Bailey's research aims to develop practical Monte Carlo methods for physical simulation that match the scalability and robustness of Monte Carlo rendering algorithms. He specifically focuses on designing accelerated random walk methods that are amenable to differentiation and leverage volumetric models to handle intractably complex geometry. He sees these methods as a powerful building block for a diverse range of applications, from optimizing the design of thermal management systems to performing high resolution electrical impedance tomography.
Bailey is a fourth year PhD student at Carnegie Mellon University where he is advised by Ioannis Gkioulekas. He received his Bachelors in Mathematics and Computer Science from Dartmouth College in 2018 where he had the privilege of working with Wojciech Jarosz. During his PhD Bailey has interned with Adobe research and the Exploratory Design Group at Apple. He previously was awarded the NSF Graduate Research Fellowship.