Accurate indoor positioning has the potential to transform the way people navigate indoors similar to the way the GPS transformed outdoor navigation. Over the last 20 years, many indoor positioning technologies have been proposed and experimented by both academia and industry.

Fast-placing Machine Learning (ML)  is continuously in need of new algorithms, but testing their effectiveness is utterly time-consuming. "Asymptotic Performance Of Regularized Quadratic Discriminant Analysis Based Classifiers," a new paper presented by CEMSE Ph.D. Student Khalil Elkhalil puts ML on the fast gear and a finalist for the Best Student Paper Award in the IEEE MLSP Conference held last September in Roppongi, Tokyo, Japan.