Talks

Here are a list of my recent talks in reverse chronological order:

SAMBa Summer Conference 2017
Title: Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution
Date: 20th - 21st June 2017
Abstract: Calculating the burden of disease attributed to air pollution requires accurate estimation of population level exposures to pollutants. Although coverage of ground monitoring networks is increasing, these data are insufficient to independently estimate exposures globally.  Information from other sources, such as satellite retrievals, chemical transport models and land use covariates must therefore be used in combination with ground monitoring data. Each of these data sources will have their own biases and uncertainties that may vary over space. Set within a Bayesian hierarchical modelling framework, the recently developed Data Integration Model for Air Quality (DIMAQ) integrates data from multiple sources and allows spatially-varying relationships between ground measurements and other factors that estimate fine particulate matter (PM2.5) concentrations. The outputs of the model are estimated exposures that can be combined with population estimates to produce population-level distributions of exposures for each country. DIMAQ was used to estimate exposures of PM2.5, together with associated measures of uncertainty, on a high-resolution grid (~11 km × 11 km) covering the entire globe for use in the 2016 WHO report ‘Ambient air pollution: A global assessment of exposure and burden of disease’, and in the 2015 and 2016 updates of the Global Burden of Disease. For 2015, 92% of the world’s population lived in areas that exceeded the WHO 10 µg/m3 guideline. Fifty percent of the global population resided in areas with PM2.5 concentrations above the WHO Interim Target 1 (IT-1 of 35 µgm-3); 64% lived in areas exceeding IT-2 (25 µgm-3); and 81% lived in areas exceeding IT-3 (15 µgm-3). Nearly all (86%) of the most extreme concentrations (above 75 µgm-3) were experienced by populations in China, India, Pakistan, and Bangladesh.

High Perfomance Computing (HPC) Conference 2017
Title: Global Estimation of Air Quality
Date: 12th June 2017
Abstract: Calculating the burden of disease attributed to air pollution requires accurate estimation of population level exposures to pollutants. Although coverage of ground monitoring networks is increasing, these data are insufficient to independently estimate exposures globally.  Information from other sources, such as satellite retrievals, chemical transport models and land use covariates must therefore be used in combination with ground monitoring data. Each of these data sources will have their own biases and uncertainties that may vary over space. Set within a Bayesian hierarchical modelling framework, the recently developed Data Integration Model for Air Quality (DIMAQ) integrates data from multiple sources and allows spatially-varying relationships between ground measurements and other factors that estimate fine particulate matter (PM2.5) concentrations. The outputs of the model are estimated exposures that can be combined with population estimates to produce population-level distributions of exposures for each country. DIMAQ was used to estimate exposures of PM2.5, together with associated measures of uncertainty, on a high-resolution grid (~11 km × 11 km) covering the entire globe for use in the 2016 WHO report ‘Ambient air pollution: A global assessment of exposure and burden of disease’, and in the 2015 and 2016 updates of the Global Burden of Disease. For 2015, 92% of the world’s population lived in areas that exceeded the WHO 10 µg/m3 guideline. Fifty percent of the global population resided in areas with PM2.5 concentrations above the WHO Interim Target 1 (IT-1 of 35 µgm-3); 64% lived in areas exceeding IT-2 (25 µgm-3); and 81% lived in areas exceeding IT-3 (15 µgm-3). Nearly all (86%) of the most extreme concentrations (above 75 µgm-3) were experienced by populations in China, India, Pakistan, and Bangladesh.

RSC Conference 2016
Title: Global modelling of air pollution using multiple data sources
Date: 14th - 16th June 2016
Abstract: Air pollution is an important determinant of health and poses a significant threat globally. The World Health Organisation (WHO) are at the forefront of health modelling and policy development worldwide and must ensure that this is based on accurate and convincing evidence. A coherent framework for integrating data from various sources is required that provides accurate and effective analysis and yield exposure estimates with associated uncertainty. These estimates should be consistent with raw data and provide a means for explanation when there are discrepancies. I will explore the current methodology used within WHO to estimate air pollution levels and how changes in framework can significantly improve model predictions. I will also explain how Bayesian melding can be used to match the requirements for air pollution modelling within WHO and the associated challenges that arise from using this technique.

SAMBa ITT4: Health
Title: Techniques for Imputing Data Missing Not at Random
Date: 6th-10th June 2016
As part of SAMBa, students are required to attend workshops called Integrative Think Tanks (ITTs). These events bring academic, industrial, and other external partners to present problems requiring research solutions, in which students are encouraged and trained to form mathematical questions and potential solutions from Industrial problems. SAMBa students are required to present the progress from these workshops throughout the week. Here are some of the talks that I have given during these events:

Numerical Analysis Seminar
Title: The Numerical Analysts Guide to Approximate Bayesian Inference
Date: 8th April 2016
Abstract: Recently, there has been increased focus on performing approximate Bayesian inference, often using Integrated Nested Laplace Approximations (INLA), particularly with large-scale problems. While this may be computationally more attractive alternative to methods such as Markov Chain Monte Carlo, there are still issues that need to be addressed. During this talk I will explain what INLA is, why it's important and some of the interesting challenges in Numerical Linear Algebra that will need addressing in the future.

BUC2 Conference CIMAT México
Title: Global modelling of air pollution using multiple data sources
Date: 24th February 2016
Abstract: Air pollution is an important determinant of health and poses a significant threat globally. The World Health Organisation (WHO) are at the forefront of health modelling and policy development worldwide and must ensure that this is based on accurate and convincing evidence. A coherent framework for integrating data from various sources is required that provides accurate and effective analysis and yield exposure estimates with associated uncertainty. These estimates should be consistent with raw data and provide a means for explanation when there are discrepancies. I will explore the current methodology used within WHO to estimate air pollution levels and how changes in framework can significantly improve model predictions. I will also explain how Bayesian melding can be used to match the requirements for air pollution modelling within WHO and the associated challenges that arise from using this technique.

BUC1 Conference CIMAT México
Title: Global modelling of air pollution using multiple data sources
Date: 11th November 2015
Abstract: Air pollution is an important determinant of health and poses a significant threat globally. The World Health Organisation (WHO) are at the forefront of health modelling and policy development worldwide and must ensure that this is based on accurate and convincing evidence. A coherent framework for integrating data from various sources is required that provides accurate and effective analysis and yield exposure estimates with associated uncertainty. These estimates should be consistent with raw data and provide a means for explanation when there are discrepancies. I will explore the current methodology used within WHO to estimate air pollution levels and how changes in framework can significantly improve model predictions. I will also explain how Bayesian melding can be used to match the requirements for air pollution modelling within WHO and the associated challenges that arise from using this technique.

Bath IMI Launch
Title: How an Industrial Placement Influenced Me
Date: 28th October 2015

SAMBa MRes Transfer Day
Title: Global modelling of air pollution using multiple data sources
Date: 5th November 2015
Abstract: Air pollution is an important determinant of health and poses a significant threat globally. The World Health Organisation (WHO) are at the forefront of health modelling and policy development worldwide and must ensure that this is based on accurate and convincing evidence. A coherent framework for integrating data from various sources is required that provides accurate and effective analysis and yield exposure estimates with associated uncertainty. These estimates should be consistent with raw data and provide a means for explanation when there are discrepancies. I will explore the current methodology used within WHO to estimate air pollution levels and how changes in framework can significantly improve model predictions. I will also explain how Bayesian melding can be used to match the requirements for air pollution modelling within WHO and the associated challenges that arise from using this technique.

SAMBa ITT2: Energy
Title 1: Spatio-Temporal Effects of Electricity Load Forecasting (1)
Title 2: Spatio-Temporal Effects of Electricity Load Forecasting (2)
Date: 1st-5th June 2015
As part of SAMBa, students are required to attend workshops called Integrative Think Tanks (ITTs). These events bring academic, industrial, and other external partners to present problems requiring research solutions, in which students are encouraged and trained to form mathematical questions and potential solutions from Industrial problems. SAMBa students are required to present the progress from these workshops throughout the week.

Student Led Symposium: Semester 2
Title: Quantile Regression estimation and Simplex Method
Date: 11th June 2015
As part of SAMBa, students are required to attend a seminar series called Student-Led Symposium (SLS). Topics in the SLS are steered to relate to upcoming Integrative Think Tanks (ITTs). Academic staff and industrial partners that attend the ITTs will be involved in the symposia series. SAMBa students are required to present at least once a semester in the first year. Students from later years will present developing research to first-year students, including open research problems for discussion.

SAMBa ITT1: Networks
Title 1: Bayesian Networks - QRA in DNV (1)
Title 2: Bayesian Networks - QRA in DNV (2)
Date: 26th-30th January 2015
As part of SAMBa, students are required to attend workshops called Integrative Think Tanks (ITTs). These events bring academic, industrial, and other external partners to present problems requiring research solutions, in which students are encouraged and trained to form mathematical questions and potential solutions from Industrial problems. SAMBa students are required to present the progress from these workshops throughout the week.

Student Led Symposium: Semester 1
Title: Monte Carlo Methods in Bayesian Statistics
Date: 4th December 2014
As part of SAMBa, students are required to attend a seminar series called Student-Led Symposium (SLS). Topics in the SLS are steered to relate to upcoming Integrative Think Tanks (ITTs). Academic staff and industrial partners that attend the ITTs will be involved in the symposia series. SAMBa students are required to present at least once a semester in the first year. Students from later years will present developing research to first-year students, including open research problems for discussion.