Call for White Papers

Your research can help solve mission-oriented challenges faced by the intelligence community.

Each summer, the Laboratory for Analytic Sciences (LAS) kicks off the annual planning cycle by releasing a call for white papers. Academic and industry researchers are invited to submit one or more research project ideas. The call for white papers is based on continuing LAS research interests and discussions with U.S. Intelligence Community partners about mission needs. The call for white papers provides examples of technical areas and applications of interest for LAS.

LAS is a mission-oriented academic-industry-government research collaboration that works at the intersection of technology and tradecraft. We apply unclassified examples from academic and commercial partners to intelligence community goals. Collaborators selected for project funding will work with LAS staff to deliver mission-relevant solutions to better support today’s intelligence analysts.

Overview and Logistics

Please watch our latest videos about the logistics, technical areas of interest, and application areas of interest of the 2025 call for white papers.

Operationalizing AI/ML

Interests related to improving the efficiency of creating and maintaining ML models, as well as applications of AI/ML techniques, in particular large language models, that can give intelligence analysts a strategic advantage.

Human-Machine Teaming

Interests related to improving how analysts can partner effectively with automation, particularly through the exploration of novel designs for user experiences that integrate state-of-the-art analytic capabilities.

Content Triage

Interests related to the scalable extraction of information from text, image, speech, audio and video, as well as the summarization of large-scale video and audio content, in order to facilitate the use of this information.

Application Areas of Interest

Object and Activity Detection in Full-Motion Video
Providing Support for Technical Intelligence
Producing Tailored Summaries of Relevant Information
Rule-Constrained Text Summaries
Audio Environment Analysis
Edge Deployment of ML-Based Content Triage Approaches