Lecturer in Machine Learning for Smart Buildings and Cities
Bartlett School Env, Energy & Resources
Faculty of the Built Environment
Joined UCL
1st Nov 2014
Research summary
Phil is involved in a variety research projects related to the health implications of decarbonising the built environment. His expertise lies in model development and the analysis of large datasets through the use of cutting edge statistical and machine learning methods with high performance computing. He is interested in applying these techniques across a broad range of cross cutting themes.Phil is the lead developer a microsimulation model in collaboration with researchers at the London School of Hygiene and Tropical Medicine which is used to assess the impacts of air pollution policy on cardiovascular morbidity. He has also developed a metamodelling framework using artificial neural networks based on EnergyPlus simulations which has been used to look at how energy efficiency interventions in homes modify population exposure to overheating and air pollution. Results from EnergyPlus models have been compared to empirical data from the Energy Follow-Up Survey to assess their validity. He has also performed analysis on large scale radon monitoring in homes and the association between indoor radon and home energy efficiency measures. Future work will seek to gain a better understanding of occupancy behaviour and the relationships humans have with the built environment.
Teaching summary
Phil is the module leader of the 'Advanced Building Simulation' module which is an optional module for the Environmental Design and Engineering MSc course and is developing a new 'Machine Learning for Smart Buildings' module for the Smart Buildings and Digital Engineering MSc which begins in 20/21.
Education
Brunel University
Doctorate, Doctor of Philosophy | 2014
University of Leeds
Other higher degree, Master of Physics | 2010
Biography
Phil Symonds joined UCL IEDE in 2014 as a research associate and was appointed as a lecturer in 2020. He is involved in the Wellcome Trust funded project 'Complex Urban Systems for Sustainability and Health' which seeks to engage with stakeholders in several cities to identify healthy decarbonisation pathways. Phil’s expertise include the computational modelling and analysis of built environment data through the use of statistical techniques. Prior to joining UCL IEDE, Phil completed a PhD in Experimental Particle Physics at Brunel University (2010-2014). During his PhD, he was based at the Center for European Nuclear Research (CERN) for 18 months as a member of the Compact Muon Solenoid (CMS) experiment.
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