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

Ph.D student at UC Berkeley.
Deep Learning methods for 3D Geometries and Physical Systems.

About

I’m a fifth-year Ph.D student at UC Berkeley advised by Prof. Philip Marcus, affiliated with the Data Analytics group at NERSC, Lawrence Berkeley National Lab. My research interest is in Machine Learning and Deep Learning methodologies related to 3D geometry. In particular, I am interested in leveraging advances in 3D learning for applications in a variety of physical and engineering systems, examples include 3D scene reconstruction, omnidirectional image segmentation, climate pattern detection and aerodynamical shape optimization.

Prior to joining UC Berkeley, I got my Bachelor’s degree from Cornell University, as well as a joint degree from Zhejiang University.

News

  • New! My recent paper on Deep Differentiable Simplex Layer has been accepted to ICCV 2019 conference!
  • I will be interning in Machine Perception @ Google Research in summer 2019 as a Ph.D research intern.
  • 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!
  • 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.
  • I am invited to visit Center for Nonlinear Studies at Los Alamos National Labratory, and to present my work on 3D deep learning.
  • I made an oral presentation of my work on Aerodynamics Optimization using Deep Learning at Physics Informed Machine Learning.

Experiences

  • [Su 19] Research Intern at Google AI - Perception:
    Research on deep 3D geometric representation and reconstructions.

  • [Su 18] Research Intern at Lawrence Berkeley National Lab:
    Research on Spherical CNNs on Unstructured Grids and applications towards computer vision and climate science (subsequent publication at ICLR 2019).

  • [Fa 17] Graduate Student Instructor:
    (CS294-73): Software Engineering for Scientific Computing.

Professional Service

Reviewer [ICCV’19], Program Committee Member [AAAI’20].

Recent Publications

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   arXiv (2017)
Chiyu "Max" Jiang, Philip Marcus

Other Select Projects

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