Chiyu Max Jiang
Chiyu 'Max' Jiang (蒋驰宇)

Research Scientist, Waymo Research
3D Deep Learning | Computer Vision | Self-driving Cars


I am currently a Research Scientst at Waymo (formerly the Google self-driving car project), where I work on 3D perception algorithms for self-driving cars.

I received a Ph.D. from UC Berkeley in 2020. I worked on 3D Computer Vision / Geometric Deep Learning algorithms, and have first-author publications in top CV/ML conferences (CVPR, ICCV, NeurIPS, ICLR). I had the pleasure of collaborating with Matthias Niessner (TUM), Tom Funkhouser (Google), Leonidas Guibas (Stanford), Andrea Tagliasacchi (Google Brain), Anima Anandkumar (CalTech, NVIDIA) and Prabhat (LBNL), among other amazing researchers in this field. I was advised by Philip Marcus, and I have worked as interns and student researchers at Google AI and Lawrence Berkeley National Lab.


  • [06/2020] New! Our recent work, ShapeFlow, has been accepted to NeurIPS 2020 for publication (spotlight).
  • [06/2020] New! Our recent work, MeshfreeFlowNet, has been nominated for the Best Student Paper Award!
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  • [06/2020] I started the position of Senior Applied Research Scientist at Cruise, working on 3D Computer Vision for self-driving cars.
  • [06/2020] Our recent work, MeshfreeFlowNet, has been selected for publication at SC20!
  • [03/2020] Two papers accepted to CVPR 2020! Check out our papers on local implicit grid representations for 3D scenes, and adversarial texture optimization from RGB-D scans.
  • [07/2019] Our recent paper on Deep Differentiable Simplex Layer has been accepted to ICCV 2019 conference!
  • [05/2019] I will be interning in Machine Perception @ Google Research in summer 2019 as a Ph.D research intern.
  • [12/2018] My work on Spherical CNNs on Unstructred Grids has been chosen for an oral presentation at the AGU (Americal Geophysics Union).
  • Two of my papers have been accepted to the ICLR 2019 conference!
  • [06/2018] I am interning this summer at the Data Analytics group in NERSC, Lawrence Berkeley National Labratory, working with Prabhat and Karthik Karshinath on new Deep Learning methodologies for Climate Science.
  • [03/2018] I am invited to visit Center for Nonlinear Studies at Los Alamos National Labratory, and to present my work on 3D deep learning.
  • [01/2018] I made an oral presentation of my work on Aerodynamics Optimization using Deep Learning at Physics Informed Machine Learning.


Waymo | Mountain View, CA

  • (01/2021 - Present) Research Scientist, Waymo Research
    • Research in 3D Computer Vision and Perception.

Cruise | San Francisco, CA

  • (06/2020 - 01/2021) Senior Applied Research Scientist, Computer Vision
    • Successfully delivered and deployed a new generation 3D object detection solution, leading to a significant functional and latency improvement, resulting in increased safety of the car.
    • Led and coordinated cross-team collaboration for deployment and performance optimization.

Google AI | Mountain View, CA

  • (05/2019 - 03/2020) Research Intern / Student Researcher
    • Developed novel learning based implicit 3D geometry representation for large-scale scene reconstruction from point clouds (Local Implicit Grid - CVPR 2020).
    • Collaborated on a project for generating enhanced texture for scanned 3D models (Adversarial Texture Optimization - CVPR 2020).
    • Proficient with Google internal infrastructure and TensorFlow for ML development, and Apache Beam for massive data processing and ML inference workflows.
    • Initiated and coordinated internal and external collaborations with research partners.

Lawrence Berekely National Lab | Berkeley, CA

  • (05/2018 - 05/2020) Research Intern / Student Researcher

Professional Service

Reviewer for ICCV, AAAI, CVPR, ECCV, NeurIPS, ICLR.


ShapeFlow: Learnable Deformations Among 3D Shapes
Neural Information Processing Systems (NeurIPS 2020, Spotlight)
Chiyu "Max" Jiang*, Jingwei Huang*, Andrea Tagliasacchi, Leonidas Guibas

MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
International Conference for High Performance Computing, Networking, Storage and Analysis (SC20, Best Student Paper nomination)
Chiyu "Max" Jiang*, Soheil Esmaeilzadeh*, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar

Local Implicit Grid Representations for 3D Scenes
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)
Chiyu "Max" Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Niessner, Tom Funkhouser

Adversarial Texture Optimization from RGB-D Scans
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)
Jingwei Huang, Justus Thies, Angela Dai, Abhijit Kundu, Chiyu "Max" Jiang, Leonidas Guibas, Matthias Niessner, Tom Funkhouser

DDSL: Deep Differentiable Simplex Layer for Learning Geometric Signals
Proceedings of the IEEE International Conference on Computer Vision (2019)
Chiyu "Max" Jiang*, Dana Lansigan*, Philip Marcus, Matthias Niessner

Spherical CNNs on Unstructured Grids
International Conference on Learning Representations (2019)
Chiyu "Max" Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Niessner

Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation
International Conference on Learning Representations (2019)
Chiyu "Max" Jiang, Dequan Wang, Jingwei Huang, Philip Marcus, Matthias Niessner

Leveraging Bayesian Analysis To Improve Reduced Order Models
Journal of Computational Physics (2019): 280-297.
B.T. Nadiga, Chiyu Max Jiang, Daniel Livscu

Finding the optimal shape of the leading-and-trailing car of a high-speed train using design-by-morphing
Computational Mechanics (2017): 1-23.
Sahuck Oh, Chung-Hsiang Jiang, Chiyu "Max" Jiang, Philip Marcus

Hierarchical Detail Enhancing Mesh-Based Shape Generation with 3D Generative Adversarial Network
Chiyu "Max" Jiang, Philip Marcus

Other Select Projects

Morphing of Genus-Zero Shapes using Spherical Parameterization  
Chiyu "Max" Jiang, Philip Marcus