I believe that Artificial Intelligence is the key to solving the challenges of our time. Equipped with a PhD and two postdocs, I’m looking to use my deep understanding of machine learning to bolster Moonfire’s data-driven thesis.
My PhD and postdocs at the Universities of Siena and Bologna were focused on distributed algorithms for optimisation, machine learning and control. Over the years I’ve published more than 20 academic papers, served as a teaching assistant and worked as a Python developer. Bridging my research and software development interests, I went on to publish a python package for distributed optimisation, which the control community has adopted enthusiastically. In 2020 I joined GlaxoSmithKline as an AI Fellow. The Fellowship enabled academics like myself to do research and get hands-on experience in an industrial setting. During that time, I led projects involving uncertainty estimation for neural networks and the usage of graph neural networks and equivariant architectures to analyse and predict quantum chemical properties of molecular compounds. I’m fascinated that Moonfire is already using similarly advanced machine learning. The opportunity to use machine learning and mathematical rigour to analyse founders and companies and predict their evolution and trajectory over time is an immensely exciting prospect. At Moonfire, you can find me driving our evaluation engine for assessing companies and founders, as well as leading on the large ecosystem analysis models to make predictions about the entire global ecosystem. Otherwise, I’m off somewhere running or playing sports!