AI @ Roma Tre
The AI program at Roma Tre University comprises a multidisciplinary group of researchers conducting investigations on methods and tools for intelligent system development. The research activity of the AI laboratory embraces both formal approaches and theoretically grounded investigations, exploration and empirical-experimental techniques.
The group is part of the Department of Engineering, which has been selected by the Minister of Education, University and Research (MIUR) as one of the “Department of Excellence” 2018-2022.
In 2017 two faculty members of the laboratory joined the A.I. Task Force promoted by Agency for Digital Italy (AgID), the technical agency of the Presidency of the Italian Council of Ministers. The task force’s focus is identifying AI solutions for public services to improve the relationship between public administrations and citizens.
Since 2018 the AI laboratory is active node of the national Laboratory of Artificial Intelligence and Intelligent Systems (AIIS) of CINI (National Interuniversity Consortium for Informatics).
Four courses that provide an AI specialization to help students master skills such as Machine Learning, Deep Learning, AI Logics, Planning and recent advances in Intelligent systems for Internet.
Investigation of new methodologies to solve specific, practical and present-day problems, with experiments on real-world case studies, with the support of GPU-based computing architecture (multi-GPU NVidia Tesla).
Critical interactions between science and business, within strategic frameworks that include cooperation in education, training and technology transfer to ensure the transition of research results.
Community Engaged Research
Active role in editorial boards of International journal, member of program committees of international conferences, organization of seminars and meetings.
Marta Cialdea Mayer
PhD, Assistant Professor
PhD, Associate Professor
Other scholars, principal collaborators and PhD students
Introduces the fundamental techniques of the various areas of Artificial Intelligence relative to Knowledge Representation and Automatic Reasoning, Machine Learning, Natural Language processing, Computer Vision. Scheduled on 2nd semester of 1st year in the Master degree of Engineering in Computer Science.
A course for describing the problems relative to the study, realization and experimentation of software systems for the Internet, realized by means of Artificial Intelligence techniques. The focus is on the adaptive systems based on user modeling. Scheduled on 1st semester of 2nd year in the Master degree of Engineering in Computer Science.
It provides the foundamental knowledge of classical and some non-classical logics and some of their applications in computer science. Scheduled on 2nd semester of 1st year in the Master degree of Engineering in Computer Science.
It enables students to deepen the main Machine Learning models and methods, such as Regression, Classification, Clustering, Deep Learning, and use them as tools for the development of innovative technologies. Scheduled on 1st semester of 2nd year in the Master degree of Engineering in Computer Science.
- A collective list of free APIs for use in software and web development April 2, 2020
- Dataset search engine powered by Google January 28, 2020
- AI national strategies @ AIIS meeting 6 Dec 2019 Rome, Italy December 6, 2019
- An Approach to Conversational Recommendation of Restaurants (Sardella, N., Biancalana, C., Micarelli, A. and Sansonetti, G.), In Communications in Computer and Information Science, volume 1034, 2019.
- Personalized weight loss strategies by mining activity tracker data (Gasparetti, Fabio, Aiello, Luca Maria and Quercia, Daniele), In User Modeling and User-Adapted Interaction, 2019.
- Cross-domain recommendation for enhancing cultural heritage experience (Sansonetti, G., Gasparetti, F. and Micarelli, A.), In ACM UMAP 2019 Adjunct – Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, 2019.
- Point of interest recommendation based on social and linked open data (Sansonetti, G.), In Personal and Ubiquitous Computing, volume 23, 2019.
- BERT, ELMo, use and infersent sentence encoders: The Panacea for research-paper recommendation? (Mohamed Hassan, H.A., Sansonetti, G., Gasparetti, F., Micarelli, A. and Beel, J.), In Proceedings of ACM RecSys 2019 Late-breaking Results co-located with the 13th ACM Conference on Recommender Systems (RecSys 2019), volume 2431, 2019.
- Evaluating the Efficacy of Traditional Fitness Tracker Recommendations (Gasparetti, Fabio, Aiello, Luca Maria and Quercia, Daniele), In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion, ACM, 2019.
- Exploiting semantics for context-aware itinerary recommendation (Fogli, A. and Sansonetti, G.), In Personal and Ubiquitous Computing, volume 23, 2019.