About Me

I'm a postdoctoral researcher in Generative AI at Imperial College London (UK) in the groups of Alexander Ganose and Yingzhen Li. I was previously a Ph.D. student in Computer Science at the University of Liverpool (UK), under the supervision of Yannis Goulermas, Vladimir Gusev, Michael Gaultois, Rahul Savani, Matthew Rosseinsky. I received my MSc degree in Pure and Applied Mathematics from the University of Rome "Tor Vergata".
Currently, my research focuses on generative models for scientific discovery, with current interests in multi-modal LLMs for molecular design, hybrid diffusion-autoregressive paradigms, and broader AI methods for automated characterization and understanding of molecules and materials.

You can find a list of my publications here.

Generative AI

I develop generative AI models, including diffusion, flow matching and autoregressive methods for structured data.

Multi-modal Learning

I'm focusing on models that integrate multiple modalities and representations, such as text, graphs, point clouds and scientific data.

AI for Scientific Discovery

I build AI systems for discovery workflows such as inverse design and hypothesis generation.

Real-world applications

I'm very interested in bridging ML methods to concrete, real-world problems.

Industry Projects

Discovery of new Transparent Conductors using Machine Learning

Oct 2020 — Dec 2024

ML-guided search to accelerate the discovery of new transparent conducting materials, an important class of semiconductors with a wide range of applications.

Funded by NSG Group