Background
Chris is a Data Science Lead at Schneider Electric — a multinational power electronics, energy storage, and building management company. He leads work in industrial applications of data science for predictive maintenance, intelligent alarm management, and efficient use of devices. He is deeply interested in robust decision-making through stochastic optimization and interpretation of machine learning models. A long-time member of INFORMS, he has authored several refereed papers and been granted multiple patents.
Chris received a Ph.D. in Industrial Engineering from Georgia Tech and a B.S. in Mathematics from William and Mary. Chris teaches Data Exploration for the Data Science & Analytics program. Chris teaches Visual Data Exploration for the Data Science program.