Matt received his PhD in Electrical Engineering from Northeastern University in 2016. He has experience applying statistical methods to construct solutions which allow severely paralyzed people to communicate via EEG, EMG, or eye gaze inputs. In particular he has developed active learning methods which learn a user’s intended message as quickly as possible, in an Information Theoretic sense, given a user-specific input channel. As a postdoctoral research fellow at the PNL he will continue to build user-specific models by characterizing the distribution of healthy dMRI datasets across demographic features (e.g. age, gender, IQ). In doing so he hopes to offer a demographics specific analysis to diagnose or identify common features of Schizophrenia and mTBI.