Tao Du

Shanghai Qi Zhi Institute PI, Assistant Professor at IIIS, Tsinghua.

Tao Du's research aims at developing computational methods to help people understand physical systems. Before joining Tsinghua University, he completed his Ph.D. at MIT in 2021 under the supervision of Wojciech Matusik and continued as a Postdoctoral Associate from 2021 to 2022. At MIT, he developed differentiable deformable-solid and fluid simulators and explored their downstream applications in machine learning and robotics. His research work has been featured by technical media outlets including WIRED, IEEE Spectrum, TechCrunch, Engadget, and so on. In addition, he was recognized by NeurIPS (2021 and 2022) and ICML (2022) as an outstanding reviewer.

Personal honor

NeurIPS top reviewer (2022)

ICML outstanding reviewer (2022)

NeurIPS outstanding reviewer (2021)

Computational Design

Automatic design of dynamical systems using computational methodology

Neural Physics Simulation

Physical simulation with first principles and learning techniques

Research Direction：

Computer Graphics: Physics Simulation/High-Performance Computing

Responsibilities：

Developing high-performance multi-physics simulators for learning/robotics applications.

Required Qualification：

CS/Math/Physics major with strong mathematical backgrounds;

Prior experience with computer graphics research and proficient in C++/CUDA programming.

Please send your CV：

leqq@sqz.ac.cn

5. Qiqin Le, Yitong Deng, Jiamu Bu, Bo Zhu, Tao Du, Second-Order Finite Elements for Deformable Surfaces, SIGGRAPH Asia (Technical Paper), 2023 查看PDF

4. Tao Du, Deep Learning for Physics Simulation, SIGGRAPH (Courses), 2023 查看PDF

3. Yichen Li, Peter Yichen Chen, Tao Du, Wojciech Matusik, Learning Preconditioners for Conjugate Gradient PDE Solvers, International Conference on Machine Learning (ICML), 2023 查看PDF

2. Pingchuan Ma, Peter Yichen Chen, Bolei Deng, Joshua B. Tenenbaum, Tao Du, Chuang Gan, Wojciech Matusik, Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics, International Conference on Machine Learning (ICML), 2023 查看PDF

1. Sizhe Li*, Zhiao Huang*, Tao Chen, Tao Du, Hao Su, Joshua B. Tenenbaum, Chuang Gan, DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics, International Conference on Learning Representation (ICLR), 2023 查看PDF