Photo: Museum Fellow Assistant Professor of Computational Astrophysics Contact Info cassandra.hall@uga.edu Personal Website: AstroCass Cassandra Hall is a National Geographic Explorer, astrophysicist and an assistant professor of astrophysics at the University of Georgia. Her research is focused on the formation of exoplanets, artificial intelligence and astrobiology. After her undergraduate degree at the University of Sheffield, she obtained her Ph.D. in astronomy from the University of Edinburgh, and became part of the inaugural class of Winton Exoplanet Fellows, a position she took at the University of Leicester. In 2021, she won the Royal Astronomical Society's Winton Early Career Award for early achievement in astronomy. Her research includes discoveries that have been published in Nature and featured in the New York Times, but she is most proud of her work supporting and mentoring students from under-resourced backgrounds, both in research and in education. She is inspired by big questions, and one in particular: are we alone in the universe? She hopes that her research into how exoplanets form can bring us closer to an answer. Other Affiliations: National Geographic Education Education: PhD in Astronomy, University of Edinburgh. MPhys (HONS) in Physics & Astrophysics, University of Sheffield. Research Research Interests: Thousands of new worlds beyond our own solar system have been discovered, revealing a hugely diverse exoplanetary architecture. Exoplanets form in evolving protoplanetary accretion discs. The conditions in these discs decide the final mass and ultimate orbital configuration of their exoplanetary systems, causing diversity in the exoplanet architecture. As exoplanets form, they leave behind signatures of their formation that can be detected in interferometric mm observations, such as rings and spirals. In order to try and measure the mass of these forming in planets inside their nascent discs, we typically perform around 100 dusty fluid simulations for each observed system, and try to get the mass this way. However, this is incredibly inefficient, inaccurate, and profoundly limits the regions of parameter space we can explore. At UGA, I am building a research group that will move past this outdated model by harnessing the power of machine learning and information extraction. We are developing neural network techniques that are widely applicable, user-friendly, and around 10,000 times more computationally efficient than current approaches to determining exoplanet mass in forming systems.
