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The specific aims of the proposed research are to:(i) Characterize the frequency of CGM and infusion set faults and improve detections and predictions for populations and individuals,(ii) Improve control-relevant glucose predictions for populations and individuals. Data analysis techniques will enable more accurate estimates of insulin sensitivity and meal effects, including the ability to anticipate when meal events are likely to occur,(iii) Create a deviational simulator to test proposed treatment or control strategies.Rensselaer Polytechnic Institute is a recipient of JDRF's research grant — [Identification of Areas of Artificial Pancreas Algorithm Enhancements Through Big-Data Analysis](http://grantcenter.jdrf.org/rfa/identification-of-areas-of-artificial-pancreas-algorithm-enhancements-through-big-data-analysis-part-1/ "Identification of Areas of Artificial Pancreas Algorithm Enhancements Through Big-Data Analysis") — supported by Tidepool Big Data Donation Project.