Marco Parola

School of Engineering · Department of Information Engineering · Largo Lucio Lazzarino, 1 - 56122 Pisa (Italy) · Building: A

Marco is a Research Fellow at the Department of Information Engineering. He received his B.S. in Computer Engineering in 2019 and his M.S. in Artificial Intelligence and Data Engineering in 2021.

His main research topics involve the study of explainable artificial intelligence and deep learning methods to effectively analyze medical images and design medical decision support tools.

Thesis proposal

Explainable machine learning on medical imaging

Machine learning and deep learning are techniques for recognizing patterns that can be applied to medical images (X-rays, CT scan, photos). However, the black-box nature of these approaches has restricted clinical use, so it is important to introduce methods that make these approaches interpretable or explainable.

Bachelor or master degree
Analysis of Deep learning architecture

In this thesis we address implementations and case studies, identifying best design practices and evaluating modern deep learning architectures such as Convolutional NN (VGG, ResNet, Inception, mask-rcnn, etc.), temporal NN (RNN, LSTM, GRU, Transformers, etc.), generative architecture (Autoencoder, GAN, etc.), Graph NN; providing benchmark performance on datasets from the literature or generated.

Bachelor or master degree
Structural health monitoring

Structural health monitoring aims to detect and identify any deviation from a baseline condition, typically a damage-free baseline, to track relative structural integrity. Starting from signals collected by sensors installed on the building, the main tasks of this domain are damage detection, damage localization and damage quantification, using machine learning and deep lerning techniques. This work can be made in collaboration with the Politecnico di Milano or with Move srl.

Bachelor or master degree