Susan Wei

Susan Wei


University of Melbourne


Susan Wei is a lecturer (assistant professor) in the School of Mathematics and Statistics at the University of Melbourne. She currently holds a Discovery Early Career Researcher Award (DECRA) from the Australian Research Council (ARC). Her research interests include statistics, machine learning, deep learning, variational inference, singular learning theory. She is part of the Melbourne Deep Learning Group.


  • Statistics
  • Deep Learning
  • Singular learning theory


  • PhD in Statistics, 2014

    University of North Carolina, Chapel Hill

  • BA in Mathematics, 2009

    University of California, Berkeley



Visiting Faculty Researcher

Google Deepmind

Jun 2023 – Dec 2023 Sydney, Australia


School of Mathematics and Statistics, University of Melbourne

Jun 2018 – Present Melbourne, Australia

Assistant Professor

Division of Biostatistics, University of Minnesota

Jan 2016 – Apr 2018 Minnesota, USA


Institute of Mathematics, Ecole Polytechnique Federale de Lausanne

Apr 2014 – Dec 2015 Lausanne, Switzerland

Deep Learning Down Under

In summer 2024, I co-organized a deep learning workshop in Lorne, Australia with Peter Bartlett. Here is the website of the workshop, which was generously supported by Google Research.


In Winter 2021, I gave a week-long lecture series on Neural Networks and Related Models as part of the Australian Mathematical Sciences Institute (AMSI) Winter School program, an annual event open to graduate students, early career researchers, and industry members across Australia. The course was an introduction to deep learning as well as some probabilistic models involving neural networks (flow-based models and deep generative models).

You can find my lecture slides here for Part 1 of the course where I covered the following topics:

  • An Introduction to Neural Networks: key components of DL pipeline, multilayer perception, forward/backward propagation, computational graphs
  • Stochastic Optimization and Extensions
  • The Art of Model Training and Regularization: Model selection, weight decay, dropout, initialization
  • Convolutional Neural Networks and Recurrent Neural Networks

The second part of the module on deep generative modeling was given by Robert Salomone. You can find his excellent teaching materials here.