Matt Hobby

Head of Data Science for Analytics, Elsevier-RELX Group

After graduating in Electronic Engineering from the University of York, UK, Matt spent several years working as founding engineer in a startup; developing technology to deliver broadband communications from balloons (10 years before Google X picked up on the idea!).

He subsequently joined the University of Leeds, UK designing and developing instrumentation for meteorological and climate observations where he gained his PhD in developing sensor fusion algorithms to extract turbulence measurements from balloon borne observations. He subsequently worked with the UK Met Office developing sensors for monitoring suspended volcanic ash.

Matt joined Elsevier in 2014 and has built the Analytics Data Science team from the bottom up. His team is focussed on scalable algorithms for extracting structure within large networks, predicting online user behaviour and recommendation algorithms. Their work takes advantage of the latest developments in GPUs and distributed parallel computing.