Hi! I am an Argentinian PhD Student at the School of Mathematical and Computer Sciences, Heriot-Watt University. I studied Electronic Engineering at the University of Buenos Aires.
Mathematical imaging is at the core of modern data science, with important applications in medicine, biology, defense, agriculture and environmental sciences. This active research field studies imaging inverse problems involving the estimation of an unobserved true image from measurements that are noisy, incomplete and resolution-limited. This project is related to an increasingly important and particularly challenging class of imaging inverse problems that, in addition to being ill-posed and ill-conditioned, are further complicated by inaccurate and partial knowledge of the observation system and of the properties of the underlying true image (which are essential to regularize the problem and deliver meaningful estimates). These so-called “semi-blind” and “unsupervised” problems are the focus of significant research efforts across a range of scientific communities, particularly Bayesian statistics, signal processing, and applied analysis, which have recently produced important developments in mathematical theory, methods, models and efficient algorithms. This project will focus on new Bayesian computation methodology for this challenging class of imaging inverse problems, with a focus on methods that tightly combine modern high-dimensional stochastic simulation and optimization, and which support advanced Bayesian analyses.