Yu Guang Wang

Email: yuguang.wang@mis.mpg.de
Max Planck Institute for Mathematics in the Sciences
University of New South Wales
Mail: Max Planck Institute for Mathematics
in the Sciences
Inselstrasse 22, 04103 Leipzig


I am a scientist (postdoc) at Max Planck Institute for Mathematics in Sciences, hosted by Prof Guido Montúfar. I am also an adjunct associate lecturer at UNSW Sydney. My research interests lie in computational mathematics, statistics, machine learning, and data science. In particular, I am working on deep learning, graph neural networks, applied harmonic analysis, Bayesian inference, information geometry, random fields, numerical analysis, and applications to healthcare, biomedical technology and cosmology. I obtained my PhD in applied mathematics from University of New South Wales under supervision of Prof Ian Sloan and Rob Womersley. I have been a semester-long visitor of IPAM, UCLA (2019) and ICERM, Brown University (2018).

Current Research Interests


    List of publications can be found at my Google Scholar.

Book Chapter

Technical Report


Recent and Upcoming Seminars

  •  Deep Learning Theory & Math Machine Learning Seminar, MPI MIS + UCLA, Virtual, Regular, 2021.
  •  Annual Meeting of Australian Mathematical Society (AustMS'20), University of New England, Virtual, 8 Dec - 10 Dec 2020.
  •  Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS'20), Virtual, 6 Dec - 12 Dec 2020.
  •  AI Group Seminar, University of Cambridge, Virtual, 28 Oct 2020.
  •  IMA Workshop on Theory and Algorithms in Graph-based Learning, University of Minnesota, Virtual, 14 Sep - 18 Sep 2020.
  •  GAMM AG Workshop Computational and Mathematical Methods in Data Science, MPI MIS, Virtual, 10 Sep - 11 Sep 2020.
  •  Thirty-seventh International Conference on Machine Learning (ICML'20), Virtual, 12 Jul - 18 Jul 2020.


I am a Guest Editor for Special Issue Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications in IEEE Transactions on Neural Networks and Learning Systems (Top AI Journal). Call for Papers!

I am a review Editor for the journal Frontiers in Applied Mathematics and Statistics.

I am a reviewer for ICML'20 (Top Reviewer), ICML'21, NeurIPS'20, ICLR'21, IJCAI'21.

I am an organiser for Collaborate@ICERM on Geometry of Data and Networks, 2019 joint with Joan Bruna (NYU), Zheng Ma (Princeton), Guido Montúfar (UCLA), Nina Otter (UCLA).

I am an organiser for Minisymposium on Harmonic Analysis for Graph Signal Processing and Deep Learning Applications in SIAM Conference on Mathematics of Data Science 2020 (MDS20) joint with Xiaosheng Zhuang (CityU HK).

Teaching and Supervision

I was a class tutor in UNSW for following courses.

    Semester 3 2019, MATH3101/5305 Computational Mathematics (Numerical Methods for PDEs)
    Semester 2 2018, MATH2089 Numerical Methods and Statistics
    Semester 1 2015, MATH1131 Mathematics 1A
    Semester 2 2014, MATH1231 Mathematics 1B, MATH1241 Higher Mathematics 1B, MATH2019 Engineering Mathematics 2E

I am supervising four PhD students:

    Kai Yi, UNSW Statistics and Data Science, research on deep Bayesian learning, graph neural networks, since 2019 (chief supervisor, joint with A/Prof Yanan Fan, Dr Jan Hamann)
    Xuebin Zheng, University of Sydney Business Analytics, research on graph neural networks, since 2018 (co-supervisor, with Prof Junbin Gao)
    Bingxin ZhouUniversity of Sydney Business Analytics, research on graph neural networks, manifold learning, since 2018 (co-supervisor, with Prof Junbin Gao).
    Nicole HalletUniversity of Sydney Medicine, research on cornea AI, since 2018 (co-supervisor, with Prof Gerard Sutton, Dr Jingjing You).

I have co-supervised two masters students in Statistics and Data Science at UNSW, 2018–2019:

    Yi Guo, thesis title: Cosmo-Encoder: A Bayesian deep learning approach for cosmic microwave background inpainting
    Kai Yi, thesis title: Variational autoencoder for cosmic microwave background image inpainting.
Copyright @ 2020 Yu Guang Wang Top