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AI for the study of Environmental Risks (AI4ER)

UKRI Centre for Doctoral Training

2021 Cohort 

Grace Beaney Colverd

PhD project title: Modelling the material and emissions impacts of the interventions required to decarbonise the building stock.

Supervisors: Professor Jonathan Cullen (Dept of Engineering), Dr Ronita Bardhan (Dept of Architecture)

PhD Project description: We propose a novel assessment of the UK housing stock for retrofit using multi-task learning on a per-house basis. The goal here is to produce an analysis that is more detailed than top-down approaches but covers much more ground than traditional bottom-up approaches. The output will be a ranked list that can be provided at varying geographic levels, in order to help prioritise and plan retrofit in the UK.

Prior to joining the CDT, I worked in strategy consulting in London, as an Associate Managing Consultant at Mastercard, and before that as a Consultant with Applied Predictive Technologies. My focus was delivering data-driven analytics to Retail, Banking, and Governmental clients. I had a particular interest in financial inclusion and its impact on intersectional financial equality. 

I completed my undergraduate and masters degrees at The Queen’s College, University of Oxford, where I gained an MPhysPhil in Physics and Philosophy. My final thesis covered topics within string theory: properties of Calabi-Yau manifolds encoded within 2-interior point polytopes, and the results can be found published in the Journal of High Energy Physics. I am interested in practical applications of machine learning to protect earth systems, it’s applications to policymaking and popular science communication.

Thomas Hojlund Dodd


PhD project title: Bayesian optimisation led design of biopolymer-based thermoset composites with enhanced recyclability

Supervisors: Professor James Elliott (Dept. of Mat. Sci. & Met.), Professor Rachel Evans (Dept. of Mat. Sci. & Met.), Professor Alexei Lapkin (Dept. of Chem. Eng. & Biotech.)

PhD Project description: Plastic pollution presents a growing threat to the environment, with vast quantities of waste adversely impacting a variety of environmental receptors. This pollution can be categorised as either thermoplastic or thermoset in nature, with the former characterised as remouldable on heating whilst the latter exhibits irreversible curing caused by strong cross-linking between macromolecular chains. Currently, thermoplastics and the microplastics associated with their degradation have been dominantly focussed upon in literature; my PhD project will study thermoset waste's impacts upon the environment and the means of mitigation.

Prior to joining the CDT, I completed an MSc in the study of Energy Systems at the University of Oxford, where I wrote my thesis on the modelling of hybrid renewable energy systems and their integration with hydrogen electrolysers. This followed three years at the University of Manchester where I obtained my BSc in Environmental Science; specialising in the geochemical modelling of interactions between low-temperature fluids and high-strength rock. As part of Cambridge’s AI4ER CDT programme’s 2021 cohort, I finished my first-year MRes with a project on the optimisation of carbon mineralisation in basaltic rock using a sequence of reactive transport model simulation, gradient boosted decision tree emulation, and Bayesian optimisation. Moving from the Department of Earth Sciences to the Department of Materials Science and Metallurgy for my PhD, I currently study the potential for Bayesian optimisation to accelerate the development of sustainable properties in thermoset plastic materials. Current work is split between looking at improving recyclability and attempting to increase the quantities of sustainably derived feedstocks used.

Madeline Lisaius

PhD project title: Adapting methods in self-supervised learning to leverage Earth Observation data for applications in agriculture. 

Supervisors: Professor Srinivasan Keshav (Dept of Computer Science and Technology), Dr Clement Atzberger (Mantle Labs)

PhD Project description: Agriculture is among the most interconnected sectors on the planet and is critical to alleviating poverty.  Given the importance of agricultural systems, threats to agriculture from poor management, climate change and shifting global food systems are among the greatest risks to society and the environment. There is however, incredible potential for improvements in boosting yields, feeding a growing population, sparing land, conserving water, preserving soils, improving environmental health and supporting communities to thrive. Remote sensing data can be used for tasks like identifying crop types and estimating crop yield. The increasing availability of compute and declining cost of satellite remote sensing has made remote sensing increasingly viable for knowing about agriculture across time and space. Despite the huge potential, remote sensing of agriculture has been largely underutilized to date. Within the bounds of my project I aim to evaluate the effectiveness of self-supervised learning for applications specifically to agriculture by the extent to which tested methods can separate the biophysical signals of soil and vegetation in spectral time series. Considerations of the limitations and risks of remote sensing are a fundamental part of this research and its framing as to mitigate the social and environmental risk posed by the use of remote sensing methods.  I will also bring perspectives from feminist geography into my approach to remote sensing to create more equity- and dignity- oriented framings of remote sensing of agriculture. 

Prior to joining the CDT, I was the Lead Data Scientist for topics of remote sensing, food and climate and Tom Ford Fellow at The Rockefeller Foundation. I graduated from Stanford University in 2019 with a B.S. with Honors in Earth Systems and an M.S. in Earth Systems with a focus on remote sensing for applications in macro land systems, especially agriculture. During my time at Stanford, I spent four years collaborating with indigenous women in the Ecuadorian Amazon as a National Geographic Young Explorer understanding working on projects of forest disturbance, women's empowerment through new land management regimes and the acute limitations and opportunities of Earth Observation for community-lead management. I am interested in advancing applications of remote sensing to topics of food, climate and gender as well as broadening critical discussion of Earth Observation and it's relationship to oppression and injustice.

Jonathan Roberts

PhD project title: Large-scale perception and visualisation of environmental data using 'few-shot' learning

Supervisors: Dr Samuel Albanie (Dept of Engineering), Dr Kai Han (University of Hong Kong), Professor Emily Shuckburgh (CST)

PhD Project description: My PhD project focuses on using computer vision and natural language processing techniques to understand satellite imagery. More specifically, we are looking to leverage the broad knowledge of foundation vision-language models to robustly interpret remote sensing data with minimal training. I joined the AI4ER CDT as part of the 2021 cohort. I completed a Master of Physics degree at the University of Warwick, where I undertook research projects on the biophysics of microorganisms, global sustainable energy, and the orbital dynamics of geosynchronous satellites. After graduating, I worked as a Systems Engineer at Lockheed Martin for 3 years, conducting primarily modelling and simulation work across a diverse range of different projects and domains.

Prior to joining the CDT, I completed a Master of Physics degree at the University of Warwick, where I undertook research projects on the biophysics of microorganisms, global sustainable energy, and the orbital dynamics of geosynchronous satellites. After graduating, I worked as a Systems Engineer at Lockheed Martin for 3 years, conducting primarily modelling and simulation work across a diverse range of different projects and domains.

Sofija Stefanović

PhD title: Sensing the water where green tech grows

Supervisors: Professor Alan Blackwell (Dept of Computer Science and Technology), Professor Jennifer Gabrys (Dept of Sociology)

PhD Project description: As a scholar-activist, I cooperate with grassroots groups scrutinising and resisting extractive industries, especially mining for “critical minerals”, to conduct collaborative research and actions. This includes assembling and deploying water monitoring sensors, co-design workshops and collective analysis of information generated through the research. My work is informed by feminist science and technology studies and environmental/climate justice research. My reflections on attempts to combine political engagements and sociotechnical research are a core part of my academic work. 

Prior to joining the CDT, I spent a year doing a PhD at Oxford as part of the Marie Curie Innovative training network focused on applying machine learning to study aerosol-cloud interactions, which I left to focus on more human-centred research. I am interested in remote sensing and data science applications for environmental citizen science and organising in communities disproportionately impacted by climate change, pollution and environmental degradation. I completed my MSc in theoretical physics at Oxford in 2019, and BSc in liberal arts and sciences at Amsterdam University College where I majored in physics. Outside of the CDT, I work with a citizens' initiative I co-founded in Serbia focused on environmental monitoring, advocacy and education.

Sophie Turner

PhD title: Optimising Atmospheric Photolysis Simulations for Climate Models

Supervisors: Professor Alex Archibald (Dept of Chemistry), Dr Luke Abraham (Dept of Chemistry)

PhD Project description: My PhD project is Optimising Atmospheric Photolysis Simulations for Climate Models. Photolysis is one of the most computationally expensive components of the United Kingdom Chemistry and Aerosols model, which is part of the Met Office Unified Model and the Earth System Model. The aim of this project is to reduce the time taken for the photolysis simulation to run. This will be done by emulating the photolysis simulation, using a neural network. I’m doing this project because I enjoy computer science and I want to help improve climate models so that more informed decisions can be made in response to climate change.

Prior to joining the CDT, I studied computer science at University of Plymouth.