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 firstname.lastname@example.org
Conference proceedings and Journal publications
First paper to derive and implement an unbiased estimator in the area-measure for the differentiable rendering equation.
Applies a path-space differentiable rendering algorithm to improve photometric BSDF recovery using concave objects.
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.
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.
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.
A timeline of my academic education and internships
Sep 2019 - Current
Dec 2018 - July 2019
May 2017 - Aug 2017
May 2016 - Aug 2016