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📍 Cambridge, UK

April 2026 - GraphNeuralRAG Talk at University of Cambridge

On April 9th, I gave a talk at the Computer Lab of the University of Cambridge entitled: Graph Neural RAG: On the Opportunities and Challenges of GNNs for GraphRAG, from Multi-Hop Question Answering to Perturbation Modelling.

I discussed the line of research I worked on in the past months here at the University of Cambridge. In particular, going from past works on GraphRAG and Knowledge Graph Q&A with GNNs, I show that there is a concrete opportunity to leverage GNNs as the de facto standard to empower GraphRAG systems. This derives directly from GNNs being naturally suited to learn on graphs. I show how such a connection is possible, and what the advantages and concrete limitations are that come from this GNN-based approach.

For instance, I also propose how these limitations can be plausibly overcome, and the related Research Opportunities for GNNs, GraphRAG, and Graph Foundation Models communities.

This was my first Talk, and I am honored to have given it at such a prestigious University.

Slides and Video.

March 2026 - 2/2 Accepted Papers at ICLR 2026

Excited to share that I got accepted at the Gen² Workshop @ ICLR 2026!🧬 2/2.

These papers are part of a new line of research I'm pursuing here at Cambridge, sitting at the intersection of generative modeling, retrieval-augmented systems, and single-cell biology.

PT-RAG: Retrieval Augmented Generation for predicting cellular responses to gene perturbations.

We use RAG for a completely different modality, namely Gene Expressions. We introduce the first differentiable RAG framework for predicting cellular responses to gene perturbations. We show that Naive RAG hurts performance while cell-type conditioning, and end-to-end differentiable retrieval leads to statistically significant improvements over classic baselines.

GGE: A Standardized Framework for Evaluating Gene Expression Generative Models.

How do you know if your generative model for single-cell data is actually better? Turns out, you often can't — because "Wasserstein distance" can mean completely different things depending on implementation choices, making cross-paper comparisons essentially meaningless. GGE fixes this with a unified, open-source evaluation framework that makes every choice explicit, so the field can finally compare methods fairly.

September 2025-April 2026 - Visiting PhD student at the University of Cambridge

I am currently a visiting PhD student at Cambridge, actively collaborating with Professor Pietro Liò’s research group. While we are exploring various domains, my primary focus is developing a foundation model for GraphRAG using Graph Neural Networks.

January 2026-March 2026 - Teaching Assistant for the Geometric Deep Learning course

I am working as a TA for the GDL course (L65) at the University of Cambridge, taught by Prof. Pietro Liò and Dr. Petar Veličković. In this role, I formulated project proposals and currently mentoring four students, on topic about efficient GNN design, link prediction, LLMs, and robotics applications.

July 2025 - London Geometry and Machine Learning Summer School

From July 7–11, I had the opportunity to attend LOGML25 Summer School, hosted at the Imperial Colledge of London, a highly selective program focusing on the intersection of mathematics, geometry, topology, and their role in Machine Learning. Coming from an engineering background, I found it particularly enriching to deepen my understanding of mathematical concepts that I can now integrate into my research.

As part of the program, I worked on a project entitled Iterative Reasoning in Graph Neural Networks for Drug Repurposing. This was a valuable chance to apply GNNs to a practical task and, importantly, to step outside my comfort zone by exploring applications in biology and medicine, fields I had not previously worked in. Updates on this project will be shared soon!

The Summer School also offered the opportunity to present my latest preprint, Early-Exit Graph Neural Networks, during the poster session.

June 2025 - International Joint Conference on Neural Networks

From 30 June to 5 July 2025, i had the chance to attend IJCNN25 in Rome, at the Pontificia Università Gregoriana as my first time as an author. Having 3 accepted papers at the conference, i had more than one occasion to present my work in front of an audience. I presented

Link Prediction with Physics-Inspired Graph Neural Networks;
GATSY: Music Artist Similarity with Graph Attention Networks;
IJCNN 2025 Competition: Learning with noisy graph labels;

Beyond presenting, the conference was a great opportunity to discover cutting-edge research and engage in valuable networking with fellow researchers.

June 2025 - Visiting PhD student at the University of Cambridge

Starting 2nd June 2025, i began my period abroad for my Ph.D. I work at the Computer Laboratory of the University of Cambridge under the supervision of Professor Pietro Liò. My research revolves around the intersection of GraphRAG and Graph Neural Networks.

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