In October 2015, Marco obtained an Undergraduate Degree, cum Laude, in Ingegneria dell’Informazione at the University of Salento, with a degree thesis in Systems Theory titled: “Principal Component Analysis (PCA) and Structural Properties of Linear Systems”. In January 2018 he obtained the Masters degree, cum Laude, in Computer Engineering at the University of Salento, with a degree thesis in High Performance Computing titled: “Advanced approaches to improve performance of numerical models on new HPC systems”, concerning the analysis and optimization of sequential models, focusing at their uses in parallel contexts.
Since March 2018 he is working with the Advanced Scientific Computing (ASC) division of the CMCC. His research activies involve climate models optimization, in particular the NEMO ocean model (Nucleus for European Modelling of the Ocean). Now he studies sequential analysis and optimization techniques. He also collaborates with the SDM (Scientific Data Management) division of the CMCC for the analysis and the optimization of the Ophidia data analytics framework. Now he is studying the use of Neural Networks as a tool for climatologic and meteorologic forecasts and their use in NEMO, in downscaling models, etc. I know the main framework, architectures and tools for Deep Learning.
His skills include a good knowledge of the high performance computing architectures, optimal knowledge of programming languages for scientific and general purposes and good knowledge of Machine Learning framework, architectures and tools.
He loves cats and technology.
- An Artificial Neural Network-based approach for predicting the COVID-19 daily effective reproduction number Rt in Italy
- MSG-GAN-SD: A Multi-Scale Gradients GAN for Statistical Downscaling of 2-Meter Temperature over the EURO-CORDEX Domain
- A multi-model architecture based on Long-Short Term Memory neural networks for multi-step sea level forecasting