Decorrelating ReSTIR Samplers via MCMC Mutations
Sawhney, Lin, Kettunen, Bitterli, Ramamoorthi, Wyman, Pharr
ACM Transactions on Graphics (2023)
Paper | Supplemental | Video
About
I’m a Computer Science PhD student at Carnegie Mellon University, advised by Keenan Crane. I'm broadly interested in designing new algorithms for geometric computing, taking inspiration from fields such as differential geometry, stochastic calculus and optimal control. My current research explores how core problems in PDE-based geometric computing can be efficiently and reliably solved via grid-free Monte Carlo methods without any volumetric mesh generation, taking inspiration from photorealistic rendering.
I was awarded the Nvidia Graduate Fellowship and the Carnegie Mellon Graduate Presidential Fellowship for my work. Previously, I worked at IrisVR, Inc. as a core graphics engineer and received my Bachelor’s in Physics and Computer Science from Columbia University. Find my CV here.
Recent Work
Walk on Stars: Grid-Free Monte Carlo for Neumann Boundary Conditions
Sawhney*, Miller*, Gkioulekas†, Crane†
ACM Transactions on Graphics (2023)
Boundary Value Caching for Walk on Spheres
Miller*, Sawhney*, Crane†, Gkioulekas†
ACM Transactions on Graphics (2023)
Grid-Free Monte Carlo for PDEs with Spatially Varying Coefficients
Sawhney*, Seyb*, Jarosz†, Crane†
ACM Transactions on Graphics (2022)
Best Paper (Honorable Mention)
Paper | Supplemental | Code | Talk
Monte Carlo Geometry Processing
Sawhney, Crane
ACM Transactions on Graphics (2020)
Paper | Project Page | Talk
Boundary First Flattening
Sawhney, Crane
ACM Transactions on Graphics (2018)
SGP Best Software Award (2019)
Paper | Project Page | Talk | Web Demo
FCPW
Header only C++ library for fast vectorized closest point queries
geometry-processing-js
Fast & flexible framework for 3D geometry processing on the web
linear-algebra-js
Sparse & dense matrix routines with Cholesky, LU & QR support on the web
