Benjamin Davis
Research Associate
Affiliation: NYU Abu Dhabi
Education: BS Pittsburg State University; PhD University of Arkansas
Research Areas: Structure and Dynamics of Galaxies; Black Hole Mass Scaling Relations; Galaxy Evolution
Benjamin Davis is a Research Associate at New York University Abu Dhabi, who joined as a new CAP³ (now CASS) Fellow in September 2020. Davis completed his undergraduate studies in Mathematics, Physics, and Music at Pittsburg State University in 2008. He earned his doctorate at the University of Arkansas in 2015, under the guidance of his doctoral advisor, Professor Julia Kennefick. After earning his PhD, he remained in Arkansas and taught as a Visiting Assistant Professor of Physics at both the University of Arkansas and Arkansas Tech University. In 2016, Davis relocated to Melbourne, Australia, and began a postdoctoral position at Swinburne University of Technology. While there, he worked with Professor Alister Graham for four years.
Davis's research focuses on using recent developments in artificial intelligence (AI) and machine learning (ML) to solve the most difficult and impactful problems in astrophysics. Astronomy is an observational rather than an experimental science, which makes determining causality very difficult without the possibility of experimentally intervening in the physical mechanics of the Cosmos. Traditionally, astronomers could only hope to discover observational correlations and then test them in numerical simulations. However, because correlations do not imply causation, this approach is inadequate and possibly misleading.
Fortunately, causal discovery has been developed over decades (and bolstered by recent developments in AI/ML) as a transformative tool in science, enabling researchers to look beyond correlation and uncover the fundamental mechanisms driving complex systems. While causal discovery has been extensively applied in other fields, its application to astrophysics is still emerging. Davis has published in astrophysical journals and presented papers at prestigious computer science conferences in an effort to cross-pollinate causal discovery across both fields to create a meaningful synergy.