You can download a PDF version of my CV here.
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Thesis: "Improving the Effectiveness of Graph Neural Networks in Practical Scenarios" [Full Text]
Supervisor: Prof. Fabio Vandin
Thesis: "A Deep Learning Model for Personalised Human Activity Recognition." [Full Text]
Advisor: Prof. Fabio Vandin
Thesis: "Algorithms for the determination of node centralities in a graph."
Advisor: Prof. Pietracaprina Andrea Alberto
Working in the AI research arm of MediaTek on both applied and fundamental research projects.
Worked on leveraging multi-modal data for tackling the cold-start problem in recommendation systems. A technical report regarding this project is available on arXiv.
As a recipient of the Helmholtz Visiting Research Grant, awarded by the Helmholtz Information and Data Science Academy, I have performed research at the intersection of Graph Neural Networks and Topological Data Analysis.
Supervisor: Dr. Bastian Rieck.
Continued my research on Graph Neural Networks. In more detail I have worked on the problem of size-generalization, and on topological techniques to capture higher-order structures in graphs.
Supervisor: Professor Pietro Liò.
I worked on neurosymbolic approaches combining Deep Learning and Logical Reasoning. In more detail, the research done at Samsung focused on the development of a logic-based loss function for deep learning models with the goal of injecting commonsense knowledge into scene graph generation models. This work has led to a publication (at AAAI 2023) and a patent.
Supervisors: Dr. Efthymia Tsamoura.
Project: "Machine Learning for Temporal Data".
Supervisor: Professor Fabio Vandin.
The research project focused on the development of novel Deep Learning frameworks for multimodal times series and has led to a publication on the IEEE Sensors Journal.
During my time at Machine Learning Reply I had the chance to work for important clients on machine learning related projects. In particular I contributed to:
I worked in the team responsible for the development of algorithms that aid pathologists in the analysis of medical slides inside the TissueMark application. This implied the creation, training and validation of Deep Learning models and the engineering, processing and analysis of data (which was mainly composed of medical slides and relative metadata).
In more detail, my tasks included:
| Reviewer | Conferences: RECOMB 2020, ISMB 2020, KDD 2020, ICDM 2020, NeurIPS Workshop on Meta-Learning (MetaLearn) 2020-2021, TheWebConf 2021, NeurIPS I Can't Believe It's Not Better! (ICBINB) Workshop 2021. |
| Journals: ACM Transactions on Information Systems, IEEE Sensors Journal. |
Proficient: Python.
Familiar: Java, C, Apache Spark, SQL, PostgreSQL, Objective-c, Swift.
I worked extensively, both in an academic and in a professional environment, with the main Machine Learning and Deep Learning libraries such as TensorFlow, PyTorch, Keras, Pandas, scikit-learn.