Alex Taylor

Machine learning in Safety Critical Engineering

Project Summary

In collaboration with Rolls-Royce, I will be researching the applicability of machine learning in safety critical engineering. I will be working on advancing the state of the art in this domain to meet the challenge of mitigating “worst case” performance in systems that must be trusted not to fail, for example in jet engines or in control systems for autonomous vehicles. I plan to develop new AI that will work with and for world leading engineering teams, helping them to make better decisions and improve design processes without sacrificing quality or safety.

I will be working with data scientists in Rolls-Royce’s R2 Data Labs and with experts within the defence business, who can provide world-leading insight into how innovations in machine learning, data science and related technologies can be used in this domain.

As aerospace engineering is a highly regulated and safety critical industry, this project requires that the AI developed is accountable, responsible and transparent. There will be a focus on understanding “why” an AI does what is does, what guarantees can be made of an AIs worst case performance, and how this can be conveyed to regulators.

 

Research Interests

I am generally interested in all things data science and machine learning. I have a particular focus on the transparency and reliability of machine learning.

Background

MSc Data Science, University of Bath

MEng Aerospace Engineering, University of Bristol

One year of experience working in patent law.

Supervisors

Dr Neill Campbell