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AI for the study of Environmental Risks (AI4ER)

UKRI Centre for Doctoral Training

2020 Cohort (MRes)


Matt Allen

Prior to joining the CDT, I completed an undergraduate Master's degree in Electrical and Information Sciences at the University of Cambridge.

The highlights from the course so far have included the opportunity to learn from an extremely diverse group of speakers about interesting research in areas that I had previously not been exposed to as an undergraduate, and the chance to take part in an equally diverse and interesting set of accompanying projects.


Herbie Bradley

I have a Master's degree in machine learning at the University of Warwick and also completed my undergraduate degree at Warwick majoring in Computer Science and Maths.


I have been working with the other CDT students on a project that is attempting to use machine learning (a Long short-term memory (LSTM) model) to determine if natural flood defences are effective or not, using data supplied by the Environment Agency.For me, the highlight from the course so far is the great selection of lectures & seminars on applications of AI to different areas of earth science.


Luke Cullen

Currently in my first year as part of the CDT, I’m looking into projects using probabilistic machine learning to help curb greenhouse gas emissions in support of our net-zero target. I graduated from the University of Leeds in 2017 with a Masters in geophysics, after which I spent 2 years working in the mining and energy industries in Australia, and 1 year as a software developer in London. In my free time, pandemic permitting, I play rugby, surf and compete in triathlons.

Arduin Findeis

I completed an undergraduate degree in Mathematics at the University of Edinburgh and most recently an MPhil in Machine Learning and Machine Intelligence at Cambridge, prior to joining the AI4ER CDT. My iinitial area of interest was flood risk assessment. However, as I come across different application areas in the MRes, I am also considering many other options for his later MRes and PhD research. The highlights from the course so far has been the diversity of interesting problems discussed as part of the MRes-specific lectures.

Katie Green

Prior to joining the CDT, I completed a Physics  degree at Durham University and graduated in 2020.I am currently working on a variety of modules studying machine learning and environmental science.So far her highlights from the course for me have been the environmental risk practicals as they have been an opportunity to practice the techniques that have been studied in a relevant context.


Seb Hickman

Before the AI4ER CDT, I studied Natural Sciences here at Cambridge, eventually specialising in atmospheric chemistry. My research in this area  was at the Centre for Atmospheric Science, looking at using neural networks to predict the concentration of the hydroxyl radical in the troposphere. This work aimed to help to create a global map of the hydroxyl radical. I also have a research interest in flood prediction and prevention, particularly in vulnerable regions worldwide.

Yilin Li

I'm doing my MRes as part of the CDT. I'm interested in the application of artificial intelligence in air quality prediction and plan to do the research related to this field. Before coming to Cambridge, I completed a bachelor in Environmental Engineering jointly provided by Nanjing University of Information Science and Technology and the University of Reading.

Joycelyn Longdon 

I joined the AI4ER CDT in the 2020 Cohort and am looking to ground my research in supporting new modes of engagement with environmental risk, from global marginalised communities. I'm interested in exploring how visualisation, citizen science, mixed-initiative interaction, crowdsourcing, and distributed cognition can be utilised, for example, in ensuring that indigenous knowledge systems are accommodated in algorithms, infrastructure, and representations. I also run ClimateInColour, a platform at the intersection of climate science and social justice making climate conversations more diverse and accessible.

Simon Mathis

I joined the CDT in 2020. Before joining, I completed a Masters in Physics from ETH Zurich, where I worked on simulating quantum field theory on quantum computers, and spent 1 year working as strategy consultant at BCG and as machine learning engineer at a Swiss tech start-up. I’ve not yet picked my PhD topic, but I am excited about remote sensing for assessing carbon sequestration and in using ML to reduce emissions in industrial processes.

 Twitter @SimMat20

Ira Shokar

My research interests are in using machine learning methods for parameterization and statistical downscaling of global circulation models to improve climate model predictions and those relating to extreme weather events. Prior to coming to Cambridge I completed a Bachelor’s degree in Theoretical Physics at University College, London, writing my thesis on ‘Using Domain Adversarial Networks for Model Classification Robustness’. [My website](

Simon Thomas

Before joining the course, I studied Natural Sciences (Physics) at the University of Cambridge.I am currently working on detecting fronts in the Southern Ocean, and the distribution of storm surges created by tropical cyclones in model and observational records.I liked the group project the MRes students have done together (over Zoom and Github) on flood risk at Shipston on Stour. We collaborated well, and I learnt a lot from the other members of the cohort.

Leyu (Natalie) Yao

I started my first year in the CDT in 2020. Before coming to Cambridge, I finished my undergraduate in the US through a 3/2 Engineering Program between Haverford College and the California Institute of Technology (Caltech). I obtained a bachelor’s degree in Math and Physics from Haverford and a bachelor’s degree in Applied and Computational Math (ACM) from Caltech. At Caltech, I worked on a research project on reducing the damage caused by cascading failures in power grids. I am currently interested in studying ocean dynamics through numerical simulations and incorporating artificial intelligence in the process.