Viviana Acquaviva is an astrophysicist using data science techniques to study the universe. She received a master’s degree in physics from the University of Pisa and a Ph. D. in astrophysics from the International School of Advanced Studies in Trieste. She held postdoctoral positions at Princeton University and Rutgers University before joining the faculty at the City University of New York (CUNY) in 2012. Currently, she is a professor in the Physics Department at the New York City College of Technology (City Tech) and at the CUNY Graduate Center. Acquaviva is also a long-term visiting scholar at the Flatiron Institute’s Center for Computational Astrophysics.
Acquaviva is passionate about supporting women and other minorities in the pursuit of STEM careers, and has been a research mentor for over 25 undergraduate and graduate students. She has been widely recognized as a role model for women in science; among other awards, she was named one of the 50 women who are “Making the History of Computer Science” by Wired Italy in 2020, and one of the 50 most influential Italian women in Tech by InspiringFifty in 2018.
Acquaviva is enthusiastic about interdisciplinary research and cross-pollination between industry and academia, and she was a core proposer of CUNY’s new major in applied computational pPhysics, for which she developed the first course called “Machine Learning for Physics and Astronomy. She has written a textbook of the same name, which will be published by Princeton University Press in 2023.
Lastly, she is the proud mom of six-year-old Clara, who recently attended her first scientific conference , and promptly asked the audience why they were all listening to her mom.
From galaxy evolution to climate models: a data-driven journey
Modeling the Earth is crucial to understanding climate variability and climate change, allocating resources for adaptation, and formulating policies that affect our lives. As a Simons Foundation Pivot Fellow, Viviana Acquaviva’s goal is to bring the numerical modeling, statistics, and machine learning skills that she currently uses in astrophysics to develop better models of the Earth. Better models mean more accurate predictions of future Earth’s conditions, improved extrapolation in data-poor regimes and regions, faster emulation in data-rich but computationally intensive regimes, validation of numerical approximations, uncertainty quantification, and dimensionality reduction to foster physical understanding. At the end of her fellowship, she hopes to understand the different components of a climate model (cryosphere, atmosphere, ocean, land) and how they interact with each other in order to select her own research questions to pursue.