Variable selection in statistical modelling is concerned with the question of choosing the most relevant explanatory variables from a potentially large set of candidates. The goal is to achieve a good fit between a model and data without including too many variables. The main objective of this project is to adapt methods from the topological theory of persistent homology in order to develop new variable selection techniques. The proposal combines model description from the point of view of computational algebra with statistical methods such as the Lasso technique and Bayesian model selection. Partners from network modeling, climate modeling and from robust engineering and design will provide data which will be used in case studies to assess the performance of the new methods. Also, the project will scope the use of software for combining topological and statistical algorithms, which will enable researchers to combine the different approaches without having to do extensive programming. Methodological results from this project have the potential to be applied to the analysis of large data sets in areas where the detection of interactions between explanatory variables is crucial.

In any quantum field theory, the S-matrix - which encodes the scattering amplitudes of the theory - is the most fundamental observable. Amplitudes are directly related to cross sections, which are measured at particle accelerators such as the LHC. Hence a thorough study of the S-matrix is of fundamental importance both on a theoretical and experimental side. In recent years, hidden mathematical structures have been unearthed - structures which are completely obscured by standard perturbative methods. These developments have dramatically improved our understanding of the S-matrix and led to the discovery of new symmetries and dualities, which in turn have triggered the development of novel, and highly efficient techniques to compute amplitudes. Remarkably, some of these results are of direct use to the LHC. This project will focus on the most recent and fascinating developments in this rapidly evolving field.

Artificial Intelligence for Supporting Musical Creativity and Engagement in Child-Computer Interaction. This research looks towards artificial intelligence (AI), and asks how can we design digital agents that adaptively provide pedagogical support with awareness of the affective and motivational context? Specifically, this project would evaluate such agents in terms of their capacity to support autodidactic use (child self-learning), and as an adjunct to classroom use, to aid generalist teachers in specialist creative domains (music/coding). The objectives are: To study child interaction in digital music composition, as an example of creativity in context. To evaluate the ability of AI algorithms to find patterns of creativity within interaction data. To inform the design of an Intelligent Tutoring System for digitally supporting creativity. To evaluate children's engagement with Intelligent Tutoring Systems for music composition.

The long-standing question about the emergence of life on Earth has attracted great interest among researchers and the general public for decades. One of the proposed scenarios involves the delivery of biologically important compounds such as amino acids on the primordial Earth by the impact of meteorites on the Earth's surface. In the early 90's, several works reported the discovery of more than seventy amino acids in meteorites, the majority of which have no known terrestrial occurrence. This finding supports the exogenous hypothesis for the origin of life. It is thus currently believed that amino acids may have formed in the interstellar medium, where complex organics, i.e. large carbon-based molecules, have been found. Laboratory experiments of highly energetic processes such as illumination by ultraviolet photons or bombardment by cosmic rays on interstellar ice analogs, have indeed found that these processes are very efficient in the production of large organic molecules and, in particular, in the formation of amino acids. Despite this progress in the past twenty years, the direct detection of amino acids in the interstellar medium remains elusive. Previous works focused on the search of amino acids in regions across the Galaxy where massive stars form. These regions are relatively hot, and are known to show an active chemistry which induces the production of large amounts of complex organics. The spectrum of light observed toward these regions is highly populated by molecular emission lines at millimeter and sub-millimeter wavelengths, resembling a forest of lines. In this highly populated spectra, the brightest line features correspond to the more abundant species in the interstellar medium. This makes the identification of less abundant species such as amino acids challenging. As a consequence, no firm detection of amino acids in the interstellar medium has been reported to date. In this research project, we will use a novel theoretical and observational approach to detect the simplest amino acids, glycine and alanine, in the interstellar medium. This approach considers that the initial stages in the formation of Solar-type systems, characterized by very cold temperatures (about 10 degrees above absolute zero), are better suited for the detection of amino acids. In a first step, the chemistry of glycine and alanine will be characterized theoretically assuming very cold conditions resembling those of young Solar-type systems. This will help us to infer the main chemical routes involved in the destruction and formation of glycine and alanine at cold temperatures. In a second step, we will perform theoretical modelling of the emission spectrum of these species. This will allow us to establish the parts of the spectrum of light where the probability to detect glycine and alanine is higher. Finally, we will perform deep observations of these species by using the unprecedented capabilities of the Atacama Large Millimeter/Sub-millimeter Array (ALMA) located on the Chajnantor plateau in Chile. The increased sensitivity of this facility (by more than a factor of 10 with respect to previous instrumentation) offers the opportunity, for the first time, to accomplish the detection of amino acids in space. This research not only will allow us to fully characterize the pre-biotic chemistry of amino acids, but will allow us to directly link the formation of these complex organics in the interstellar medium with their subsequent delivery onto planetary systems. The detection of amino acids at the early stages in the formation of Solar-type systems will represent a milestone in our understanding of the emergence of life on Earth.

The importance of photosynthesis for the evolution of life can hardly be overemphasised. It represents one of the key innovations that transformed Earth and paved the way for the rise of complex life. Today, the improvement of photosynthesis to enhance crops and the production of compounds of commercial interest has become one of the grand challenges of photosynthesis research. To improve photosynthesis, it is necessary to change photosynthesis. The study of the evolution of photosynthesis is the study of how photosynthesis has changed through time, which has been the focus of my research. The study of the evolution of photosynthesis can provide relevant insight on its potential for change, optimisation, or improvement. For example, my research has shown that in several occasions through geological time, the chemistry of oxygenic photosynthesis was rapidly and radically optimised to match environments with very atypical light conditions such as those found at 200 meter-deep open ocean waters or within stromatolites. This indicated that the process has a level of plasticity and potential for adaptability well beyond what is currently recognised. I want to link my research on the evolution of photosynthesis with Directed Evolution methods to experimentally prove that it is possible to control and purposefully change the chemistry of photosynthesis. Directed Evolution is an extremely versatile method that is used to change the traits or the activity of a given enzyme by exploiting evolution. It can be done simply by subjecting an organism through repeated cycles of selection under the conditions that favour the desired traits, it can be enhanced by turbocharging mutational rates, it can be focused on a single gene of interest, and it can be combined with another method called Ancestral Sequence Reconstruction (ASR). ASR is an evolutionary method commonly used to compute the most likely ancestral state of an enzyme. The ancestral enzyme gene can then be made using commercially available services and used to study the properties of the ancestral enzyme in the test tube. An interesting outcome of ASR is that the ancestral enzymes show superior stability and functional flexibility. These properties have made the combination of ASR and Directed Evolution a powerful biotechnological tool. I currently lead a research programme on the molecular evolution of photosynthesis and this employs ASR to reconstruct the ancestral states of Photosystem II. Photosystems are nature's solar cells and they power life on Earth by converting light into useful chemical energy. They have done so for billions of years. Photosystem II uses light to decompose water into oxygen, protons, and to generate an electric current. This is the hallmark chemical reaction of oxygenic photosynthesis. The photosystems are very complex molecular machines. This complexity means that they evolve very slowly. It is often believed that they exist as "frozen metabolic accidents". A concept that was introduced to imply that these systems have reached a maximum level of optimal performance and therefore have limited evolvability: in other words, it is thought that they cannot be changed in any way that is useful. This view is however contradicted by my own work, which instead suggests the photosystems have tremendous natural adaptability potential. My research group aims to demonstrate that the function of the photosystems can be changed and controlled in any desirable way with the use of Directed Evolution. We will demonstrate that the function of the photosystems can be optimised to any particular condition given an appropriate set of selective pressures. We will provide tools and a molecular blueprint for the control and optimisation of photosystem chemistry for potential future molecular applications.