EDUCATION:
- Industrial PhD in computer science - ongoing
- Master's degree in Data Science for Management. Vote: 110/110 cum laude. Title of the thesis: "Unsupervised Anomaly Detection for data-driven Financial Audits".
- Bachelor's degree in Business Administration. Vote: 110/110 cum laude. Title of the thesis: "Behind the sparkling luxury world: Customers, Consumption dynamics, growth & Forecasts".
WORK EXPERIENCE:
- 12/2024 - ongoing. Senior Data Scientist for CDSHotels.
- 06/2022 - 10/2022. Research grant at the National Institute of Geophysics & Vulcanology (INGV).
- 03/2022 - 06/2022. Junior software developer assigned to STMicroelectronics, for Xenia Progetti.
- 06/2021 - 02/2022. Internship for drafting the MS degree thesis at STMicroelectronics.
Since mine is an industrial PhD, my research activity is mainly related to the study and development of AI solutions for the operational sector of the company itself, namely revenue management in the hospitality sector. In general, the goal of my research path is to develop innovative tools to be integrated into the Revenue Management system that the company sells to accommodation facilities, with the aim of providing insights and predictions useful both to maximize the turnover of the facilities and to simplify and optimize the work of Revenue Managers, providing them with advanced decision-making support.
- Susanna Saitta, Vito D’Amico and Giovanni Maria Farinella, “Predictions of Hotel Dynamic Pricing for Revenue Management System”, accepted by Communications in Computer and Information Science Journal. It will be published in April 2025.
- Susanna Saitta, Vito D’Amico and Giovanni Maria Farinella, “Dynamic Price Prediction for Revenue Management System in Hospitality Sector”, in Proceedings of the 13th International Conference on Data Science, Technology and Applications (DATA 2024), pp. 218-228. https://www.scitepress.org/Papers/2024/127077/127077.pdf
- Vittorio Minio et al., “Towards a monitoring system of the sea state based on microseism and machine learning”, in Environmental Modelling and Software, Elsevier, vol. 167, 2023. https://www.sciencedirect.com/science/article/pii/S1364815223001676
- Flavio Cannavò et al., “Machine Learning and Microseism as a Tool for Sea wave Monitoring”, presented at EGU 2023. https://meetingorganizer.copernicus.org/EGU23/EGU23-7492.html
