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AI for the study of Environmental Risks (AI4ER)

UKRI Centre for Doctoral Training

Current aligned students

2021 intake

Amandine Debus

PhD project:

Supervisor:  Dr Emily Lines (Dept of Geography)


Joe Fone

PhD project: Machine Learning in Seismic Tomography

Supervisor: Prof Nick Rawlinson (Dept of Earth Sciences)


Amelia Holcomb

PhD project

Supervisor: Prof Srinivasan Keshav

Scott Jeen

PhD project:

Supervisor: Dr Jonathan Cullen (Dept of Engineering)

Scott's a PhD student in the engineering department, researching theoretical and applied reinforcement learning. He works with the US firm Emerson Electric to build RL agents that can control energy-intensive industrial processes efficiently, drawing power from the grid at optimal times to better match intermittent renewable supply with demand. Doing so requires agents that are capable of learning useful polices in low-data regimes, which creates several interesting theoretical challenges that he believes are best mitigated by model-based RL. More generally, he's interested in probabilistic approaches to machine learning, and the role these techniques can play in climate change mitigation

Sam Lewin

PhD project


Jiayu Pan

PhD project: Future-proofing office design: optimising the office space design with experimental data-driven approaches


Supervisor: Dr Ronita Bardhan (Dept of Architecture)

My PhD project aims to study the office design in the post-pandemic era with the analysis and prediction of the use of office space. As the challenge of climate change, the risk of black-swan events like the pandemic and the evolution of information technology are reshaping the way that people use the office, it is essential to rethink what type of office space is desirable and sustainable in the future and figure out how to respond to the rising demand of managing environmental risk and accommodating new working modes through the design of space. The data-driven methods have a large potential to inform evidence-based design decisions. The application of machine learning (ML) approaches assists the analysis of primary data, the prediction of office performance and the optimisation of office layout.

Norbert Toth

PhD project using ML methods for the quantification of electron microscope data from volcanic rocks

Supervisor:  Dr John Maclennan ( Dept of Earth Sciences)

My project aims to develop a more quantitative and more automated method for microscopy analysis of the products of volcanic eruptions. The work will centre around the use of signals generated through electron microscopy in state of the art machine learning systems. This is then hoped to allow for a more intelligent method to study the underlying physics and chemistry of volcanic plumbing systems.

2020 intake

Edgar Cifuentes

Edgar's research aims to develop microclimatic models to understand how forest composition affects local climates in Colombia and how its degradation has repercussions for biodiversity and global warming. He will track the changes in forest cover and microclimates by combining climatic data from field sensors with a diverse range of satellite data. By using AI, he will be able to build statistical models that will enable the discovery of microclimatic patterns and predicting these in the future (or past), and in larger areas. Because Colombia has a diverse set of climates and landscapes, these models will be useful for predicting microclimates at regional and global scale in the tropics. Datasets and models will be the basis for showcasing detailed distributions of tropical species and the quantification and localization of future changes in temperature and moisture. Through this work, it would be possible then to prove the hypothesis that climatic changes in the tropics are predominantly driven by land-use changes (deforestation) and to a lesser extent, by global warming through greenhouse gas emissions.

Jess Fleming

Further details to follow, however Jess's project relates to atmospheric chemistry and will involve the use of portable sensors to measure air quality in indoor and outdoor settings and analysis of the associated data.

Jason Zhe Sun

Jason's research looks at improving estimates of the global distribution of surface ozone (O3) and accordingly improving our estimates of the health impacts of surface O3. This will enable an improved population exposure risk assessment for the associated chronic diseases (like COPD).

His focus so far has been on the analysis of multi-model CMIP6 (1990-2100) global surface O3 simulations. The model evaluation, together with integrated analyses on other assisting data source (like underlying land-surface types, population etc) are being investigated to determine the sources of simulation-observation biases and malposition, so as to provide suggestions on making improvements in further process based modelling. The prediction enhancement using Machine Learning (ML) could provide more credible global O3 concentrations, based on which the all-cause premature death and COPD-related mortality will be estimated with multiple Shared Socioeconomic Pathways (SSPs) into 2100, for public health awareness rising and policy-making suggestions. 

Aaron Wienkers

Aaron is studying the dynamics of ocean fronts using theory and a suite of idealized numerical simulations.  Although Aaron’s work doesn’t involve machine learning directly, he has acquired expertise in theory of ocean and atmospheric circulation and high performance computing.


James Ball

James' PhD will help us discover how resilient tropical rainforest are to anthropogenic climate change. Tropical forests (TFs) store a vast quantity of carbon and can act as a substantial carbon sink, playing an important role in regulating the global climate system. However, the interlinked threats of deforestation and increased likelihood of drought from climate change put their continued existence in doubt. The aim is to apply innovative deep learning methods (2D/3D-CNNs, RNNs) to satellite/geospatial data to develop a system that forecasts the locations of future deforestation events. Through collaboration with an international NGO it will facilitate pre-emptive action that protects valuable ecosystems. To better understand how TFs will respond to future climate change, James will be join a team to conduct novel repeat surveys of an Amazon forest plot with airborne LiDAR and other ground based techniques. He will investigate what drives changes in the forest canopy (climatic conditions, plant phenology etc.), including at the individual/species level, and use satellite data to scale up the findings and understand the implications of a changing climate on the wider region. The results will lead to improved design and parameterization dynamic global vegetation models, and thereby advance understanding of the resilience of TFs to climate change.

Matt Ball

Matt is working on the use of a Zeiss Secondary Ion Mass Spectrometry (SIMS) instrument for Earth and environmental science applications. As part of this work, Matt works extensively with Machine Learning methods for the analysis of hyper-spectral data, which will be of direct relevance to many of the applications worked on by students in the CDT. He is familiar with the "Hyperspy" toolkit that is used extensively by people in this field for the extraction of quantitative data from hyper-spectral images.

Xiaolei Feng

Xiaolei's work involves the prediction of mineral structure from quantum mechanical first principles calculations. She is currently investigating the cycling of volatile elements (H, O, N, C) in the silicate portion of the Earth. Her work involves creation of large data sets and search for low energy structures, with implications for geochemical cycling.

Rachel Furner

Rachel's research focuses on using Machine Learning and data-driven methods to improve the efficiency of climate models. In particular her work focuses on learning from model data, to enable faster algorithms for specific applications, such as finding the equilibrium state of particular scenarios. Future plans include developing and applying these data-driven techniques to modelling of the ocean carbon cycle - a computationally expensive part of a climate simulation, which is also key to understanding and predicting the future climate. Prior to her PhD Rachel completed an undergraduate degree in Mathematics at the University of Oxford, she then worked for 8 years as a researcher at the UK Met Office, developing physically-based ocean models for short term forecasting. Following this she worked coordinating data-focused research institutes, before returning to research to pursue her PhD.

Jonathan Rosser

Jon will apply novel data analysis techniques and machine learning algorithms such as dimensionality reduction, clustering and graphical networks to output produced from the latest group of coupled climate models. Unguided learning techniques in particular offer considerable potential to uncover presently unknown or misunderstood relationships within models. Challenges will include adapting existing algorithms to deal with the very high dimensional nature of the data and its non-stationary, non-isotropic, highly-correlated nature. Jon will contrast the state space parameters between models and work to associate such parameters with future model states (e.g why some models convect while others don’t) and constrain climate projections using the dynamical knowledge uncovered.

Robert Edwin Rouse

Robert is applying Bayesian machine learning methods to provide informative and actionable predictions on the future risk of urban flooding. Urban flooding may be caused by a number of factors including local geology and soil type, urban structural design and drainage capacity, extreme weather, and previous rainfall over daily to seasonal timescales. The challenge here is to intelligently combine the required information in a computationally efficient manner while providing robust estimates of uncertainty as required by decision makers. Robert works closely with industry partners Mott MacDonald who are a world-leading environmental engineering company, focusing on a range of structural projects including urban drainage, dams and reservoirs. Over the course of the PhD Robert will work with their engineers to integrate his new algorithms into Mott MacDonald’s data analysis pipelines.
Before joining Cambridge, Robert completed an MA in Royal College of Art, and an MEng at Imperial College London. He is also a Co-founder and the Chief Technology Officer at Ichthion Limited.

Matthew Shin

Matthew’s research is using ML to understand how we can make more accurate projections of the exposure of people to air pollution in the future using global chemistry climate models. Air pollution and climate change are coupled problems with some very non-linear feedbacks. To understand them we use expensive simulators, models. These models are run at a low resolution and the initial focus has been on using Gaussian Mixture Models (GMMs) to unpick rural and urban air pollutant levels simulated by the global models.

Will Tebbutt

Will Tebbutt holds an M.Phil. in Machine Learning, Speech and Language Techology from the University of Cambridge and an M.Eng. in Engineering Mathematics from the University of Bristol. He previously worked as a researcher in machine learning at Invenia Labs in Cambridge, and is a member of Darwin College. His research interests include Gaussian processes, automating Bayesian inference, the application machine learning to climate science, and probabilistic numerics.

Risa Ueno

Risa is developing state-of-the-art probabilistic machine learning methods to predict future changes in heatwaves and their associated increases for energy demand driven by air conditioning usage. Initially the aim is to focus on megacities, where we have a wealth of relevant climate and non-climate data, before applying these models to more data-sparse cities around the world to improve resilience to future climate extremes. Risa’s working closely with AI engineers at the Alan Turing Institute, and industry partners Max Fordham who are an award-winning architectural engineering firm with a specialism for designing buildings accounting for future environmental change, withstanding weather extremes, and maintaining human comfort through natural or low-energy solutions.

Before joining BAS and the University Risa completed an MSc in Computational statistics and Machine Learning from UCL and an MPhys from Imperial College London. Between her two masters degrees Risa worked as a Technology Consultant for IBM.

Ran Xiao

Ran is a PhD student in the Department of Architecture. In his previous career as an architect, he became fascinated about how design knowledge is acquired through experience. This inspired his research topic, which explores using machine learning to acquire existing spatial knowledge to better inform architects of design decisions. Spatial design is a fundamental issue to sustainability of the built environment. Orientation, shape and size all affects a building’s exposure to the elements. Existing practice, however, relies on modelling, which predicts performances at final stages of design. It means that fundamental changes are rarely made as the spatial design has already been committed. Ran’s research aims to produce a prototype for learning from real world data, coupling spatial design and sustainability performances. It aims to generate sustainable designs at an early stage for similar context, based on a database of best examples of sustainable design.

Yue Zhu

Due to rapid urbanisation and climate change, a large number of cities across the globe are now facing increased flood risks. Recent advances in data and machine learning (ML) methods have provided new opportunities to investigate the effects of spatial-temporal changes in land-cover and land-use (LCLU) on flood risk in and around cities. This research project exploits new machine learning methods to investigate urban flooding risks. The hypothesis underlying the research is that ML methods, which hitherto have rarely been tested in this field, would bring significant new insights because of their abilities to model the diversity and variability of LCLU. This research aims to facilitate more informed decision-making for urban planners, economists, ecologists, and policy-makers.