Machine Learning for Astrophysics: Proceedings of the ML4Astro International Conference 30 May - 1 Jun 2022

About

Filomena Bufano (Ph.D. in Astronomy) has been a research staff scientist at Istituto Nazionale di Astrofisica (INAF) since 2016. Her scientific interests have been mainly focused on the study of massive stars evolution in particular on their final stages. Promoting a multi-wavelength approach in her studies she worked using data from different telescopes/surveys from UV to radio frequencies and has been a member of numerous international collaborations and projects. In view of the approaching era of a deluge of data expected from new ground and space-based facilities she acquired deep skills in the use of machine learning algorithms: since ~2017 she has been engaged in two important European projects i.e. ViaLactea and the ongoing NEANIAS project (sponsor of the conference too). Nowadays she is involved in the preliminary activities of the Square Kilometre Array focussed on the Galactic Plane and in the Early Science Data Analysis phase of two important pathfinder/precursor of SKA: ASKAP and MeerKAT. Eva Sciacca (Ph.D. in Mathematics for Technology) is a Computer Scientist and Information Technology researcher with over a decade of experience working at the Istituto Nazionale di Astrofisica (INAF) since 2012. She has been extensively involved in cutting-edge research activities in the field of big-data visual analytics and machine learning. She has been instrumental in facilitating astrophysical data processing on distributed computing infrastructures with a special focus on High-Performance Computing (HPC) and Cloud Computing. Over the past five years Eva has played a pivotal role in several European-funded projects including VIALACTEA INDIGO-DataCloud AENEAS EOSC-Pilot NEANIAS and SPACE. She has been at the forefront of harnessing the potential of the European Open Science Cloud (EOSC) and the European High-Performance Computing Joint Undertaking (EuroHPC JU) to advance scientific research and she isactively involved in the IT activities of the Square Kilometre Array (SKA) Regional Centres. Francesco Schilliro is a signal processing engineer skilled in algorithm and instrumentation for radio astronomy working at the Istituto Nazionale di Astrofisica (INAF) since 2000. He started working at the Noto VLBI Antenna where he was also involved in the design of radio astronomy antenna control system software and devices. Both experiences were important for his activity as digital engineer and designer for SKA post-processing equipment in particular for the design and prototyping of Tile Processor Module of Low-Frequency Aperture Array component of SKA. His experience as software architect for radio astronomy control system was improved by working as software Architect for the SKA Dish Consortium and in particular for control and monitoring the SKA antennas. Currently he is involved in AI research involving the application of Machine Learning and Deep Learningalgorithms to heterogeneous hardware processing data coming from SKA precursors (Meerkat ASKAP). Recent activity involves quantum computing algorithms and application to astrophysical items.   Simone Riggi (Ph.D. Physics) has been a Research Data Scientist at the Istituto Nazionale di Astrofisica (INAF) since 2012. His work has primarily focused on scientific data analysis and visualization machine learning distributed computing instrumentation simulation monitoring and control and system engineering. He has contributed to large research and technological projects in the fields of radio astronomy high-energy cosmic rays and applied physics such as the Pierre Auger Observatory experiment the Muon Portal project and various European H2020 projects (AENEAS NEANIAS). Currently he is involved in the design and construction phase of the Square Kilometer Array (SKA) telescope and in the Galactic science programs carried out within the ASKAP-EMU and MeerKAT-GPS surveys. In these contexts he is responsible for the monitoring and control system of SKA-Mid antennas and for developing radio source analysis tools using machine learning techniques and multi-wavelength data.