Viviana is an Astrophysicist with a strong interest in Data Science. She is a Professor of Physics in the City University of New York and a Visiting Scholar at the Center for Computational Astrophysics of the Flatiron Institute. She wrote a textbook on Machine Learning for Physics and Astronomy, which will be published by Princeton University Press in 2023. She has been recognized widely as a role model for women in science. In 2021, she was named one of 13 "Tecnovisionarie" Italian Women in AI by Women&Tech. In 2020, Wired (Italy) listed her as one of 50 women who are doing the history of Computer Science. In 2018, she was named one of Italy's 50 most influential Women in Tech by InspiringFifty.

Željko Ivezić is a professor of astronomy at the University of Washington. Željko's scientific interests are in detection, analysis and interpretation of electromagnetic radiation from astronomical sources. Of relevance to VOSS 2023, he co-authored a book on Statistics, Data Mining and Machine Learning in Astronomy . His current obsession is the Rubin Observatory's Legacy Survey of Space and Time (LSST), for which he was the Project Scientist and now serves as the Director of Construction. In many ways similar to SDSS, which provided to us the first large digital color snapshot of the faint optical sky, LSST will deliver a digital color movie of the night sky by collecting 100,000,000 GB (100 PB) of astronomical imaging data for about 40 billion stars and galaxies.

Studies galaxy evolution and the impact of accreting supermassive black holes of their hosts. She has extensive experience working with large public datasets (e.g., SDSS, 2MASS, WISE, IRAS, and more). In her research, she combines dedicated observations obtained throughout the electromagnetic spectrum (e.g., XMM-Newton, Keck, VLT, ALMA, and NOEMA) with photoionization and dynamical modeling. She has significant experience in developing and applying Machine Learning algorithms to large astronomical datasets and has written a well-accepted practical review on Machine Learning in astronomy. She will be starting the Carnegie-Princeton fellowship in Carnegie in September 2022.

MHC is a Maitre de Conferences (eq. Associate Professor) at the Paris Observatory and the University of Paris-Cité (France) since 2010. Since 2020, he is also Adjunct Professor at the University of California Santa Cruz. In 2018, he obtained a competitive award delivered by the Spanish Government which he is using now to spend time at the Instituto de Astrofísica de Canarias (IAC). He is an expert in galaxy formation and evolution - with main focus on galaxy statistical structural evolution using large surveys - and one of the pioneers in the use of machine learning for astrophysics. He currently leads the Machine Learning group at the IAC whose main activity revolves around the use of artificial intelligence to address the physics of galaxy formation. He is pushing the use of machine learning beyond the obvious classification tasks to explore how data-driven approaches can be used to reach an improved understanding of the physical processes governing galaxy formation.

Francisco (Paco) Villaescusa-Navarro is a research scientist at the Flatiron Institute in New York City. He did his PhD at the University of Valencia in Spain. He then held postdoctoral positions at the Astronomical Observatory of Trieste, Italy, and the Center for Computational Astrophysics in New York before becoming an associate research scholar at Princeton University, where he holds a visiting research scholar position. Paco is the main architect of the Quijote simulations, the largest suite of cosmological N-body simulations ever run. He is also part of the CAMELS core team that designed and ran the largest set of state-of-the-art hydrodynamic simulations to-date. Paco combines the output of numerical simulations with machine learning methods to develop theoretical models to extract the maximum amount of information from cosmological surveys in order to unveil the Universe’s mysteries.