ONHS Report: FA875024S7006

Automated Processes for Knowledge Discovery and Information Retrieval

Solicitation ID: FA875024S7006

Agency: AFRL/Information Directorate

Type: Presolicitation

Deadline: 2029-09-09T17:00:00 ET

Eligibility: Maybe (Dual Approval Required)

Funding: Total funding is approximately $99M. Individual awards will normally range from $3M to $35M, with the potential for awards up to the BAA ceiling amount.

Doc Type: BAA

Clearance: Top Secret

Program Manager: Carolyn Sheaff

Last Updated: 2025-11-24 13:09

Analyzed File: 24-06 Amend 4 first repub.docx

Eligibility Reasoning

Federally Funded Research and Development Centers (FFRDCs) ... cannot propose to this BAA in any capacity unless they meet the following conditions: FFRDCs must clearly demonstrate that the proposed work is not otherwise available from the private sector; and FFRDCs must provide a letter on official letterhead from their sponsoring organization citing the specific authority establishing their eligibility... FFRDC and Government entity eligibility will be determined on a case-by-case basis.

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

Summary

The Air Force Research Laboratory (AFRL) is soliciting white papers under Broad Agency Announcement (BAA) FA8750-24-S-7006 for the 'Automated Processes for Knowledge Discovery and Information Retrieval' program. With a total estimated funding of $99 million available through September 2029, this two-step BAA seeks innovative research to develop, test, and deliver technologies that process and exploit multi-source, multi-domain data (including classified, open source, and non-traditional sources). The primary objectives are to improve Battlespace Awareness, derive decision-quality information, and automate analysis and decision-making processes. Key technology interests include algorithms for learning normal patterns of behavior, detecting anomalies, anticipating future events, discovering knowledge gaps, and enabling continuous incremental learning to adapt to evolving situations across air, space, ground, maritime, and cyberspace domains.

Overall Technical Areas

Multi-source Data FusionData Exploitation TechnologiesAutomated Data AnalysisKnowledge Discovery SystemsInformation Retrieval AutomationMulti-Domain Activity AnalysisCross-Source Data CorrelationPattern of Life AnalysisBehavioral Pattern LearningAnomaly Detection AlgorithmsPredictive AnalyticsContinuous Incremental LearningKnowledge Gap AnalysisHypothesis Generation Algorithms

Probable LBNL Areas

BSACSAEESAESAETALDPSA

Focus Areas

Example Priorities:

process and exploit multi-source, multi-domain classified, open source, non-traditional source data
reduce the time needed to search and correlate data from multiple sources on multiple systems
discovery and analysis of emerging activities across multiple domains (ground, air, maritime, space and cyberspace)

Specific Technical Skills:

Multi-source Intelligence (Multi-INT)Data FusionInformation RetrievalOpen Source Intelligence (OSINT)Data CorrelationKnowledge Graph ConstructionBig Data AnalyticsCross-Domain Analysis

Potential PIs for this Area:

Name	Score	Organization
Gazi Mahmud	0.70	BSA \| Environmental Genomics & Systems Biology \| Molecular Ecosystems Bio
Alexander Sim	0.67	CSA \| SciData \| Data Science Research
Ana Kupresanin	0.67	CSA \| Computing
Marcos Lopez de Prado	0.67	CSA \| SciData \| Data Science Research
Drew Paine	0.67	CSA \| SciData \| Data Science Applications
Chris Mungall	0.67	BSA \| Environmental Genomics & Systems Biology \| BioSystems Data Science
Xiaoya Chong	0.67	ESA \| ALS \| PS Computing staff
Shreyas Cholia	0.67	CSA \| SciData \| Data Science Applications
Ana Comesana	0.66	ETA \| Building Technology & Urban Systems \| Bldg & Industrial App Dpt
Zhe Bai	0.66	CSA \| AMCR \| Computer Science

Example Priorities:

Provide richer and more robust patterns of life to support timely, effective and efficient command and control and decisions.
Algorithms for exploiting data to learn normal patterns of behavior, detect deviations from normalcy, and anticipate future behavior
Learning algorithms which construct models of normal activity patterns at a variety of conceptual, spatial, and temporal levels

Specific Technical Skills:

Pattern of Life AnalysisBehavioral AnalyticsAnomaly DetectionMachine LearningSpatiotemporal ModelingTime Series AnalysisUnsupervised LearningActivity Recognition

Potential PIs for this Area:

Name	Score	Organization
Gazi Mahmud	0.68	BSA \| Environmental Genomics & Systems Biology \| Molecular Ecosystems Bio
C. Anna Spurlock	0.67	ETA \| Energy Analysis & Environmental Impacts \| Sustainable Energy Dept
Xiaoya Chong	0.67	ESA \| ALS \| PS Computing staff
Charuleka Varadharajan	0.67	EESA \| EG \| Geochemistry Dept
Marcos Lopez de Prado	0.67	CSA \| SciData \| Data Science Research
Alexander Sim	0.67	CSA \| SciData \| Data Science Research
Zhe Bai	0.66	CSA \| AMCR \| Computer Science
Ana Kupresanin	0.66	CSA \| Computing
Jeetika Malik	0.66	ETA \| Building Technology & Urban Systems \| Bldg Technologies Dept
Ana Comesana	0.66	ETA \| Building Technology & Urban Systems \| Bldg & Industrial App Dpt

Example Priorities:

Anticipate events by building a deep understanding of the networks that give rise to specific incidents.
anticipate future behavior
Characterize, locate and compare/contrast activities, driving rapid data exploration and discovery of significant events.

Specific Technical Skills:

Predictive AnalyticsNetwork AnalysisGraph TheoryEvent PredictionCausal InferenceProbabilistic ModelingForecasting AlgorithmsDeep Learning

Potential PIs for this Area:

Name	Score	Organization
Marcos Lopez de Prado	0.68	CSA \| SciData \| Data Science Research
Alexander Sim	0.68	CSA \| SciData \| Data Science Research
Shashank Subramanian	0.67	CSA \| NERSC \| Sci Eng & Workflows Department
Gazi Mahmud	0.67	BSA \| Environmental Genomics & Systems Biology \| Molecular Ecosystems Bio
Marco Pritoni	0.67	ETA \| Building Technology & Urban Systems \| Bldg Technologies Dept
Yuting Chen	0.67	ETA \| Energy Analysis & Environmental Impacts \| Energy Efficiency Studies
Ana Comesana	0.67	ETA \| Building Technology & Urban Systems \| Bldg & Industrial App Dpt
Haitam Laarabi	0.67	ETA \| Energy Analysis & Environmental Impacts \| Sustainable Energy Dept
Corneel Casert	0.67	CSA \| NERSC \| Sci Eng & Workflows Department
Stephanie Prince	0.67	CSA \| SciData \| Data & Computational Science

Example Priorities:

automate analysis/decision processes
Algorithms which discover gaps in existing knowledge, create hypotheses about what the missing data might provide, and provide a recommended collection tasking.
Continuous incremental learning which enables the models of normal behavior to adapt well to evolving situations while maintaining high levels of performance.

Specific Technical Skills:

Automated ReasoningDecision Support SystemsHypothesis GenerationIncremental LearningOnline Machine LearningAdaptive SystemsExplainable AI (XAI)Automated Tasking

Potential PIs for this Area:

Name	Score	Organization
Gazi Mahmud	0.69	BSA \| Environmental Genomics & Systems Biology \| Molecular Ecosystems Bio
Oluwamayowa Amusat	0.68	CSA \| SciData \| Data Science Applications
Marcos Lopez de Prado	0.67	CSA \| SciData \| Data Science Research
Neel Rajeshbhai Vora	0.67	PSA \| ATAP \| BACI
Zhe Bai	0.67	CSA \| AMCR \| Computer Science
Shawn Tornga	0.67	LD \| LD \| ONHS
Alexander Sim	0.67	CSA \| SciData \| Data Science Research
Marco Pritoni	0.67	ETA \| Building Technology & Urban Systems \| Bldg Technologies Dept
Shreyas Cholia	0.66	CSA \| SciData \| Data Science Applications
Xiaoya Chong	0.66	ESA \| ALS \| PS Computing staff

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	0.74	CSA \| SciData \| Data Science Applications · Automated Data Analysis
Satyarth Praveen	0.74	CSA \| SciData \| Data Science Applications · Knowledge Discovery Systems
Michael Mahoney	0.73	CSA \| SciData \| Data Science Research · Predictive Analytics
Houjun Tang	0.73	CSA \| SciData \| Data Science Applications · Data Exploitation Technologies
Wei Zhang	0.73	CSA \| SciData \| Data Science Research · Cross-Source Data Correlation
Alexandre Bayen	0.73	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Multi-Domain Activity Analysis
Mark Bandstra	0.72	PSA \| NSD \| ANP · Anomaly Detection Algorithms
Victor Negut	0.72	PSA \| NSD \| ANP · Multi-source Data Fusion
Josh North	0.72	EESA \| CESD \| Earth System Sciences · Pattern of Life Analysis
Thomas Hendrickson	0.71	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Knowledge Gap Analysis

Fundamental Research Exemption (FRE)

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. Violations of these regulations can result in criminal or civil penalties.