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Personal profile

Research interests

My interests revolve around how energy systems can be better designed, implemented and used to reduce humanity's impact on the climate. This incorporates a wide span of topics - including engineering, economics and politics, behavioural science and psychology, and environmental sciences - with digitalisation playing an important role across all of these.

My PhD is approaching the challenge by working with an automotive testbed manufacturer - AVL - to develop autonomous anomaly detection. This aims to reduce the number of redundant tests that are run due to both incorrect set up and calibration and, equipment or test unit failure or malfunction. Outputs of my project will include an algorithm informed by machine learning and statistics, a commentary on the impact of human factors on data quality and, an estimation of the cost savings (monetary, energy and carbon) possible by implementing anomaly detection. 

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 7 - Affordable and Clean Energy
  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 11 - Sustainable Cities and Communities
  • SDG 12 - Responsible Consumption and Production
  • SDG 13 - Climate Action

Education/Academic qualification

Advanced Automotive Propulsion Systems, Master of Research, Department of Mechanical Engineering

27 Sept 202123 Sept 2022

Award Date: 1 Dec 2022

Energy Systems and Data Analytics, Master of Science, University College London

28 Sept 202027 Sept 2021

Award Date: 1 Dec 2021

Environmental Sciences, Bachelor of Science, University of Leeds

20 Sept 20173 Jul 2020

Award Date: 3 Jul 2020

Keywords

  • Anomaly detection
  • Energy analysis
  • Mechanical Testing
  • Manufacturing
  • Data Analysis
  • Quality Assurance, Innovation
  • Data quality
  • Qualitative research
  • Machine Learning
  • Quantitative research
  • digitalisation
  • Behavioural change

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