The past century has seen the most spectacular discoveries and theoretical developments in fundamental physics, but despite this success many questions of fundamental physics remain unanswered. In my PhD, I am focusing on string theory and its implications for our understanding of nature.To study string theories and interpret the physics it produces, we already use a heavy machinery of mathematical tools. Data science and machine learning offer a new route to interrogating how the predictions arising from string theories map onto the world around us.
Solar photovoltaic technologies are vital for sustainable renewable energy. If we’re to make photovoltaics as effective as possible, we need advanced nanomaterials that optimally harvest the Sun’s electromagnetic spectrum. To design these materials, we need better understandings of the quantum dynamics and photo-physics at play. Machine learning can help develop these understandings by analysing high-dimensional datasets to reveal novel physics.
Understanding the proteome – the set of proteins that a cell produces – is crucial in understanding how a cell works, and (in the case of a disease-causing organism) developing effective treatments. One important way to understand the proteome is to study where proteins are found within a cell - known as spatial proteomics. Spatial proteomics can use machine learning methods with quantitative proteomic data to determine where proteins are localised within a cell.
The composition of metals found at archeological sites can tell us a lot about the communities that lived in an area. In my research, I analyse metals found at archeological sites in Cyprus to investigate what metals were used by whom, and to understand how craftspeople in these communities worked.