John Thomas Wilkinson

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We address the problem of information fusion in uncertain environments. Imagine there are multiple experts building probabilistic models of the same situation and we wish to aggregate the information they provide. There are several problems we may run into by naively merging the information from each. For example, the experts may disagree on the probability(More)
When decisions need to be made in government, the intelligence community (IC) is tasked with analyzing the situation. This analysis is based on a huge amount of information and usually under severe time constraints. As such, it is particularly vulnerable to attacks from insiders with malicious intent. A malicious insider may alter, fabricate, or hide(More)
We are working on the problem of modeling an analyst's intent in order to improve collaboration among intelligence analysts. Our approach is to infer the analyst's goals, commitment, and actions to improve the effectiveness of collaboration. This is a crucial problem to ensure successful collaboration because analyst intent provides a deeper understanding(More)
A user’s cognitive style has been found to affect how they search for information, how they analyze the information, and how they make decisions in an analytical process. In this paper, we propose an approach that uses Hidden Markov Models (HMM) to dynamically capture a user’s cognitive style by automatically exploring the sequence of actions(More)
Social networks are an important way to represent and analyze social phenomena. One aspect that is critical in order to provide relevant and useful analyses is the capability to infuse culture systematically. Cultural elements are typically either lacking or implicitly (and sometimes unintentionally) embedded in social network construction. Thus, current(More)