Giovanni Aloisio is full professor of Information Processing Systems at the Dept. of Innovation Engineering of the University of Salento, Lecce, Italy, where is leading the HPC laboratory.
At CMCC he is a member of the Governance bodies, director of the Supercomputing Center and is member of the Strategic Council and of the Executive Committee.
He is also the director of the “Scientific Computing and Operations” (SCO) Division at the Euro-Mediterranean Center on Climate Change (CMCC).
His expertise concerns high performance computing, grid & cloud computing and distributed data management. He has been a co-founder of the European Grid Forum (Egrid) which then merged into the Global Grid Forum (GGF), now Open Grid Forum (OGF). He was strongly involved in the EGEE (Enabling Grids for E-science) EU FP5-FP6 grid projects. He is the responsible for CMCC of the EU-FP7 IS-ENES1 and IS-ENES2 (InfraStructure for the European Network for Earth System modelling) projects. He was the responsible for ENES of the EU-FP7 EESI (European Exascale Software Initiative) project and for University of Salento (as PRACE Third Party) of the EU-FP7 EESI 2 project, also chairing in both cases the WCES (Weather, Climate and solid Earth Sciences) European Working Group. He is member of the ENES HPC Task Force. He is one of the key experts of the IESP project (International Exascale Software Project), whose main goal is the definition of the roadmap for a common, open source software infrastructure for scientific computing at exascale.
He is the author of more than 100 papers in referred journals on parallel & grid computing.
- The effect of known and unknown confounders on the relationship between air pollution and Covid-19 mortality in Italy: A sensitivity analysis of an ecological study based on the E-value
- Limits of Compartmental Models and New Opportunities for Machine Learning: A Case Study to Forecast the Second Wave of COVID-19 Hospitalizations in Lombardy, Italy
- Towards HPC and Big Data Analytics Convergence: Design and Experimental Evaluation of a HPDA Framework for eScience at Scale
- A multi-model architecture based on Long-Short Term Memory neural networks for multi-step sea level forecasting
- An Open Source and Low-Cost Internet of Things-enabled Service for Irrigation Management
- Parallel Mining of Correlated Heavy Hitters on Distributed and Shared-Memory Architectures
- Parallel space saving on multi- and many-core processors
- Parallel mining of correlated heavy hitters
- Assessing correlations between short-term exposure to atmospheric pollutants and COVID-19 spread in all Italian territorial areas
- Towards High Performance Data Analytics for Climate Change