Ramit Debnath

Ramit’s research focuses on the distributional justice implications of built environment design and energy demand. He is trying to understand the injustices associated with energy service demand in slum rehabilitation housing of the Global South. He is employing data-driven approaches to inform evidence-based policymaking. Recently, he collaboratively developed a novel methodology for discovering hidden topics in the narratives of energy and climate justice of low-income communities (1). This methodology was called ‘deep-narrative’ analysis that had its basis in social sciences (grounded theory), and machine learning (ML) and AI (topic modelling using Latent Dirichlet Allocation). He is currently expanding its application to discover policy focus points from occupants’ narratives of energy and climate injustices in the slum-built environment of Mumbai (India), Rio de Janeiro (Brazil) and Abuja (Nigeria). 

Lately, Ramit has used a similar approach in exploring India’s policy nudges to contain COVID-19 pandemic (2), and market shifts in global food systems due to the pandemic using ML-driven web-scraping. He is also investigating the effect of lockdowns on residential energy demand in India using ML-based clustering. For the latest update on his research, please visit https://tinyurl.com/y64hmcf4 

twitter: @RamitDebnath

Joseph Lillington

Joseph's research focusses on nuclear waste disposal aiming to better understand the behaviour of waste, were it to be stored deep underground within a geological disposal facility. Half of this research is on predicting radioactive waste glass dissolution behaviour and glass material properties by applying machine learning algorithms to large-scale industrial and lab derived experimental data. The other half of this research is on developing Monte-Carlo models to predict radionuclide transport behaviour in the near-field environment of a geological disposal facility.

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.

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.