What We Do

The Laboratory for Analytic Sciences’ research themes focus on using data to improve intelligence analysis. Some of the questions arise from properties of the data—they might be large, streaming or heterogeneous—while others arise from applications specific issues.

Content Triage

How can technology help intelligence analysts determine what’s important in order to effectively perform their analysis?

When we are faced with large data sets – like video, images, audio and text – how do we prioritize what’s most important? The process of quickly and easily finding valuable information within large data sets is key for intelligence analysts. Content triage projects aim to improve analysts’ ability to efficiently and effectively search, explore, prioritize, retain and extract value from an ever-increasing amount of data in various formats. We research and apply methods that enable the analysis of content in order to find the most relevant information. 

Project Examples

Content triage projects demonstrate novel ways to address mission challenges around the ever-present need to process and exploit large data volumes, like video, images, audio and text.

Human-Machine Teaming

How can technology help intelligence analysts lighten their cognitive load? 

Technology may reduce the burden associated with analytic tasks, like content triage, but for it to be effective we need an understanding of how people actually incorporate new technologies into their workflows. Human-machine teaming helps analysts gather data and produce intelligence on adversaries’ actions, intentions, and capabilities so decision-makers can make plans and execute policies and operations. Projects in this space build models of what analysts do, identify tasks that could be improved, develop interventions, and identify current analytic methods that could be used in new ways to enhance analyst tradecraft.

Project Examples

Human-machine teaming projects demonstrate ways to address mission challenges around the need to enhance the effectiveness of human analysts partnering with automated technology.

Operationalizing Artificial Intelligence and Machine Learning (AI/ML)

How can we decrease the costs of machine learning? Where can AI/ML give analysts a radical strategic advantage? 

Our research on machine learning (ML) and artificial intelligence (AI) focuses on questions around how machine learning techniques can still be useful even when working under a variety of constraints in an operational environment. This research theme supports LAS’s strategic goal of advancing the science of ML and operationalizing it for intelligence analysts. How can practical applications of ML be used to address challenges that mission analysts face now, or will face in the future?

Project Examples

These projects demonstrate novel ways to address mission challenges around the ever-present need for machine learning and artificial intelligence to be useful even when working under operational environment constraints — whether they be financial, time, or cognitive resources.