ONHS Report: FA8750-26-S-7001

Resilient Analytic Platforms for Intelligence Delivery (RAPID)

Solicitation ID: FA8750-26-S-7001

Agency: AFRL/RI

Type: Presolicitation

Deadline: 2031-02-06T15:00:00 ET

Eligibility: Maybe (Dual Approval Required)

Funding: Approximately $237M total funding. Individual awards will normally range from $10M to $50M, with the potential for awards up to $99M.

Doc Type: BAA

Clearance: Top Secret

Program Manager: Frank Gambino

Last Updated: 2026-02-12 17:03

Analyzed File: 26-01 ARA synopsis v10.pdf

Eligibility Reasoning

Federally Funded Research and Development Centers (FFRDCs)... are subject to applicable direct competition limitations and cannot propose to this ARA in any capacity unless they meet the following conditions: a. FFRDCs must clearly demonstrate that the proposed work is not otherwise available from the private sector; and b. FFRDCs must provide a letter on official letterhead from their sponsoring organization citing the specific authority establishing their eligibility to propose to Government solicitations and compete with industry... This information is required for FFRDCs proposing to be prime contractors or sub-awardees.

Note: This is an AI-generated summary...

Summary

The U.S. Air Force Research Laboratory (AFRL) is soliciting white papers for the Resilient Analytic Platforms for Intelligence Delivery (RAPID) program (FA8750-26-S-7001), an Advanced Research Announcement (ARA) with approximately $237M in total funding available until February 2031. The program seeks to research, develop, and integrate advanced technologies into consolidated, cyber-resilient intelligence analysis platforms. Key technical focus areas include multi-INT fusion, latency reduction, AI/ML for geospatial intelligence (GEOINT), secure cloud and edge computing, and integrated system design. The application is a two-step process, starting with a white paper submission, which may lead to an invitation for a full proposal. Individual awards are anticipated to range from $10M to $50M over 36 months, with the potential for larger awards. The program is generally closed to foreign participation.

Overall Technical Areas

Multi-INT Data FusionAI / Machine LearningAssured & Resilient Cloud ComputingSecure Edge ComputingData Models & OntologiesSynthetic Data GenerationGeospatial Intelligence CorrelationAutomated Processing WorkflowsCross-Domain Information SharingProactive Vulnerability ManagementFederated System ArchitectureAlgorithm Benchmarking & ValidationCyber Threat Detection

Probable LBNL Areas

BSACSAEESAETALD

Focus Areas

Example Priorities:

Develop new analytic techniques, leverage emerging technologies, and integrate diverse sensor and intelligence data to produce more sophisticated, timely, and actionable products and services.
Explore Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate intelligence analysis tasks, improve the accuracy and efficiency of information extraction, correlation, access, visualization, and enhance cyber threat detection and response.
Develop tools, techniques, and secure workflows for managing, processing, and analyzing large, disparate, and often unstructured datasets from diverse intelligence sources.
Develop robust data models and ontologies to facilitate data integration, knowledge representation, and automated reasoning across different intelligence domains.
Explore techniques for automatically generating synthetic data sets representative of mission data to train machine learning models, test algorithms, and characterize data for further processing and analysis.

Specific Technical Skills:

Multi-INT FusionArtificial IntelligenceMachine LearningGEOINT AnalysisData ManagementData ModelingOntology DevelopmentSynthetic Data GenerationData VisualizationHigh-Performance Computing

Potential PIs for this Area:

Name	Score	Organization
Gazi Mahmud	0.72	BSA \| Env. Genomics & Sys. Biology \| Molecular Ecosystems Bio
Shawn Tornga	0.71	LD \| LD \| ONHS
Jean Luca Bez	0.71	CSA \| SciData \| Data Science Research
Alexander Sim	0.71	CSA \| SciData \| Data Science Research
Charuleka Varadharajan	0.70	EESA \| EG \| Geochemistry Dept
Zhe Bai	0.70	CSA \| AMCR \| Computer Science
Oliver Ruebel	0.70	CSA \| SciData \| Data & Computational Science
Kunxiaojia (Tammy) Yuan	0.69	EESA \| CESD \| Earth System Sciences
Danielle Svehla Christianson	0.69	CSA \| SciData \| Data Science Applications
Madison Burrus	0.69	EESA \| CESD \| Integrated Ecosystem Sciences

Example Priorities:

Design and implement integrated prototype systems that are composed of federated frameworks optimized for secure information sharing across disparate and novel data sources.
Information Systems need to be developed with security and security interfaces so that the application of various security practices is transparent to the user, ensuring efficient system use with a high level of security.
As cloud environments rely heavily on distributed assets, it is necessary to develop approaches to fulfill the 3 pillars of the CIA triad (confidentiality, integrity, availability).
What is required is a flexible, resilient framework that allows privacy-preserving transparent migration of mobile application components to cloud infrastructure while maximizing application performance and minimizing energy consumption.
This includes addressing challenges of cross-domain information sharing and secure data exchange between different security enclaves.

Specific Technical Skills:

Secure Cloud ArchitectureEdge ComputingSystems IntegrationCross-Domain SolutionsFederated FrameworksMobile Application SecurityNetwork SecurityDistributed SystemsInformation AssuranceZero Trust Architecture

Potential PIs for this Area:

Name	Score	Organization
Sean Peisert	0.71	CSA \| SciData \| Data Science Applications
Vinay Sawal	0.69	CSA \| NERSC \| Data Center Department
Karlo Berket	0.68	CSA \| SciData \| Data Science Applications
Gazi Mahmud	0.68	BSA \| Env. Genomics & Sys. Biology \| Molecular Ecosystems Bio
John Wu	0.68	CSA \| ESNET \| Tech Adv and Engagement
Georgios Michelogiannakis	0.68	CSA \| AMCR \| Computer Science
Stefan Lasiewski	0.68	CSA \| NERSC \| HPC Technology Department
Adam Slagell	0.67	CSA \| ESNET \| Security
Alexander Sim	0.67	CSA \| SciData \| Data Science Research
Tiffany Connors	0.67	CSA \| NERSC \| Data Center Department

Example Priorities:

Vulnerabilities in DoD and non-DoD systems, to include weapon systems must be identified in the early development stages, so that vulnerabilities are not introduced into the operational environment.
Development of comprehensive tools, techniques, reporting systems and mitigation processes is required.
Develop processes, techniques and reporting mechanisms to verify the accuracy of results received from data processed through algorithms, workflows, and models.
Develop strategies and tools to facilitate secure deployment of open systems architectures within controlled government information system (IS) while adhering to relevant organizational standards and security measures.
Cybersecurity testing should include penetration testing, vulnerability assessments, and incident response simulations.

Specific Technical Skills:

Vulnerability ManagementPenetration TestingDevSecOpsSecurity GovernanceTest and EvaluationConfiguration ManagementSecurity AccreditationPolicy EnforcementRisk ManagementIncident Response Simulation

Potential PIs for this Area:

Name	Score	Organization
Sean Peisert	0.69	CSA \| SciData \| Data Science Applications
Adam Slagell	0.67	CSA \| ESNET \| Security
Tiffany Connors	0.66	CSA \| NERSC \| Data Center Department
Sunhee Baik	0.65	ETA \| Energy Analysis & Env. Impacts \| Energy Markets & Policy
Stefan Lasiewski	0.65	CSA \| NERSC \| HPC Technology Department
Gregory Lemieux	0.65	EESA \| CESD \| Earth System Sciences
Angelos Ioannou	0.65	CSA \| AMCR \| Computer Science
Hannah Cohoon	0.65	CSA \| SciData \| Data Science Applications
Gazi Mahmud	0.65	BSA \| Env. Genomics & Sys. Biology \| Molecular Ecosystems Bio
Alex Newkirk	0.65	ETA \| Building Tech. & Urban Sys. \| Bldg & Industrial App Dpt

Potential LBNL PIs (Overall)

Note on PI Matching: These suggestions are generated through an AI-driven semantic analysis of LBNL staff profiles.

Name	Score	Organization
Anna Giannakou	0.76	CSA \| SciData \| Data Science Applications · Automated Processing Workflows
Talita Perciano Costa Leite	0.75	CSA \| SciData \| Data Science Research · AI / Machine Learning
Oscar Antepara	0.75	CSA \| AMCR \| Computer Science · Algorithm Benchmarking & Validation
Cees De Laat	0.74	CSA \| ESNET \| Tech Adv and Engagement · Federated System Architecture
Tiffany Connors	0.73	CSA \| NERSC \| Data Center Department · Cyber Threat Detection
Chris Mungall	0.73	BSA \| Env. Genomics & Sys. Biology \| BioSystems Data Science · Data Models & Ontologies
Wei Zhang	0.73	CSA \| SciData \| Data Science Research · Geospatial Intelligence Correlation
Zixi Hu	0.72	CSA \| AMCR \| Mathematics · Synthetic Data Generation
Rich Wolski	0.72	CSA \| ESNET \| Science Engagement · Assured & Resilient Cloud Computing
Victor Negut	0.72	PSA \| NSD \| ANP · Multi-INT Data Fusion

Fundamental Research Exemption (FRE)

As of the date of publication of this ARA, the Government does not anticipate work proposed under this ARA to be considered fundamental research. ... Applicable to any effort not considered Fundamental Research. Offerors are responsible for ensuring that all employees and/or subcontractors who will work on a resulting contract are eligible to do so. Any employee who is not a U.S. citizen or a permanent resident will be restricted from working on any resultant contract unless prior approval of the Department of State or the Department of Commerce is obtained via a technical assistance agreement or an export license.