Meeting roles in:
IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics
Giacomo has interdisciplinary and international research and work experience.
Since his MSc studies, he has worked at the Japanese Aerospace Exploration Agency in the context of the Hayabusa2 mission, and with the European Space Agency (ESA) Advanced Concepts Team, in the context of space trajectory optimization.

Moreover, he has performed a postgraduate research period in collaboration with ESA/ESOC in uncertainty quantification and propagation in astrodynamics.
Since 2020, he has been involved as a mentor with Frontier Development Laboratory, a public-private partnership between ESA, NASA, Google Cloud, Trillium Technologies, and other partners. Recently, he has worked as a researcher at the University of Oxford on the use of machine learning for spacecraft collision avoidance and orbital propagation.

Currently, he is a PhD researcher at the European Space Agency Advanced Concepts Team and the University of Surrey. His research mostly focuses on astrodynamics as well as applied machine learning techniques (in particular probabilistic and differentiable programming and deep learning) for space science problems.

Some of his latest publications involve dynamical system theory under uncertainties, spacecraft collision avoidance via probabilistic programming, differentiable programming and orbital propagation, thermospheric density modeling, and space weather forecasting, among other topics.