Yu Guang Wang 

yuguang.wang@mis.mpg.de yuguang.wang@unsw.edu.au 

Max Planck Institute for Mathematics in the Sciences & University of New South Wales 
Max Planck Institute for Mathematics in the Sciences Inselstrasse 22, 04103 Leipzig Germany 
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 semesterlong visitor of IPAM, UCLA (2019) and ICERM, Brown University (2018).
List of publications can be found at my Google Scholar.
•  Deep Learning Theory & Math Machine Learning Seminar, 

•  Annual Meeting of Australian Mathematical Society (AustMS'20), 

•  Thirtyfourth Conference on Neural Information Processing Systems (NeurIPS'20), Virtual, 6 Dec  12 Dec 2020.  
•  AI Group Seminar, 

•  IMA Workshop on Theory and Algorithms in Graphbased Learning, 

•  GAMM AG Workshop Computational and Mathematical Methods in Data Science, 

•  Thirtyseventh 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
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
I am an organiser for Minisymposium on Harmonic Analysis for Graph Signal Processing and Deep Learning Applications in
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:
Xuebin Zheng, 

Bingxin Zhou, 

Nicole Hallet, 
I have cosupervised two masters students in Statistics and Data Science at UNSW, 2018–2019:
Yi Guo, thesis title: CosmoEncoder: 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 