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Photo of Dr George De Ath

Dr George De Ath

Research Fellow


Telephone: 01392 724536

Extension: (Streatham) 4536

I am part of Project Bluebird, a prosperity partnership programme between the University of Exeter, NATS and The Alan Turing Institute. I lead the development of novel optimisation-based control algorithms for safe and efficient air traffic control. Following a successful presentation at the British Science Festival, our work was recently featured in the Financial Times.

Previously, I served as a permanent Research Fellow for the Institute for Data Science and Artificial Intelligence (IDSAI) at the University of Exeter. Between 2019 and 2021, I worked as a Postdoctoral Research Fellow on two UKRI-funded projects, RIBA to Reality: Deep Digital Twin to enable Human-Centric Buildings for a Carbon Neutral Future and Rapid Calibration for Operational and Strategic Digital Twins. Prior to this, I obtained my MSci in Computer Science and Mathematics and my Ph.D. in Computer Science, both at the University of Exeter.

Research Interests

My main research interests include the optimisation (calibration) of expensive-to-evaluate problems (models) using Bayesian optimisation, as well as more general single- and multi-objective optimisation tasks, and solving regression/classification problems in machine learning. I have experience in evolutionary optimisation, probabilistic modelling, with a particular focus on Gaussian processes, uncertainty quantification, and general machine learning methods, e.g., neural networks, random forests, support vector machines, etc.

Prospective Students

I am eager to supervise Masters and Ph.D. students, particularly those with interests in (Bayesian) optimisation, machine learning and uncertainty quantification, using both traditional statistical methods (i.e., Gaussian processes) and deep learning-based methods. However, I am also open to supervising students with other interests.

Here is a list of current opportunities:

Current Students

  • (Ph.D.) Ben Carvell: Safety Critical Decision Making under Uncertainty – Machine Learning Approaches for Tactical Air Traffic Control