Via Marco Biagi 5 – 73100 Lecce, Italy
(+39) 0832 1902411
Gabriele Accarino received his Master’s degree in Computer Engineering with honors from the University of Salento (Italy), where he presented a thesis on the application of Long Short-Term Memory (LSTM) neural networks to forecast sea level rise across different locations in the Mediterranean basin.
In November 2018, he began a PhD in Environmental Sciences at the same university, focusing on the intersection of machine learning and climate science. His doctoral research explored data-driven methods for a range of applications in the weather and climate, including spatial downscaling and the detection of extreme events, with a particular emphasis on wildfires and tropical cyclones. He got also interested in the potential relationship between environmental factors and the COVID-19 spread during the pandemic in 2019 and the link of climate change and armed conflicts.
Following his PhD, Gabriele was awarded a research fellowship at the Department of Engineering for Innovation at the University of Salento, where he worked on the design and development of data-driven ocean emulators using Transformer architectures. During this time, he also served as an adjunct professor, teaching courses in Information Processing Systems and High-Performance Computing.
He later joined the CMCC Foundation as a Junior Scientist in the Advanced Scientific Computing division (now ADIC), where he led the machine learning research unit. At CMCC, he contributed to and co-supervised multiple European research projects and served as a Scientific Officer for the Silvanus project.
At the end of 2024, Gabriele joined Columbia University in New York and the Learning the Earth with Artificial Intelligence and Physics (LEAP) Science and Technology Center (STC) as a Postdoctoral Research Scientist. His current research focuses on the design and development of multi-scale similarity metrics, spatio-temporal verification techniques for climate fields, and benchmarking of data-driven and physics-based models. He has been a CMCC affiliate ever since.
LATEST PUBLICATIONS
- Transferring climate change physical knowledge
- End-to-End Workflows for Climate Science: Integrating HPC Simulations, Big Data Processing, and Machine Learning
- An ensemble machine learning approach for tropical cyclone localization and tracking from ERA5 reanalysis data
- An Artificial Neural Network-based approach for predicting the COVID-19 daily effective reproduction number Rt in Italy
- 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
- MSG-GAN-SD: A Multi-Scale Gradients GAN for Statistical Downscaling of 2-Meter Temperature over the EURO-CORDEX Domain
- 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
- A multi-model architecture based on Long-Short Term Memory neural networks for multi-step sea level forecasting
- Assessing correlations between short-term exposure to atmospheric pollutants and COVID-19 spread in all Italian territorial areas