Researching is the thing I do. While the field changed a couple of times, I found that the first-principles approach I’ve grown into over time transfers well, as long as the problem is challenging and at the frontier.
I currently work as a researcher at the Joint Research Centre , the European Commission’s science service. The work sits at an intersection I value: serious AI research, and a direct contribution to the European Union project. It spans LLM watermarking, AI-enhanced forensic investigation, and agentic systems. More on the projects page .
I did a PhD in astrophysics at the Università degli Studi di Milano, studying the dynamics of protoplanetary discs around binary and triple star systems. The work was a mix I grew to love: pen and paper first, building analytical intuition about why a disc should precess, misalign, or snap into a polar orbit , and then large-scale numerical simulations to stress-test that intuition, discover what the theory missed, and go back to understand what the numbers were actually saying. I spent a lot of time with Phantom (a widely used SPH code for astrophysical fluid dynamics), running simulations on HPC clusters and chasing the interplay between accretion , alignment , and orbital architecture across hierarchical stellar systems. That cycle, theory, experiment, interpretation, back to theory, is the way I approach most problems nowadays.
SPH simulation of a polar disc in the T CrA hierarchical triple system (Rigliaco, Gratton, Ceppi et al. 2023).
It also made the move to CINECA a natural one, after the PhD. Italy’s main supercomputing centre was a place where HPC was not a tool in service of a specific science, but the subject itself. I worked on infrastructure and contributed to a large-scale seismic imaging software used in industrial settings, solving the elastic wave equation in anisotropic media (a Tectonophysics exam I had taken years earlier turned out not to be wasted after all) at a scale where every design decision has a cost, and where squeezing precision down to fit memory constraints is not a hack but an engineering art (yes, 5-bit floats are a thing, and yes, they matter). When you work alongside engineers who describe mapping a PDE solver onto GPU hardware as “the most beautiful thing in the world, after mom and wife” (L.F., and he meant it), you cannot help but be infected by the same virus.
The move to AI emerged on its own. During my PhD I had been experimenting with machine learning on the side, out of genuine curiosity. When I started studying language modeling more seriously, I found Bengio et al. 2003 , one of the founding papers of neural language models, through a lecture by A. Karpathy. The paper opens by referencing kernel density estimation : the same family of methods that underpins SPH . In a period of transition between worlds, from academia to industry, from astrophysics to HPC to something new, that recognition felt like a welcome. I followed the thread, and I’m still following it today.