Tzu-Mao Li

I am a Ph.D. student in the computer graphics group at MIT CSAIL, advised by Frédo Durand. I am interested in researching photorealistic rendering and programming systems. Specifically, I work on developing efficient sampling methods for light transport simulation, and building systems for facilitating computer graphics applications through symbolic manipulation (e.g. automatic differentiation). I received my B.S. and M.S. degree in computer science and information engineering from National Taiwan University in 2011 and 2013, respectively. During my time at National Taiwan University, I was a member of the graphics group at Communication and Multimedia Lab, where I worked with Yung-Yu Chuang.


Differentiable Programming for Image Processing and Deep Learning in Halide
Tzu-Mao Li, Michaël Gharbi, Andrew Adams, Frédo Durand, Jonathan Ragan-Kelley
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2018), to appear.
Halide meets automatic differentiation.
Aether: An Embedded Domain Specific Sampling Language for Monte Carlo Rendering
Luke Anderson, Tzu-Mao Li, Jaakko Lehtinen, Frédo Durand
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2017).
A programming language for Monte Carlo rendering that automatically computes probability density of a sample.
Anisotropic Gaussian Mutations for Metropolis Light Transport through Hessian-Hamiltonian Dynamics
Tzu-Mao Li, Jaakko Lehtinen, Ravi Ramamoorthi, Wenzel Jakob, Frédo Durand
ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2015).
Metropolis light transport guided by automatically differentiated Hessian of light path contribution.
Dual-Matrix Sampling for Scalable Translucent Material Rendering
Yu-Ting Wu, Tzu-Mao Li, Yu-Hsun Lin, and Yung-Yu Chuang
IEEE Transactions on Visualization and Computer Graphics (TVCG), 2015.
Subsurface scattering with many-lights using matrix sampling.
SURE-based Optimization for Adaptive Sampling and Reconstruction
Tzu-Mao Li, Yu-Ting Wu, Yung-Yu Chuang
ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2012).
Stein's unbiased risk estimator for sampling and denoising in Monte Carlo rendering.


Joint Stein’s Unbiased Risk Estimation for Adaptive Sampling and Reconstruction
A short note on a generalized formulation of our SURE-based rendering method.
My prototypical renderer.
Gradient-Domain Path Tracing in ~450 lines.