Tzu-Mao Li tzli@ucsd.edu CV

I am an assistant professor at the CSE department of UCSD, working with awesome people at the Center for Visual Computing. I explore the connections between visual computing algorithms and modern data-driven methods and develop programming languages and systems for facilitating the exploration. I did a 2-year postdoc with Jonathan Ragan-Kelley at both MIT CSAIL and UC Berkeley. I did my Ph.D. in the computer graphics group at MIT CSAIL, advised by Frédo Durand. I received my B.S. and M.S. degrees in computer science and information engineering from National Taiwan University in 2011 and 2013, respectively, where I worked with Yung-Yu Chuang at the Communication and Multimedia Lab.

Students

Prospective students/postdocs

Teaching

Publications

	Differentiable Rendering of Neural SDFs through Reparameterization Sai Praveen Bangaru, Michaél Gharbi, Tzu-Mao Li, Fujun Luan, Kalyan Sunkavalli, Miloš Hašan, Sai Bi, Zexiang Xu, Gilbert Bernstein, Frédo Durand SIGGRAPH Asia 2022 (conference track full paper) unbiased differentiable SDF rendering. concurrent with Vicini et al.'s work. some extra memory saving tricks are introduced.
	Designing Perceptual Puzzles by Differentiating Probabilistic Programs Kartik Chandra, Tzu-Mao Li, Joshua Tenenbaum, Jonathan Ragan-Kelley SIGGRAPH 2022 (conference track full paper) synthesizing optical illusion by differentiating through Bayesian vision models
	Searching for Fast Demosaicking Algorithms Karima Ma, Michael Gharbi, Andrew Adams, Shoaib Kamil, Tzu-Mao Li, Connelly Barnes, Jonathan Ragan-Kelley ACM Transaction on Graphics (Presented at SIGGRAPH 2022) systematically exploring the Pareto frontier of efficient and high-quality data-driven image processing algorithms
	Efficient Automatic Scheduling of Imaging and Vision Pipelines for the GPU Luke Anderson, Andrew Adams, Karima Ma, Tzu-Mao Li, Tian Jin, Jonathan Ragan-Kelley Proceedings of the ACM on Programming Languages (OOPSLA 2021) a scalable and data-driven Halide autoscheduler that can process large pipelines and output GPU schedules
	Learning to Cluster for Rendering with Many Lights Yu-Chen Wang, Yu-Ting Wu, Tzu-Mao Li, Yung-Yu Chuang ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2021) adapting both the clustering and sampling distribution in lightcuts rendering using a data-driven method built on reinforcement lightcuts learning
	Multi-Resolution Shared Representative Filtering for Real-Time Depth Completion Yu-Ting Wu, Tzu-Mao Li, I-Chao Shen, Hong-Shiang Lin, Yung-Yu Chuang High-Performance Graphics (HPG) 2021 fast sampling-based cross bilateral filtering for depth completion with large missing regions
	Systematically Differentiating Parametric Discontinuities Sai Praveen Bangaru*, Jesse Michel*, Kevin Mu, Gilbert Bernstein, Tzu-Mao Li, Jonathan Ragan-Kelley *equal contribution ACM Transactions on Graphics (Proceedings of SIGGRAPH 2021) a programming language perspective on the automatic differention of integrals with discontinuous integrands, and its applications in physics and rendering
	Unbiased Warped-Area Sampling for Differentiable Rendering Sai Praveen Bangaru, Tzu-Mao Li, Frédo Durand ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2020) solving the differentiable rendering boundary integral with standard area sampling in an unbiased way using divergence theorem
	Differentiable Vector Graphics Rasterization for Editing and Learning Tzu-Mao Li, Michal Lukáč, Michaël Gharbi, Jonathan Ragan-Kelley ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2020) design choices and applications of differentiable vector graphics rasterization
	Physics-Based Differentiable Rendering: A Comprehensive Introduction Shuang Zhao, Wenzel Jakob, Tzu-Mao Li SIGGRAPH 2020 Course our differentiable rendering tutorial!
	DiffTaichi: Differentiable Programming for Physical Simulation Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, Frédo Durand International Conference on Learning Representation (ICLR) 2020 automatic differentiated Taichi and applications in model-based reinforcement learning
	Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, Frédo Durand ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2019) a data-oriented compiler that decouples hierarchical sparse data structures design from computation
	Learning to Optimize Halide with Tree Search and Random Programs Andrew Adams, Karima Ma, Luke Anderson, Riyadh Baghdadi, Tzu-Mao Li, Michaël Gharbi, Benoit Steiner, Steven Johnson, Kayvon Fatahalian, Frédo Durand, Jonathan Ragan-Kelley ACM Transactions on Graphics (Proceedings of SIGGRAPH 2019) first Halide autoscheduler that produces faster code comparing to human experts on average
	Sample-based Monte Carlo Denoising using a Kernel-Splatting Network Michaël Gharbi, Tzu-Mao Li, Miika Aittala, Jaakko Lehtinen, Frédo Durand ACM Transactions on Graphics (Proceedings of SIGGRAPH 2019). permutation invariant mapping from Monte Carlo samples to an image through splatting
	Differentiable Visual Computing [slides (Keynote)] [slides (Powerpoint)] Tzu-Mao Li MIT PhD Dissertation ACM SIGGRAPH 2020 Outstanding Doctoral Dissertation Award (announcement) a coherent view of my PhD research, with some new discussions regarding previous work, and some background reviews
	Inverse Path Tracing for Joint Material and Lighting Estimation Dejan Azinović, Tzu-Mao Li, Anton Kaplanyan, Matthias Nießner Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (oral presentation) applying differentiable rendering for material and lighting reconstruction
	Differentiable Monte Carlo Ray Tracing through Edge Sampling Tzu-Mao Li, Miika Aittala, Frédo Durand, Jaakko Lehtinen ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2018) computing gradients of the light transport equation through an explicit sampling of Dirac delta functions on triangle edges
	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) Halide meets automatic differentiation, and a new way to think about data-driven image processing
	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 the probability density of a light path 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) a Metropolis light transport algorithm that makes use of automatically differentiated Hessian matrix 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

Word cloud

Misc

	lajolla An educational renderer for UCSD CSE 272
	Deriving Radiative Backpropagation using the Recursive Form of Path Tracing A short note about the paper "Radiative Backpropagation: An Adjoint Method for Lightning-Fast Differentiable Rendering". I show that you can derive a similar algorithm using traditional automatic differentiation.
	diffvg A differentiable vector graphics rasterizer with PyTorch and Tensorflow interfaces.
	redner A differentiable Monte Carlo ray tracer with PyTorch and Tensorflow interfaces.
	Graphics bibtex A mega bibtex file containing many graphics-related literatures.
	Joint Stein’s Unbiased Risk Estimation for Adaptive Sampling and Reconstruction A short note on a generalized formulation of our SURE-based rendering method.
	dpt My prototypical renderer.
	smallgdpt Gradient-Domain Path Tracing in ~450 lines.