Alejandro Toyofusa Komai, Ph.D. is a statistical data scientist at Southern California Edison. He has applied machine learning algorithms to predict asset failure, to help set annual goals, and to provide prioritized scoping of covered conductor to reduce wildfire risk. In his previous role at Edison, he developed the company’s residential solar adoption forecast and the community choice aggregation forecast. After completing his doctorate in economics from UC Irvine in 2015, Alejandro joined Edison directly from graduate school. He also holds a masters in economics from UCLA, a bachelors in economics from UC Irvine, and a bachelors in mathematics from UC Irvine. He really liked Irvine.