Samir Agarwala

I am a MS CS student at Stanford University working in computer vision.

I am grateful to have worked with Jiajun Wu and Leonidas Guibas during my MS and completed my undergraduate education at the University of Michigan where I was fortunate to be advised by David Fouhey.

Before starting research in mainstream vision, I had the privilege of working with Amy Cohn and Stephen Parker at Michigan where I worked on healthcare operations research and bioinformatics respectively.

Email  /  CV  /  Google Scholar  /  Github

I am interested in developing robust computer vision systems that can understand the world around us. Some areas of particular interest include scene understanding, learning robust visual representations and models using unstructured visual data, and neural rendering.

A novel real-world object inverse rendering benchmark to evaluate inverse rendering methods.

Specular objects such as coke cans often appear "accidentally" in images and can be used to recover scene lighting from single image observations using differentiable rendering.

Transformers are really good at integrating evidence across multiple views and producing a planar reconstruction.

Real-life variation in learning speeds of surgical trainees and decrease in available training opportunities can affect trainee competency and potentially endager patient safety.

Identifying early disease-driving events of type 2 diabetes through integrative analysis of diverse modalities.

Website based on Jon Barron's website