Antonio Scardace
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“Stay hungry, stay foolish.”
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About me
I’m 23 years old, pursuing a Master’s degree in Computer Science at the University of Catania, with a GPA of 29.8 / 30. In March 2023, I graduated with honours in a Bachelor’s degree in Computer Science from the same university, earning a final grade of 110/110 with honors. I was also nominated for the prestigious “Archimede” Prize, recognizing exceptional students in my department.
I have been closely collaborating with my Algorithms professor for over three years. Recently, I began working with another faculty member on AI applications in Healthcare.
Since October 2023, I have also been a member of the ITACPC Technical-Scientific Committee.
In September 2020, I was selected as a mentee for LeadTheFuture, a Forbes-recognized mentorship organization for talented Italian students with an acceptance rate below 13%. In May 2023, I was honoured to become an Ambassador for the organization.
Thanks to my experience in LeadTheFuture, I have grasped the transformative power of surrounding myself with the right people for personal and professional growth. For this reason, I am eager to connect with mentors and open-minded peers to help shape the future of Italy together. I am passionate about both Computer Science and Politics and aspire to combine these interests to create meaningful societal impact.
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.