Title: Learning from simulations using ML/AI tools
Presenter: Viviana Acquaviva (associate professor of physics at the City University of New York)

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Zoom link: https://us06web.zoom.us/j/92063302735
Venue: University of Texas, PMA 15.216B

Abstract: Recent developments in machine learning and AI have given us a new set of tools to answer science questions, while creating new challenges in terms of trust and interpretability. My research focuses on the process of learning from simulations using these tools. I will show a few examples from my Astrophysics work, on validating cosmological simulations and formulating hypotheses for the physical model that drives galaxy evolution processes. I will then move on to current research in climate science, where we are developing custom metrics to assess similarity in climate models outputs, and using representation learning to reconstruct full spatiotemporal fields from sparse and biased ocean data. I will conclude with some lessons learned in applying AI across disciplines, and some considerations and open questions on how AI is changing the way we do science.

Speaker Bio: An associate professor of physics at the City University of New York, Dr. Acquaviva specializes in applying machine learning techniques to cosmological data, with a focus on galaxy evolution and spectral energy distribution modeling. She has recently transitioned to working on AI in Climate Science and thus also provides a broader scientific perspective.