Rohan is broadly interested in designing algorithms for geometric computing, taking inspiration from fields such as differential geometry, stochastic calculus and control theory. His current research explores how core problems in PDE-based geometric computing can be efficiently and reliably solved via grid-free Monte Carlo methods based on distance queries. In particular, by avoiding the costly and error-prone process of finite element mesh generation entirely, the methods he works on eliminate bottlenecks between the modeling and analysis of 3D shapes. They also share many benefits with Monte Carlo methods from photorealistic rendering: excellent scaling, trivial parallel implementation, view-dependent evaluation, and the ability to work with any geometric representation.
Rohan is a PhD student at Carnegie Mellon University, advised by Keenan Crane. He received his Bachelor’s in Physics and Computer Science at Columbia University. Prior to his PhD, he worked as a core graphics engineer at IrisVR, and has interned at Adobe Research and nTopology since.