Federica works on data-driven inference in the time domain on problems in astrophysics, urban science, as well as other complex systems that can be studied through interdisciplinary data-driven approaches. She is interested in the development and application of machine learning and AI methods to the studies of lightcurves, time series of light, in astronomy to understand stellar evolution in the extragalactic domain, including cosmology and rare explosive transients, in the urban environment at the Urban Observatory to study the sustainability of the electrical grid and sociology of living centers. She has worked on solar system science, supernova science, stellar eruptions and flares. Federica has served as the coordinator of the eight Rubin LSST Science Collaborations since 2017 and as the chair of the Transients and Variable stars SC since 2015.
