DillonGardner

Dillon is a principal member of EnerNOC’s data science team. In this position, he focuses on using the growing set of energy time series data to design innovative experiments, architect machine learning algorithms, and extract new-found business insights. He has worked on projects ranging from understanding how buildings apparent power and real power usage differs to architecting techniques for obtaining real-time electricity baseline estimations. Prior to joining EnerNOC, Dillon completed his Physics PhD at MIT, where he performed neutron and x-ray scattering experiments on novel electronic and magnetic materials. This worked launched his career in developing models based on massive sets of data.