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Opportunity at Davies Teaching Fellowships (ARL/USMA)

Information Fusion over Sources of Unknown Reliability


Sensors Electron Devices Directorate-FFP, Signal and Image Processing - FFP

RO# Location
AA.31.02.B7080 Adelphi, MD 207831197


Name E-mail Phone
Kaplan, Lance Michael 301.394.0807


Information fusion requires the processing of disparate hard and soft data sources over various operational contexts. The reliability of the data sources is generally unknown a priori. These sources might report noisy data or worse yet, intentionally report misleading data. Much work has been done in the sensor network community to detect and repair sensor faults. Recently, many efforts in trust/reputation systems and truth-finding have led to methods to jointly estimate the credibility of reports and the reliability of the sources. Most all of these methods focus on a single information domain and they do not exploit the semantic relations between the hard/soft reports or the social relations between the data sources themselves. This research opportunity considers both the theory and applications for detecting and resolving conflicting data from disparate sources and extracting/estimating the resulting information. We anticipate that this research will draw on uncertain representations, such as basic belief mass assignment in belief theory, reasoning using uncertain probabilistic logics, trust estimation in reputation and/or truth-finding systems, and finally social network theory for modeling the sources.



Wang D, Abdelzaher T, Kaplan L: Social Sensing: Building Reliable Systems on Unreliable Data, Morgan Kaufmann, 2015

Kaplan L, et al: "Partial Observable Update for Subjective Logic and Its Application for Trust Estimation," Information Fusion, 2015


Information fusion; Reasoning under uncertainty; Trust and reputation systems; Truth-finding; Social network theory;


Citizenship:  Open to U.S. citizens
Level:  Open to Postdoctoral applicants
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