Dillon Gardner
ArchConf
San Diego · April 4 - 7, 2016
Data Scientist at EnerNOC
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.
Presentations
Big Data Workshop: A Crash Course in Machine Learning For Architects Part I
Humanity has dramatically increased it’s ability to generate and retain astronomically large amounts of data in the last decade. Sooner or later you probably will be working on a project that will have a significant “Data Science” component to it. This is the first of a two-part introduction using machine learning techniques to solve data science questions using the R programming language.