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Computer Science and Engineering
About Domain
<p>In this domain, we introduce several competences that enable us to develop intelligent, scalable, and user-centric digital solutions that address complex challenges across different sectors. Some of these skills include:</p>
Human-Computer Interaction
Software Engineering
Data Science
Parallel and Distributed Systems
Challenges
<p>The Computer Science and Engineering scientific domain focuses on addressing the multifaceted challenges of the digital transformation era. We aim to explore the complexity, scalability, and performance demands of modern computer and software systems, with a focus on ensuring their trustworthiness, security, and sustainability. To that end, our research challenges in this domain are:</p>
Main achievements
<p>Our research in Computer Science and Engineering is paving the way for a new generation of digital systems that are more secure, efficient, and tailored to demanding applications. Our achievements range from post-quantum cryptography to optimised data storage and advanced analysis of complex networks. Some of our key accomplishments include:</p>
Post-Quantum Cryptography with Formal Guarantees
<p>We are co-founders of Formosa Crypto, a company dedicated to developing formal verification tools for cryptography, such as EasyCrypt and the Jasmin language. We participated in the formal verification of the three algorithms proposed in the new NIST post-quantum standards and identified correctable vulnerabilities. We also contributed to the verified assembly implementation of the ML-KEM algorithm (Publications: <a href="https://ia.cr/2023/408" target="_blank">here</a>, <a href="https://ia.cr/2023/246" target="_blank">here</a>, and <a href="https://ia.cr/2023/215" target="_blank">here</a>).</p>
Next-Generation Storage Systems
<p>We redefined the operational principles of storage systems to make them more efficient in intensive computing and cloud environments. Our solutions enable complex optimisation, quality of service, and enhance performance in demanding tasks such as training AI models. Computing centres such as TACC, AIST, and MACC have already expressed interest in integrating our advancements. More information <a href="https://dl.acm.org/doi/10.1145/3385896" target="_blank">here</a>, <a href="http://www.usenix.org/conference/fast22" target="_blank">here</a>, <a href="https://ieeexplore.ieee.org/document/10171504" target="_blank">here</a>, and <a href="https://ieeexplore.ieee.org/document/9826112" target="_blank">here</a>.</p>
Pattern Identification in Complex Networks
<p>With over a decade of theoretical and practical research, we developed scalable algorithms for pattern discovery in complex networks. We have created a comprehensive taxonomy and expanded the concepts to networks with directionality, weights, colours, temporal and spatial information, and higher-order interactions such as hypergraphs. More information <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0205497" target="_blank">here</a>, <a href="https://link.springer.com/chapter/10.1007/978-3-030-40943-2_1" target="_blank">here</a>, and <a href="https://link.springer.com/chapter/10.1007/978-3-031-21131-7_44" target="_blank">here</a>.</p>