I'm a Senior Research Scientist in the High Fidelity Physics group at NVIDIA. Before joining NVIDIA, I did my PhD in Computer Science at Carnegie Mellon University, advised by Keenan Crane. I'm broadly interested in developing new algorithms at the intersection of geometry processing, simulation and rendering. 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 algorithms for photorealistic rendering. Checkout my PhD thesis for further details.

I've received an Nvidia Graduate Fellowship, a Carnegie Mellon Graduate Presidential Fellowship, and Best Paper Awards at SIGGRAPH for work on Monte Carlo PDE solvers. I've also received the Symposium on Geometry Processing best software award for the Boundary First Flattening application to quickly and robustly generate UV maps. Previously, I worked at IrisVR as a core graphics engineer, and received a Bachelor’s in Physics and Computer Science from Columbia University. Find my CV here.


Monte Carlo Geometry Processing

Sawhney, Miller

SGP Graduate School Course (2024)

Project Page  |  Talk

Walkin’ Robin: Walk on Stars with Robin Boundary Conditions

Miller*, Sawhney*, Crane, Gkioulekas

ACM Transactions on Graphics (2024)

Best Paper Award

Paper  |  Project Page

Decorrelating ReSTIR Samplers via MCMC Mutations

Sawhney, Lin, Kettunen, Bitterli, Ramamoorthi, Wyman, Pharr

ACM Transactions on Graphics (2023)

Paper  |  Supplemental  |  Video

Walk on Stars: A Grid-Free Monte Carlo Method for PDEs with Neumann Boundary Conditions

Sawhney*, Miller*, Gkioulekas, Crane

ACM Transactions on Graphics (2023)

Paper  |  Code  |  Tutorial  |  Talk

Boundary Value Caching for
Walk on Spheres

Miller*, Sawhney*, Crane, Gkioulekas

ACM Transactions on Graphics (2023)

Paper  |  Code  |  Talk

Monte Carlo Geometry Processing: A Grid-Free Approach to PDE-Based Methods on Volumetric Domains

Sawhney, Crane

ACM Transactions on Graphics (2020)

Paper  |  Project Page  |  Talk


Boundary First Flattening

Quick and robust UV mapping

Project Page


3D geometry processing on the web

Project Page


Sparse & dense matrix routines on the web

Project Page


Multi Agent Reinforcement Learning

Deep Reinforcement Learning agents playing tag

Report  |  Code

Medial Axis Transform

Undergrad. research project on computing medial axis

Report  |  Code