management, cybersecurity metrics, security engineering and cryptographic conformance testing. The Division maintains a Computer Security Resource Center (CSRC), which provides access to NIST's cybersecurity-and information security-related projects, publications, news, and events. Specific objectives of interest in these areas of research include Cryptography and Cryptographic Test Methods; Hardware Cybersecurity; Threshold Cryptography; Post Quantum Cryptography; Security Testing Tools and Metrics; Vulnerability Semantics; Privacy Enhancing Cryptography; Cybersecurity Conformance Testing; Security Automation; and Security Engineering.

Information Access Division (IAD) supports technologies used to access complex information relating to human action, behavior, communication, or characteristics. Through collaborations with industry, academia, and the federal government, IAD enables the advancement of these technologies for commercial use. IAD provides guidelines and measurement methods to accelerate this evolution. Specific objectives of interest in these areas of research include: Measurement approaches for trustworthy and responsible AI, including human-centered approaches; Biometrics for Forensics, Quality, Synthetic Data Generation, and Digital Identity; Analytics and methods addressing (Synthetic) Content authentication and Provenance in the areas of Generative AI and Deepfake Detection; Human-Centered Cybersecurity; Information Extraction; Modeling and Generating Language and Video Content; Search, Recommendation and Information Retrieval; Research into the use of Foundational Models to support Information Access Activities; Watermarking and Information Provenance.

Software and Systems Division (SSD) works with industry, academia, and other government agencies to accelerate the development and adoption of correct, reliable, and testable software. This collaborative effort leads to increased trust and confidence in deployed software and methods to develop better standards and testing tools. SSD focuses on advances in state-of-the-art software testing and facilitates the transfer of applications and technologies into national infrastructures and commercial sectors. SSD also collaborates with other government agencies to develop Computationally-Enabled Measurements that leverage data-driven approaches, Artificial Intelligence & Machine Learning (symbolic, neural networks, and generative AI), and High-Performance Computing. Specific objectives of interest in these areas of research include artificial intelligence, digital forensics; data analytics; data storage; cloud computing; health IT; high-performance computing; image analytics; IoT for health applications; material genome initiative; medical device interoperability; software assurance; systems biology; and systems interoperability.

Statistical Engineering Division (SED) conducts fundamental and applied statistical research on problems in metrology; develops and applies best practices for the characterization of measurement uncertainty; and implements modern methods and techniques for survey and experiment design, data analysis, statistical modeling, and probabilistic inference using computer software. Specific objectives of interest in these areas of research include statistics for metrology (with an emphasis on the design and analysis of experiments for interlaboratory comparisons, calibration, or reference material certification); statistical methods in multiple domains including forensic science, informatics, communications technology, and next-generation clock metrology; statistical aspects of machine learning and artificial intelligence; and enhanced access to statistical analysis methods via software.