I am Ivan Debono, a physicist and data scientist specialising in cosmology and astrophysics, and machine learning. I earned my PhD in 2009 from the University of Paris (Paris 7 – Diderot), with a thesis on cosmological parameter forecasts for weak lensing surveys as part of the DUNE/Euclid team at the French Atomic Energy Commission (CEA). My research interests centre on dark energy, inflationary models, and observations of large-scale cosmic structures.
From 2012 to 2014, I held an International Research Fellowship with the European Space Agency at the Observatory of Paris, where I worked asteroid impact mitigation for the NEOShield mission, and the University of Paris, where I worked on the European Space Agency Euclid mission, investigating dark energy model selection through weak lensing surveys. From 2016 to 2020, I was affiliated with the Centre National de la Recherche Scientifique (CNRS) at the AstroParticle and Cosmology laboratory at University of Paris (Paris Cité), where I had the privilege of co-authoring work with two distinguished physicists: George Smoot, who won the Nobel Prize in Physics in 2006 for his work on the cosmic microwave background (CMB), and Alexei Starobinsky, who won the Kavli Prize in Astrophysics in 2014 for developing the theory of inflation. With them, I explored constraints on inflationary models, including the Starobinsky model of inflation. We showed that CMB observations combined with large-scale-structure observations can decide between inflationary models. We later showed that some classes of inflation models with features are supported by the data. I also contributed research on 21 cm intensity mapping with the Five hundred metre Aperture Spherical Telescope (FAST).
In recent years, I ventured into data science, with a particular focus on machine learning, large language models (LLMs), and artificial intelligenc. At Opus 2, a leading provider of AI-enhanced case management and litigation support solutions for law firms, I apply advanced machine learning techniques to develop AI-driven tools ro improve decision-making in high-stakes disputes. My work in this domain draws upon my prior experience as a cosmologist, enabling me to address the challenges of big data and sophisticated AI applications with rigour and analytical precision.
I have taught general physics at the University of Malta and lectured on technological and scientific innovation at the Edward de Bono Institute for the Design and Development of Thinking.
If you’d like to know more about my work in cosmology or machine learning and AI, feel free to ask.
Follow Ivan Debono on Academia.edu
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