Susan Wei

Susan Wei

Lecturer

University of Melbourne

Biography

Susan Wei is a lecturer 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, and deep learning. She is part of the Melbourne Deep Learning Group.

Interests

  • Singular learning theory
  • Continuous depth neural networks
  • Deep generative modeling

Education

  • PhD in Statistics, 2014

    University of North Carolina, Chapel Hill

  • BA in Mathematics, 2009

    University of California, Berkeley

Experience

 
 
 
 
 

Lecturer

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
 
 
 
 
 

Postdoc

Institute of Mathematics, Ecole Polytechnique Federale de Lausanne

Apr 2014 – Dec 2015 Lausanne, Switzerland

Teaching

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.