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Claudio Meggio

Student - Faculty of Mathematics and Natural Sciences

$Image of Claudio Meggio$

Email claudm@math.uio.no

Room 822

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Academic interests

My research primarily revolves around both supervised and unsupervised machine learning. Specifically, I am keen on the explainability of these models. The main focus of my PhD research is the application of boosting algorithms to graph-structured data. Currently, I am collaborating with the Hylleraas Centre on a project that studies metal compounds.

Background

I obtained my bachelor’s degree in math at University of Trento. For my master’s degree I moved to Torino where I continued my studies in math, focusing on probability and statistics. During my period of study abroad at University of Waterloo I discovered machine learning and I decided to make it my main field of study. I graduated with a master’s thesis on the COVID-19 outbreak with Professor Andrea Pugliese. After graduation, before landing in Oslo, I joined the SALSA team at University of Bolzano.

Partners

My supervisors are Riccardo De Bin, Johan Pensar and David Balcells.
I am also part of CompSci group where I collaborate with Hannes Kneiding.

Tags: Machine Learning, boosting, statistics and data science, Supervised Learning

Cibinel, Luca; Meggio, Claudio & Zuccato, Lorenzo (2026). Enhancing Quantum Graph Representations via Heuristics and Model-Based Clustering. In Bin, Riccardo De; Salucci, Clara Bertinelli & Hubin, Aliaksandr (Ed.), Proceedings of the 40th International Workshop on Statistical Modelling 28.06 – 03.07.2026, Oslo, Norway. International Workshop on Statistical Modelling. ISSN 9788255325000. Full text in Research Archive Show summary
We propose a novel class of representations for graph-structured data, with the objective of improving feature prediction performance. We focus on graphs with clearly identifiable and interpretable subgraphs, such as transition metal complexes. Our approach is based on the identification of subgraph popu- lations whose citizens exhibit a similar statistical behaviour. Knowledge of these populations is injected in the original graphs in the form of virtual nodes to make global information available at the local level.
Meggio, Claudio; Pensar, Johan; Badia, David Balcells & Bin, Riccardo De (2026). A path-based boosting algorithm for exploring transition metal compounds. Statistical Modelling. ISSN 1471-082X. doi: 10.1177/1471082x251407458. Full text in Research Archive Show summary
Motivated by the analysis of chemical metal compounds and their properties for catalysis, we developed a gradient boosting model that explores graph structures to perform prediction tasks. Taking advantage of the iterative nature of boosting, our novel approach, called PathBoost , explores the graphs to identify the relevant paths and simultaneously fits a prediction model. Advantages of PathBoost include automatic variable selection, as only relevant paths are kept in the model and explainability, as a measure of variable importance is provided. The novel algorithm is applied to the tmQM dataset, where the molecules are represented as graphs with atoms as nodes and bonds as edges. The goal is to predict specific quantum properties of the molecules, such as the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) gap. These properties usually require heavy computational power to be computed, while our model aims to provide comparable results using much fewer resources.

View all works in NVA

Cibinel, Luca; Meggio, Claudio & Zuccato, Lorenzo (2026). Enhancing Quantum Graph Representations via Heuristics and Model-Based Clustering. Full text in Research Archive Show summary
We propose a novel class of representations for graph-structured data, with the objective of improving feature prediction performance. We focus on graphs with clearly identifiable and interpretable subgraphs, such as transition metal complexes. Our approach is based on the identification of subgraph popu- lations whose citizens exhibit a similar statistical behaviour. Knowledge of these populations is injected in the original graphs in the form of virtual nodes to make global information available at the local level.
Meggio, Claudio; Balcells, David; Pensar, Johan & Bin, Riccardo De (2023). Path Boosting, a statistical learning method for chemistry applications. Full text in Research Archive
Meggio, Claudio; Balcells, David; Bin, Riccardo De & Pensar, Johan (2023). Adapting Gradient Booting methods to graph inputs. Full text in Research Archive