Bad cellular reception is a universal frustration, but keeping the public at four bars takes a lot of work. Cellular providers constantly test, tune, and develop their infrastructure, with a close eye on their performance – and that of their competitors. Optimizing the network requires keeping tabs on signal strength, and on identifying problems quickly so that enhancements can be made, but high-quality data isn’t available for every time and location. To fill in the gaps, operators need to be able to predict when and where signal strength will be strong, and when it will fall short of customer expectations. In work being presented at the 2019 World Wide Web Conference, NCASD Lab PI Butts with collaborators Emmanouil Alimpertis, Athina Markopoulou, and Konstantinos Psounis show how machine learning methods can be used to assist with this problem. Using flexible modeling techniques that can easily adapt to the peculiarities of local geography, the team’s approach is able to stitch together even irregularly and unevenly sampled measurements of signal strength from users’ phones into a “map” that allows signal strength to be accurately predicted at any location in the area, at any time of day. This approach improves on prior efforts that used less flexible techniques, that did not consider both location and time, or that were limited to particular types of measurement. The team’s work demonstrates how the latest developments in data analysis are helping to maintain and improve the lifelines on which we all depend.