Gabriele Scaffidi Militone

Ph.D. candidate in Ingegneria Informatica E Dei Sistemi , 39^th cycle (2023-2026)
Department of Control and Computer Engineering (DAUIN)

Docente esterno e/o collaboratore didattico
Department of Control and Computer Engineering (DAUIN)

gabriele.scaffidi@polito.it

Profile

PhD

Research topic

Cloud continuum machine learning

Tutors

Research interests

Data science, Computer vision and AI

Parallel and distributed systems, Quantum computing

Biography

As the demand for novel distributed machine learning models operating at the edge continues to grow, so does the call for cloud continuum frameworks and technologies that combine edge and fog computing to support machine learning. In this broad context, it's necessary to explore innovative solutions achieved by combining the benefits of edge-based machine learning models with the cloud continuum scenario.

Teaching

Teachings

Master of Science

Mobile application development. A.A. 2023/24, INGEGNERIA INFORMATICA (COMPUTER ENGINEERING). Collaboratore del corso
Web Applications II. A.A. 2023/24, INGEGNERIA INFORMATICA (COMPUTER ENGINEERING). Collaboratore del corso

Research

Research groups

DBDM - Database and Data Mining Group (DAUIN)

Publications

Latest publications View all publications in Porto@Iris

REGE CAMBRIN, Daniele; SCAFFIDI MILITONE, Gabriele; Colomba, Luca; Malnati, Giovanni; ... (In stampa)
Level Up Your Tutorials: VLMs for Game Tutorials Quality Assessment. In: CV2: Computer Vision for Videogames Workshop at ECCV2024, Milano (Italy), 29/09/2024
Contributo in Atti di Convegno (Proceeding)
Scaffidi Militone, Gabriele; Apiletti, Daniele; Malnati, Giovanni (2024)
Hermes, a low-latency transactional storage for binary data streams from remote devices. In: DATA & KNOWLEDGE ENGINEERING, vol. 153. ISSN 0169-023X
Contributo su Rivista
Militone, Gabriele Scaffidi; Apiletti, Daniele; Malnati, Giovanni (2023)
Combining fault-tolerant persistence and low-latency streaming access to binary data for AI models. In: 2023 IEEE International Conference on Big Data (BigData), Sorrento (IT), 15/12/2023 - 18/12/2023, pp. 3149-3153. ISBN: 979-8-3503-2445-7
Contributo in Atti di Convegno (Proceeding)