• 2019 - present, Reader, University of Manchester,School of Earth and Environmental Science.
• 2018 - present, Fellow of the Alan Turing Data Science Institute.
• 2017 - Senior Lecturer in Centre for Atmospheric Science, University of Manchester, School of Earth and Environmental Science
• 2013 - 2017 Senior Research Fellow in Centre for Atmospheric Science/ University of Manchester funded by the UK National Centre for Atmospheric Science (NCAS)
• 2009 - 2013: Research Fellow, University of Manchester and Research scientist NCAS-Composition
• 2006 - 2009: PDRA, NCAS-Composition - fundamental understanding, observation and prediction of atmospheric composition, including air quality;
• 2005 - 2006: PDRA, Distributed Institute for Atmospheric Composition (DIAC) 
• 2001-2005:Phd, UMIST. 'Modelling the hygroscopic properties of atmospheric aerosol'.
  
• 1998-2001: Physics Degree 1st Class, UMIST (University of Manchester Institute of Science and Technology)

My research interests focus on building computational models of atmospheric aerosol particles for use in interpretation of measured properties and as sub models for incorporation into climate change models. This broad classification masks a hierarchy of models and techniques with greatly varying complexity and range of applicability.  In addition, the research area is highly multi-disciplinary, covering: Physics, Chemistry, Numerical methods and Computational Science.

Aerosol particles are ubiquitous components in the atmosphere. Ranging from a few nanometres to microns in size, they are comprised of potentially thousands of chemical components, their size and composition key determinants in their environmental impacts. They remain one of the most uncertain contributors to climate change:directly influencing the radiative flux by scattering/absorbing radiation and indirectly through their ability to act as cloud condensation nuclei (without aerosol particles there would be no clouds!).

On a broader scale, air pollution and climate change are two key socio-environmental drivers that represent some of the biggest multidisciplinary challenges in science, society and the economy today. The need to understand the chemical and physical processes in the atmosphere that dictate the impacts of both has created a wide range of experimental platforms over the past two decades. However, whilst these facilities persistently identify and hypothesise new processes and compounds deemed important to improve our understanding of change, the research community is now struggling to use the data and subsequent information in a truly meaningful way. 

In addition to the above, my work includes evaluating how machine learning might mitigate existing 'complexity' bottlenecks in atmospheric modelling, experimental data analysis and impact assessment. This includes, for example, replacing parameterised or iterative models of key atmospheric processes, traditionally solved using stiff ODE methods, using surrogate models. This also includes extracting new information from existing instrumentation through a wide range of both supervised and unsupervised methods. Alongside this, he will collaborate on methods that combine both air pollution data and human symptomatic responses.

• Atmospheric Science