Research activity
My research activity focuses on enabling efficient machine learning for data analysis, encompassing three main research areas. The first one is the optimization of distributed big data analysis applications in HPC environments, with the aim of designing and developing methodologies to optimize the performance of big data applications in high-performance systems. The second topic concerns distributed big data analysis at the network edge. Given the massive volume of big data generated by IoT devices at the network edge, my research delves into exploring the integration of conventional cloud solutions with edge environments for distributed data analysis applications within the edge-cloud continuum, in order to enhance latency, privacy preservation, energy efficiency of edge devices, and application scalability in specific application scenarios. Finally, in the third area, I am committed to developing context-aware and real-time adaptive learning models in dynamic environments, such as real-time hashtag recommendation systems in social media or methodologies for test-time adaptation (TTA) on resource-constrained edge devices.
Participation in research projects
ASPIDE: exAScale ProgramIng models for extreme Data procEssing
The ASPIDE project will contribute with the definition of new programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on Exascale systems, which can pave the way for the exploitation of massive parallelism over a simplified model of the system architecture, promoting high performance and efficiency, and offering powerful operations and mechanisms for processing extreme data sources at high speed and/or real-time. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 801091.
INSIDER: INtelligent ServIce Deployment for advanced cloud-Edge integRation
The INSIDER project explores novel solutions to advance the state of the art toward more effective use of hybrid cloud/edge infrastructures for IoT applications. In particular, it aims at defining novel adaptive solutions for finding the most suitable deployment of the services of an application between cloud and edge, so as to meet both functional and non-functional application requirements. INSIDER will provide automatic tools to explore the large number of alternative deployment configurations and identify the best one(s) according to infrastructure constraints and service requirements. The main result of this project is the definition and development of an intelligent framework for analyzing, supporting, and deploying IoT applications for hybrid cloud/edge infrastructures. This project has received funding from the Italian Ministry of University and Research, PRIN 2022 “INSIDER: INtelligent ServIce Deployment for advanced cloud-Edge integRation”, grant n. 2022WWSCRR, CUP H53D23003670006.