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AI for the study of Environmental Risks (AI4ER)

UKRI Centre for Doctoral Training
 

2019 Cohort

Edward Brown PhD project - tbc
Petr Dolezal PhD project - tbc
Marc Girona PhD project - tbc
Omer Nivron PhD project - tbc
Raghul Parthipan PhD project - tbc
Tudor Suciu PhD project - tbc
Kenza Tazi PhD project - tbc
Anna Vaughan PhD project - tbc
Mala Virdee PhD project - tbc
Michelle Wan PhD project - tbc

Aligned Students

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.

Martin Rogers

Martin's PhD research project involves exploiting satellite technology and machine learning to describe and predict shoreline change for hazard regulation. His main focus is on East Anglia, UK, which is particularly vulnerable to sea level rise, increases in storminess, coastal erosion, and coastal flooding. Critical national infrastructure (including Sizewell’s nuclear power stations and the Bacton gas terminals), population centres close to the coastal zone (> 600,000 in Norfolk and Suffolk) and iconic natural habitats (the Broads, attracting 7 million visitors a year) are under threat. Shoreline change, driven by complex interactions between environmental forcing factors and human shoreline modifications, is a key determinant of total vulnerability: its prediction is imperative for future coastal risk adaptation. This project will train Artificial Neural Networks (ANN) to predict shoreline evolution until 2040. High resolution satellite imagery, databases of sea defences, beach modification, wave and tide gauge data, bathymetry and meteorological fields, combined with land cover, population and infrastructure data will act as inputs.

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.

Rebecca Self

Rebecca's masters title is "Taskforce on Climate-related Financial Disclosures (TCFD) : Using geospatial data at Financial Institutions to analyse the physical climate risks associated with mortgage portfolios in London". Her research will seek to analyse how these data could be used to identify, analyse and report physical climate risks related to mortgages at large UK and Dutch banks. Working closely with risk managers at banks, the aim is to investigate the current application of geospacial data, possible barriers and any unintended consequences.

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.