Dr Graham Hesketh, as a Data Scientist at Trilateral Research, is involved in the design and development of software and data science tools for public and private clients. He is active in all aspects of data curation and management across the data lifecycle and contributes to project proposals, research, and report writing in data analysis, data visualization, cloud computing, big data and machine learning.
At Trilateral, he has designed and built, among other analytics tools, a machine learning web app that predicts air pollution from noise pollution collected on a smartphone, a deep learning entity resolution tool for database management and a research collaboration network visualization system built on the OpenAIRE database.
Other personal projects include social media analysis with Twitter (Clustering, topic/sentiment analysis), an electoral swing predictor (Gradient Boosted Regression), a probability to vote predictor (Logistic Regression), a house price predictor (Random Forests), and a likely party to vote for predictor (Neural Network).
Graham has authored and co-authored peer-reviewed journal articles, acted as a reviewer for leading journals, presented at conferences across the world, recently hosted the deep learning session at the big data conference STRATA 2018 and has over eight years’ experience in computational research, applied statistics and data analytics.
His background research involved large supercomputer simulations of optical phenomena including long-haul fibre optic communications, nonlinear optical processes and digital signal processing.
He was awarded the EPSRC doctoral prize for research excellence in 2014.
Graham holds a BSc. in Physics with Astrophysics from the University of Kent, an MSc. in Quantum Field Theory from Imperial College London and a PhD in the Computational Physics of Optical Communications from the University of Southampton, where he has also conducted postdoctoral research.