Zizhou Huang
Contact Info:
  • 60 5th Ave, Office 550
  • New York, NY 10011
Email: zizhou at nyu.edu
About Me

I am a 4th year Ph.D. student at the Courant Institute of Mathematical Sciences, New York University. I am a member of the Geometric Computing Lab where I work with Professor Daniele Panozzo and Denis Zorin. My research focuses on physics simulations in Computer Graphics and optimization problems based on simulations. I received my Bachelor's degree in Applied Mathematics at University of Science and Technology of China in 2020.

June 12, 2024

Our paper Cut-Cell Microstructures for Two-scale Structural Optimization is accepted and will be presented at SGP 2024 at MIT!

May 28, 2024

I'm joining the Avatar team at Roblox for a Research internship this summer!

Feb. 8, 2024

Our paper Differentiable solver for time-dependent deformation problems with contact is accepted by ACM Transaction on Graphics and will be presented at Siggraph 2024 in Denver!

June 6, 2022

I'm joining the simulation team at NTop for a summer internship today!

Sep. 1, 2020

I started Ph.D. in Computer Science at New York University.

June 1, 2020

I received my Bachelor's degree in Applied Mathematics at University of Science and Technology of China.

Open-Source Project
A polyvalent C++ and Python FEM library.
Zizhou Huang, Davi Colli Tozoni, Arvi Gjoka, Zachary Ferguson, Teseo Schneider, Daniele Panozzo, Denis Zorin
ACM Transaction on Graphics (SIGGRAPH), 2024.
Summary: We introduce a general differentiable solver for time-dependent deformation problems with contact and friction. It support static and dynamic problems, and differentiation with respect to all physical parameters involved, including shape, elastic parameters, friction coefficient, and initial conditions. Our analytically derived adjoint formulation is over 10x faster than automatic differentiation. code.
Davi Colli Tozoni, Zizhou Huang, Daniele Panozzo, Denis Zorin
Computer Graphics Forum (SGP), 2024
Summary: Two-scale topology optimization, combined with the design of microstructure families with a broad range of effective material parameters, is increasingly widely used in many fabrication applications to achieve a target deformation behavior for a variety of objects. We adapt this approach to complex shapes in situations when preserving the shape's surface is important. We propose an automated and robust pipeline based on this approach with the ability of handling 2D and 3D shapes of high complexity.
Zizhou Huang, Teseo Schneider, Minchen Li, Chenfanfu Jiang, Denis Zorin, Daniele Panozzo
arXiv, 2021
Summary: We introduce a collection of benchmark problems in 2D and 3D (geometry description and boundary conditions), including simple cases with known analytic solution, classical experimental setups, and complex geometries with fabricated solutions for evaluation of numerical schemes for incompressible Navier-Stokes equations in laminar flow regime. We compare the performance of a representative selection of most broadly used algorithms for Navier-Stokes equations on this set of problems.
Teaching Assistant, New York University
Honors Analysis of Algorithms, 2023
Honors Analysis of Algorithms, 2022
Computer Graphics, 2021
Teaching Assistant, University of Science and Technology of China
Numerical Linear Algebra, 2019
Multivariate Calculus, 2018