He primarily works on machine learning for healthcare and infrastructure development. A recurring theme across his work is the use of nonparametric prediction methods in solving temporal or spatial forecasting problems. Since these methods inform interventions that can be costly and affect people’s well-being, ensuring that predictions are interpretable is essential. George obtained his PhD in 2015 from the Electrical Engineering and Computer Science department at MIT, where he received the George M. Sprowls award for best PhD thesis in Computer Science. He previously completed his BS at UC Berkeley in 2010, dual majoring in Electrical Engineering and Computer Sciences, and Engineering Mathematics and Statistics.