Antonio Scardace
|
“Stay hungry, stay foolish.”
SCROLL DOWN
SWIPE UP
About me
I’m 24 years old. In July 2025, I graduated with honours in a Master’s degree in Computer Science from the University of Catania, earning a final grade of 110/110 with honors. I had previously completed my Bachelor’s degree in March 2023, also with top grade and honours. On both occasions, I was nominated for a prestigious merit prize, recognizing exceptional students in my department.
I have collaborated closely with my Algorithms professor for over three years.
In the past year, I have also been conducting research in Medical Imaging with another professor.
In September 2020, I was also selected as a mentee for LeadTheFuture, a Forbes-awarded mentorship organization for talented Italian students with <13% acceptance rate. Recently, in May 2023, I was honoured to become an Ambassador for the organization.
LeadTheFuture has shown me the incredible impact of surrounding myself with the right people on my 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 am fascinated by the application of AI in Healthcare to improve lives, and beyond innovation, I want to help Italy counteract brain drain.
Publications
Practical Implementation of a Quantum String Matching Algorithm
Association for Computing Machinery (HPDC - QUASAR 24) • doi.org/10.1145/3660318.3660327
This article 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 2024) • 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 • 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.