Antonio Scardace
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“Whatever it takes.”
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About me
In July 2025 I graduated with honours from the University of Catania with a Master’s degree in Computer Science with a final grade of 110/110 cum laude. I completed my Bachelor’s degree in March 2023, also with top marks and honours. On both occasions I was nominated for a prestigious departmental merit prize.
I have conducted research activities for over three years focusing on Theoretical Computer Science and Quantum Computing under Professor Simone Faro.
Since September 2024 I have been working on Medical Imaging research with Professor Daniele Ravì, and in October 2025 I started a PhD in Computer Science under his supervision. My research equally combines applied multimodal AI for Alzheimer’s Disease-progression modelling and theoretical studies on detecting and quantifying memorization in generative models trained on medical datasets.
In September 2020 I was selected as a mentee for LeadTheFuture, a Forbes-recognised mentorship programme for outstanding Italian students with an acceptance rate <13%. In May 2023 I also became an Ambassador for the organisation.
LeadTheFuture taught me the value of the right network for personal growth. I am eager to connect with visionary mentors and peers. With a strong devotion to Computer Science and the Public Interest, I aim to merge these fields to achieve meaningful social impact. In particular, I want to apply AI in healthcare to improve lives and make a real social impact.
Publications
A Novel Metric for Detecting Memorization in Generative Models for Brain MRI Synthesis
IEEE/CVF Winter Conference on Applications of Computer Vision 2026 (WACV) • openaccess.thecvf.com
In this paper, we address the risk of patient data leakage in deep generative models for medical imaging, which may memorize sensitive training samples. We propose DeepSSIM, a self-supervised metric designed to detect and quantify memorization across large sets of generated images, providing a scalable solution to safeguard privacy.
Practical Implementation of a Quantum String Matching Algorithm
2024 Workshop on Quantum Search and Information Retrieval (HPDC) • doi.org/10.1145/3660318.3660327
This paper presents a first practical implementation of a quantum circuit tailored to address string matching, particularly focusing on binary strings. By elucidating various algorithmic nuances overlooked in prior theoretical formulations, our solution serves as a conduit between the realms of text processing and quantum computing, fostering cross-disciplinary dialogue and innovation.
The Great Textual Hoax: Boosting Sampled String Matching with Fake Samples
12th International Conference on Fun with Algorithms (FUN) • doi.org/10.4230/LIPIcs.FUN.2024.13
This paper explores character distance sampling, a cutting-edge text sampling technique focusing on sampling distances between characters in a selected alphabet. We propose the introduction of strategically placed fake samples, which significantly reduce the required index space by nearly 50% without compromising the algorithm's correctness. This method also enhances the algorithm’s efficiency under specific conditions.
Towards an Efficient Text Sampling Approach for Exact and Approximate Matching
Prague Stringology Conference 2021 (PSC) • stringology.org/event/2021/p07
In this paper we present some preliminary results obtained in the attempt to extend sampled-string matching to the general case of approximate string matching. Specifically, we introduce a new sampling approach which turns out to be suitable for both exact and approximate matching and evaluate it in the context of a specific case of approximate matching, the order preserving pattern matching problem.