The goal of the CAGE project is to develop a software platform within which we can rapidly recreate a given part of the world - complete with the "look and feel" of that part of the world and, more importantly, with people who behave in accordance with models of the behaviors of the socio-cultural-ethnic-religious groups to which they belong, and whose behaviors can be predicted through the use of some of our predictive tools such as SOMA and CONVEX (as well as others that may be developed by other researchers).

Nowadays there are plenty of applications which have to deal with incomplete and inconsistent data. In many cases, different users of the same database might have different needs and might want to resolve inconsistency and incompleteness by taking their knowledge of the data into account. Tools for managing inconsistent and incomplete data today do not support such users. The main goal of this project is to provide a single tool to define policies for handling inconsistency and incompleteness according to users' needs.

The SOMA Terror Organization Portal (STOP) is an online tool that provides national security experts, policy analysts, and political science researchers with a single point of contact through which to access data on terror organizations and the behavioral modeling and forecasting tools developed by LCCD.

Numerous applications need to continuously monitor a body of data for the occurrence of certain activities. Data to be monitored might include activity at an airport or military base or another sensitive site, video streams from surveillance cameras, or logs generated by web applications or any transaction processing system (e.g., ATMs). Applications might require activity detection to be preformed either in real-time as data is being received or offline after a body of data has been acquired. Activities tend to be high-level and can often be executed in many different ways. Our research is aimed at developing techniques and algorithms to formally describe what the activities of interest are and identify instances of them from a body of data, both online and offline.

COSI leverages compute clouds and is not only the first algorithm to perform subgraph matching using clouds, but is also the first algorithm to perform subgraph matching of complex query subgraphs against a social network database consisting of 778M edges in under a second.

The DOGMA project investigates novel approaches to storing and retrieving graph data from disk. DOGMA features index structures and retrieval algorithms inspired by concepts from graph theory which have been shown to outperform existing systems.

Our objective is to develop formal models and computational techniques that will lead to better decision-making in various multi-player domains.

We are developing formalisms and algorithms for game-tree search in partially-observable Euclidean space, and implementation and tests in a scenario where a multi-agent team of tracking agents pursues a target agent that wants to evade the tracking agents

Using generalized annotated programs, we study "social network optimization problems" (SNOPs) where we wish to achieve a certain goal (e.g. maximize the expected number of adoptees of a product or minimize the spread of a disease) where we have limited resources to use in trying to achieve the goal (e.g. give out a few free plans, provide medication to key people in the SN) - and want to use these resources in a way to maximize diffusion through the network.

There are many applications where we have pairs of locations - observations and partners - in a geospatial area related based on a set of constraints. This new type of reasoning is called Geospatial Abduction. SCARE - the Spatio Cultural Abductive Reasoning Engine employs this new type of reasoning. Recently, we implemented SCARE to predict IED caches based on attack locations.