Learning-based Modeling Approach for Information Asset Valuation and Selection

This research opportunity through the Army Research Laboratory Research Associateship Program (ARL-RAP) focuses on developing novel methods for information assets selection and content filtering from high dimensional data. The research will develop and validate an approach for context aware and adaptive learning models for selecting the most relevant and valuable information assets from high dimensional streaming data and quantification approaches to confidence levels on the models. The project specifically focuses on utilizing state-of-the-art machine learning algorithms to dynamically adapt to, or learn from human or agent actions and contextual situations and environments. The ARL-RAP is designed to significantly increase the involvement of creative and highly trained scientists and engineers from academia and industry in scientific and technical areas of interest and relevance to the Army. The research will be conducted within the Computational and Information Sciences Directorate (CISD), which conducts research in disciplines relevant to achieving the digital battlefield, including communications, atmospheric modeling, battlefield visualization, and computing.