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".
What I do
I work in the exciting field of AI4Science, specializing in AI-driven materials discovery. My research encompasses deep learning for material property prediction, representation learning, and inverse materials design using deep generative models. Currently, I am particularly interested in Geometric Graph Neural Networks, Contrastive Learning, and Diffusion Models.
You can find a list of my publications here.
Material property prediction
I develop and utilize ML frameworks to predict chemical properties of atomistic systems, accelerating the discovery of new compounds for applications.
Generative design
I am currently exploring generative models for inverse materials design, with a specific focus on diffusion models for crystal structure prediction (CSP).
Industry Projects
Discovery of new Transparent Conductors using Machine Learning
Oct 2020 — Dec 2024ML-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