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AI for the study of Environmental Risks (AI4ER)

UKRI Centre for Doctoral Training

2019 Cohort

Edward Brown

PhD project - AI for state-of-the-art Space Weather Forecasting

My PhD project will focus on the creation of state-of-the-art artificial intelligence/machine learning techniques to forecast space weather conditions on Earth. Space weather is a potent natural hazard capable of massive economic damage to the satellite industry, power outages and harm to human health. Robust forecasting of this phenomena is of massive importance. Research will be conducted into providing forecasts using solar wind data and solar imaging.

Petr Dolezal

PhD project - Complementarity of Renewables across Super-grids

I'm studying the possibility of using multiple renewable energy sources, distributed spatially across large areas, to complement each other and ultimately reduce the uncertainty of the total renewable energy supply in an electric grid.

These sources, such as solar panels and wind turbines, have their ability to deliver power limited by multiple meteorological variables, primarily cloud cover and wind speed, respectively. If the variability of these sources was independent of each other, we could expect the total supply to be more stable. However, it is often heavily correlated, especially across short distances. I'm studying this correlation both in meteorological data and in climate models, where the distribution will likely shift due to Climate Change.

The goal is to quantify the stability gained by connecting existing grids into a single large super-grid. Another goal is to figure out how to use anti-correlated regions to supplement each other during an abnormally low power supply, instead of fossil fuel sources of electricity. Finally, I analyze what is the necessary grid connectivity for harnessing this level of complementarity.

Marc Girona 

PhD project - A machine learning approach to mapping local scale hydro-meteorological regimes in the Himalayas

I joined the CDT in 2019, after a few years working in the water resources sector. Before that, I completed an MSc in Hydrology and Water Resources Management at Imperial College London, and an integrated Masters in Civil Engineering at the Polytechnic University of Catalonia (BarcelonaTECH). 

My PhD focuses on probabilistic modelling for environmental and climate sciences. I am currently working on improving precipitation and river flow predictions in data-scarce regions, such as the Himalayas, and exploring how this translates into actionable information for better decision making. I am co-supervised by Andrew Orr - British Antarctic Survey and Richard Turner - Machine Learning Group in the Department of Engineering.

Omer Nivron

PhD project - Climate Model Bias Correction – the Unfathomable Neural Network

For my PhD project I plan to focus on the development of a new Bias Correction framework that would match climate model outputs and real-world observations. Additionally, I aim to introduce a new method that would allow us to read aleatoric and epistemic uncertainty estimates associated with the bias correction. Both goals would be applied on the daily mean temperature for latitudes between 0 ̊ and 15 ̊ North and the year 2050.

Raghul Parthipan

PhD project - Probabilistic machine learning, using GANs and Gaussian Processes, to reduce climate model computational costs, improve parameterisations and improve accuracy

Global Climate Models (GCMs) are important for our understanding of Earth's past, present and future climates. They are based on fundamental physical processes, therefore accurate cloud modelling is important, with clouds strongly influencing the transfer of radiant energy and spatial distribution of latent heat in the atmosphere, influencing weather and climate. The representation of clouds is a major source of uncertainty in climate models and the primary reason behind the inter-model variance in climate response. I will be bringing probabilistic machine learning approaches, such as Gaussian Processes and Generative Adversarial Networks, to this problem. These will be used to reduce computational requirements of GCMs, reduce errors through better understanding of the distribution and uncertainty associated with cloud processes, and improve generalisability for future climate scenarios. 

Tudor Suciu

PhD project- AI for Climate related Business Risks

I come from an Environmental Sciences background and studied Geophysics (MSci) at UCL for my undergraduate degree. In 2019, I joined the AI4ER CDT, where the research component of my MRes investigated how to extract weather signals from a novel measurement system from alpine lakes for water scarcity estimations. For my PhD, I am co-supervised by Emily Shuckburgh from Cambridge Zero and Nicolas Lane from the Computer Lab and in my project I am looking at assessing the risks of a changing climate and how to deliver this information in a more actionable way towards businesses and policy makers.

Kenza Tazi

PhD project - Local alpine precipitation predictions from largescale data: a probabilistic machine learning approach

I started my PhD in 2020, after completing my MRes as part of the CDT. I am co-supervised by Scott Hosking from the Artificial Intelligence Lab at the British Antarctic Survey and Richard Turner from the Machine Learning Group in the Department of Engineering. My research focuses on applying probabilistic machine learning methods to predicting alpine precipitation. Before coming to Cambridge, I completed an integrated masters in Physics at Imperial College London. 

Anna Vaughan

PhD project - On-board physics informed learning for severe weather nowcasting

My PhD research focuses on applying recent advances in Bayesian learning and physics-informed neural networks to forecast extreme weather events. Specifically, my interests include short-range forecasting of tornadoes and extreme convection using remote sensing data and exploring the application of physics informed learning in medium-range weather prediction. This work aims to develop a global statistical forecasting model leveraging remote sensing observations capable of out-performing existing operational numerical weather prediction systems. 

Prior to my PhD, I completed my undergraduate studies in pure mathematics and theoretical physics, followed by a MSc in meteorology from the University of Melbourne. My MRes project in the CDT developed a new methodology for downscaling climate model output using Bayesian learning. 

Mala Virdee

PhD project - AI for climate risks in sustainable development

My project focuses on improving local-scale future climate information, tailored to the needs of stakeholders which can support decision-making for climate-related risks in developing countries. Climate models provide robust predictions of global average temperature that are informative for attempts to mitigate against worst-case scenarios of climate change. However, they are less able to give reliable predictions on the spatial and temporal scales necessary to support planning and adaptation to the existing and unavoidable future adverse effects. Downscaling and bias-correction of global general circulation models can help to bridge this gap. In my project I will calculate the predictions on a regional or city-scale, on 5-year and 20-year time horizons to  provide actionable information to local business-owners, decision-makers and other key stakeholders, enabling planning and adaptation to build resilience to future climate extremes.

Michelle Wan

PhD project

My research explores the applications of machine learning in the predictive modelling of outdoor air pollutants and their association with human lung cancer. The first stages of the project consist of investigation into air pollution simulation and the utility of real-world data. This includes the evaluation of modelling approaches such as land-use regression and dispersion models, and the application of machine learning techniques to incorporate real-world data into modelling approaches. My project will explore a pollution/disease case study using simulated pollutant data to predict lung cancer incidence. Air pollution epidemiology studies typically analyse association using odds or hazard ratio statistics, which have limited utility for incidence trend prediction, thus motivating the application of predictive machine learning models. The project takes a long-term view (beyond the project itself) of predictive model development for applications in fields such as healthcare and policy-making.

Before joining the CDT, I completed a BA and MSci in Natural Sciences (Biological) at the University of Cambridge, specialising in Chemistry and Atmospheric Chemistry for my MSci year. During my MRes year with the CDT, I studied the association between NO2 air pollution exposure and human breast cancer incidence in London, using LSTM networks.