Dr. Stephen Galsworthy is a data driven executive and advisor who loves to create products which address significant challenges. With an analytical background, including a Master’s degree and Ph.D. in Mathematics from Oxford University, he has been leading data science teams since 2011. Currently Stephen is Chief Data Officer at Quby, a leading company offering data driven home services technology and known for creating the in-home display and smart thermostat Toon. In this role, he is responsible for the creation of value from data and Quby’s overall product strategy to enable commodity suppliers such as utilities, banks and insurance companies to play a dominant role in the home services domain.
Quby is the creator and provider of Toon, a leading European smart home platform. We enable Toon users to control and monitor their homes using both an in-home display and app. As a data driven company, we use AI and machine learning to generate actionable insights for our end users. Using the data we collect via our IoT devices we have introduced multiple data driven services, including an energy waste checker and a boiler monitoring service. In this talk, Stephen will describe how AI and machine learning are implemented on the Toon platform, and will show multiple AI use cases relating to the connected home. We'll take a look at how Deep Learning algorithms are used to detect inefficient appliances from electricity meter data and how streaming algorithms allow users to be alerted to anomalies with their heating systems in near real-time. Stephen will share the experiences from the Data Science and Data Engineering teams at Quby with bringing data science algorithms from R&D to production and the lessons learned in offering multiple data driven services to hundreds of thousands of users on a daily basis. Session hashtag: #SAISAI4.
Toon, a leading European smart thermostat and energy display, enables users to control and monitor gas and electricity consumption in their homes. Using the energy data we collect from over 400,000 homes we have developed a new Energy Waste Checker app to give actionable advice to end users to ensure that they do not needlessly waste energy. We identify inefficient electrical appliances and suboptimal uses of central heating and hot water by applying disaggregation algorithms to the raw sensor data. In this talk Stephen will describe our how our novel disaggregation algorithms are implemented in Spark and will show how Toon uses cloud-based big data processing to offer data driven services to hundreds of thousands of users. Session hashtag: #EUds10