Homepage - Chiyu Max Jiang

Chiyu Max Jiang

Senior Manager / Senior Staff Research Scientist, Waymo AI Foundations
Generative Modeling for Autonomous Vehicles

Bio

I am currently a Senior Manager / Senior Staff Research Scientist at Waymo (formerly the Google self-driving car project), where I lead the World Modeling team in Waymo AI Foundations, building bleeding-edge world models for autonomy. My research interest is in building foundational generative models that serve the entire self driving stack, from perception, behavior prediction to planning and simulation.

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). During my Ph.D. 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.

News

[06/2026] New! Sensor2Sensor, our work on cross-embodiment sensor conversion for autonomous driving, was presented at CVPR 2026!
[02/2026] New! We introduced the Waymo World Model, a new frontier for autonomous driving simulation.
[10/2025] Drive&Gen, on co-evaluating end-to-end driving and video generation models, was presented at IROS 2025.
[06/2025] Two papers, SceneDiffuser++ and SceneCrafter, were presented at CVPR 2025!
[12/2024] SceneDiffuser, our work on efficient and controllable driving simulation, was presented at NeurIPS 2024.
[06/2023] Three papers accepted to and presented at CVPR 2023!

[09/2022] Our paper, Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining, has been accepted to ECCV 2022!
[09/2021] Our paper, Shape-As-Points, has been accepted to NeurIPS 2021 as an Oral paper!
[01/2021] I have started at Waymo as a research scientist working on 3D perception research.
[06/2020] Our recent work, ShapeFlow, has been accepted to NeurIPS 2020 for publication (spotlight).
[06/2020] Our recent work, MeshfreeFlowNet, has been nominated for the Best Student Paper Award!
[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 Unstructured Grids has been chosen for an oral presentation at the AGU (American Geophysical 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 Laboratory, 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 Laboratory, 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.

Experiences

Waymo | Mountain View, CA

(08/2026 - Present) Senior Manager / Senior Staff Research Scientist, Waymo AI Foundations
- Lead the World Modeling team, building bleeding-edge world models for autonomy.
(08/2024 - 07/2026) Staff Research Scientist
- Generative Models for Autonomous Vehicles (Perception, Prediction, Planning, Simulation).
(08/2022 - 07/2024) Senior Research Scientist
(01/2021 - 07/2022) Research Scientist
- 3D perception research for autonomous driving.

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 Berkeley National Lab | Berkeley, CA

(05/2018 - 05/2020) Research Intern / Student Researcher
- Research in physics-informed machine learning for spatial-temporal super-resolution (MeshfreeFlowNet - SC 20).
- Research on Spherical CNNs on Unstructured Grids and applications towards computer vision and climate science (Unstructured Grid Spherical CNN - ICLR 2019).
- Mentored and supervised junior researchers and research interns (Dana Lansigan, Eli Jergensen, Haris Sheikh)

Professional Service

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

Publications

		Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026) Jiahao Wang, Bo Sun, Yijing Bai, Vincent Casser, Songyou Peng, Zehao Zhu, Meng-Li Shih, Xander Masotto, Shih-Yang Su, Kanaad Parvate, Tiancheng Ge, Linn Bieske, Dragomir Anguelov, Mingxing Tan, Chiyu "Max" Jiang

		Drive&Gen: Co-Evaluating End-to-End Driving and Video Generation Models IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025) Jiahao Wang, Zhenpei Yang, Yijing Bai, Yingwei Li, Yuliang Zou, Bo Sun, Abhijit Kundu, Jose Lezama, Luna Yue Huang, Zehao Zhu, Jyh-Jing Hwang, Dragomir Anguelov, Mingxing Tan, Chiyu "Max" Jiang

		SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2025) Shuhan Tan, John Lambert, Hong Jeon, Sakshum Kulshrestha, Yijing Bai, Jing Luo, Dragomir Anguelov, Mingxing Tan, Chiyu "Max" Jiang

		SceneCrafter: Controllable Multi-View Driving Scene Editing Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2025) Zehao Zhu, Yuliang Zou, Chiyu "Max" Jiang, Bo Sun, Vincent Casser, Xiukun Huang, Jiahao Wang, Zhenpei Yang, Ruiqi Gao, Leonidas Guibas, Mingxing Tan, Dragomir Anguelov

		SceneDiffuser: Efficient and Controllable Driving Simulation Initialization and Rollout Neural Information Processing Systems (NeurIPS 2024) Chiyu "Max" Jiang*, Yijing Bai, Andre Cornman, Christopher Davis, Xiukun Huang, Hong Jeon, Sakshum Kulshrestha, John Lambert, Shuangyu Li, Xuanyu Zhou, Carlos Fuertes, Chang Yuan, Mingxing Tan, Yin Zhou, Dragomir Anguelov

		3D Open-Vocabulary Panoptic Segmentation with 2D-3D Vision-Language Distillation European Conference on Computer Vision (ECCV 2024) Zihao Xiao, Longlong Jing, Shangxuan Wu, Alex Zihao Zhu, Jingwei Ji, Chiyu "Max" Jiang, Wei-Chih Hung, Thomas Funkhouser, Weicheng Kuo, Anelia Angelova, Yin Zhou, Shiwei Sheng

		MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2023, Highlight, 2.6% acceptance rate) Chiyu "Max" Jiang, Andre Cornman, Cheolho Park, Ben Sapp, Yin Zhou, Dragomir Anguelov (*equal contributions)

		OpenScene: 3D Scene Understanding with Open Vocabularies Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2023) Songyou Peng, Kyle Genova, Chiyu "Max" Jiang, Andrea Tagliasacchi, Marc Pollefeys, Thomas Funkhouser

		NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2023) Congyue Deng, Chiyu "Max" Jiang, Charles R. Qi, Xinchen Yan, Yin Zhou, Leonidas Guibas, Dragomir Anguelov

		Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining European Conference on Computer Vision (ECCV, 2022) Chiyu "Max" Jiang, Mahyar Najibi, Charles R. Qi, Yin Zhou, Dragomir Anguelov

		Shape-As-Points: A Differentiable Poisson Solver Neural Information Processing Systems (NeurIPS 2021, Oral) Songyou Peng, Chiyu "Max" Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, Andreas Geiger (* corresponding authors)

		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