About Me

I am currently a PhD student at MIT CSAIL in the Computer Graphics group, advised by Prof. Fredo Durand.

Currently, I focus on differentiable simulation methods, more specifically, on differentiable rendering and its applications to computer vision and perception. Other topics I'm actively pursuing include compilers for differentiable programming, real-time path tracing, neural rendering, and physically-based modelling.

My previous experience includes a 6-month research staff position at CMU (advised by Prof. Ioannis Gkioulekas and Prof. Anat Levin) and two consecutive summer internships at INRIA (advised by Prof. George Drettakis).

In my time-off, I like to travel, take pictures, and design websites. The end of this webpage has a few of my favourite pictures.

You can find a copy of my CV here!

If you have any questions, contact me at sbangaru@mit.edu

Affliations

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PhD (EECS)
2019 - current
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MS in CS (CSD)
2017 - 2019
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BTech (CSE)
2013 - 2017
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SWE Intern
Summer 2018
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Research Intern
Summer 2016
Summer 2017

Publications

Conference proceedings and Journal publications

ACM SIGGRAPH Asia 2020

Unbiased Warped-Area Sampling for Differentiable Rendering

Sai Praveen Bangaru, Tzu-Mao Li, Fredo Durand

First paper to derive and implement an unbiased estimator in the area-measure for the differentiable rendering equation.

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IEEE ICCP 2020

Towards Reflectometry from Inter-reflections

Kfir Shem-Tov*, Sai Praveen Bangaru*, Anat Levin, Ioannis Gkioulekas

Applies a path-space differentiable rendering algorithm to improve photometric BSDF recovery using concave objects.

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CMU Technical Reports

Towards Shape Reconstruction through Differentiable Rendering

Sai Praveen Bangaru

A solution to the problems with shape-recovery methods. Uses a mitsuba shape-differentiable path tracer and a TensorFlow optimizer to obtain accurate reconstructions in the presence of interreflections and non-lambertian surfaces.

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IIT-Madras Technical Reports

Action Conditional Projection Neural Networks

Sai Praveen Bangaru

Similar to Action-Conditional Video Prediction, which predicts the next frame of an ATARI game given the user input, ACPNNs are designed to predict 2D and 3D environments where the output is based on perspective vision.

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NIPS Deep Reinforcement Learning Workshop 2016

Multi-task Reinforcement Learning

Sai Praveen Bangaru, Suhas Jayaram, Balaraman Ravindran

Exploration in multi-task reinforcement learning is critical in training agents to deduce the underlying MDP. We present a novel method to facilitate exploration in multi-task reinforcement learning using deep generative models.

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Travel

Pictures from around the world!