The Cloud Alchemist is a cutting-edge software platform, developed as a spin-off from the University of Cagliari. It utilizes advanced (semi-)automatic artificial intelligence models and tools to monitor and optimize costs associated with corporate cloud infrastructures in areas not covered by standard services from cloud computing providers. The spin-off has received various commissions from medium-sized and large companies and its development has been funded by the Autonomous Region of Sardinia, with a grant of €100,000.

As the Coordinator of the "User Modeling" Research Unit at the Department of Mathematics and Computer Science, I oversee a team of over 12 members, including postdoctoral fellows, doctoral students, and research assistants. The unit engages in both national and international collaborations, leading to several publications, scientific tutorials and workshops, special issues, and granted projects. In addition to my research role, I contribute to the "Applied Computer Science and Data Analytics" Bachelor's degree program by serving on various committees (Equal Opportunity, School Orientation, Teaching, and Student Affairs) and giving the "Deep Learning" course.

I contributed to advancing digital education and vocational training in Switzerland within the Machine Learning for Education Lab, led by Prof. Tanja Kaser. The research findings have been published in prestigious conferences, including EDM 2021 (Best Student Paper and Best Presentation Awards), EDM 2022, AIED 2022, and LAK 2023. Additionally, I mentored over ten graduate and doctoral students and played a key role in drafting successful funding proposals, including SCESC, a Flagship project funded by Innosuisse.

Supervised by Prof. Nasir Memon and Dr. Pawel Korus, my research focused on analyzing, developing, and evaluating attacks against biometric systems based on voice recognition, with an emphasis on dictionary attacks using adversarial neural networks. During this period, I got accepted with a paper at INTERSPEECH 2019. This work laid the foundation for subsequent research conducted remotely with the same NYU's members, culminating in the publication in the IEEE Transactions on Information Forensics and Security.

Under the supervision of Prof. Modesto Castrillon-Santana, my research concentrated on collecting multimodal biometric datasets and benchmarking deep learning algorithms and models for biometric recognition using voice and face in human-robot interaction contexts. This work was accepted at ICPRAM 2019 and provided foundation for further research conducted remotely with the same university, whose results were subsequently published by Springer as one of only seven papers selected for extension from ICPRAM 2019.

Within one of the largest industrial technology providers in Catalonia, under the supervision of Dr. Ludovico Boratto and Dr. David Laniado, I contributed to the European DECODE project, focusing on the analysis, development, and evaluation of multimodal data mining and visualization techniques for various use cases, with a particular emphasis on decision support systems for citizens. This research culminated in the publication of a demo paper, which received a honorable mention, at WWW 2018.

I led research on machine learning for indexing, recommendation, identity verification, and opinion mining primarily for educational platforms. I published three large-scale datasets and over seven state-of-the-art models utilizing text, speech, images, and time series. My work was published in more than five top-tier conferences (such as INTERSPEECH 2019 and ECIR 2019) and journals (such as Elsevier's PRLetters and CiHB). I contributed to five deliverables across two EU/EU-MIUR projects and mentored six BSc / MSc students.

I completed my Master’s degree with the maximum grade of 110/110 (cum laude), achieving the highest score (30/30) on all exams, with all but one being awarded cum laude. I earned the degree in just one year and a half  (instead of two). My MSc thesis proposing a new learning dashboard was presented at a peer-reviewed conference. I was recognized as the Best Student of the Faculty of Science.

I completed my Bachelor’s degree with a grade of 110/110 (cum laude), achieving the highest score on all exams, with all but one being awarded cum laude.  I was recognized as the Best Third-Year Student of the Computer Science BSc Programme.