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+# Ph.D. Thesis
+
+This project contains the LaTeX code of the **Beamer presentation** of the thesis of my Ph.D. defence.
+
+The LaTeX file compiles using PDFLaTeX as backend.
+Make sure to download all the style files (`debug.sty` and `sciencestuff.sty`) and the class `thesis.cls`.
+
+## Abstract
+
+We present topics of (semi-)phenomenological relevance in string theory ranging from particle physics amplitudes and Big Bang-like singularities to the study of state-of-the-art deep learning techniques for string compactifications based on recent advancements in artificial intelligence.
+
+We show the computation of the leading contribution to amplitudes in the presence of non Abelian twist fields in intersecting D-branes scenarios in non factorised tori.
+This is a generalisation to the current literature which mainly covers factorised internal spaces.
+We also study a new method to compute amplitudes in the presence of an arbitrary number of spin fields introducing point-like defects on the string worldsheet.
+The procedure can then be treated as an alternative computation with respect to bosonization and approaches based on the Reggeon vertex.
+We then present an analysis of Big Bang-like cosmological divergences in string theory on time-dependent orbifolds.
+We show that divergences are not due to gravitational feedback but to the lack of an underlying effective field theory.
+We also introduce a new orbifold structure capable of fixing the issue and reinstate a distributional interpretation to field theory amplitudes.
+
+We finally present a new artificial intelligence approach to algebraic geometry and string compactifications.
+We compute the Hodge numbers of Complete Intersection Calabi-Yau 3-folds using deep learning techniques based on computer vision and object recognition techniques.
+We also include a methodological study of machine learning applied to data in string theory: as in most applications machine learning almost never relies on the blind application of algorithms to the data but it requires a careful exploratory analysis and feature engineering.
+We thus show how such an approach can help in improving results by processing the data before utilising them.
+We then show that deep learning the configuration matrix of the manifolds reaches the highest accuracy in the task with smaller networks, less parameters and less data.
+This is a novel approach to the task: differently from previous attempts we focus on using convolutional neural networks capable of reaching higher accuracy on the predictions and ensuring phenomenological relevance to results.
+In fact parameter sharing and concurrent scans of the configuration matrix retain better generalisation properties and adapt better to the task than fully connected networks.