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 control theory. 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.

My work is supported by the Nvidia Graduate Fellowship and the Carnegie Mellon Graduate Presidential Fellowship, as well as generous support from nTopology and Disney. 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

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


Header only C++ library for fast vectorized closest point queries

Project Page


Fast & flexible framework for 3D geometry processing on the web

Project Page


Sparse & dense matrix routines with Cholesky, LU & QR support 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