For decades computer graphics has successfully leveraged stochasticity to enable both expressive volumetric representations of participating media like clouds and efficient Monte Carlo rendering of large scale, complex scenes. In this talk, we’ll explore how these complementary forms of stochasticity (representational and algorithmic) may be applied more generally across computer graphics and vision. In the first part of the talk, I’ll discuss our work on rendering probabilistic representations of 3D geometry, which explains the connection between classical volume rendering and more recent techniques like NeRF. For the second part of the talk, I’ll discuss our work on Monte Carlo simulation where we’ve developed accelerated random walk techniques for physics simulation that are analogous to Monte Carlo rendering for light transport.

Bailey is a PhD candidate in the Computer Science Department at Carnegie Mellon University where he is advised by Ioannis Gkioulekas. He works on theory and core algorithms for stochastic graphics primitives which are leveraged in applications across both computer graphics and vision. 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, the Exploratory Design Group at Apple, and the High-Fidelity Physics Research team at NVIDIA. He is a recipient of the NSF Graduate Research Fellowship, the NVIDIA Graduate Research Fellowship, a Best Paper award at SIGGRAPH 2024, and a Best Student Paper Honorable Mention award at CVPR 2024.