About Me

Research Interests

My research focuses on machine learning, particularly deep learning. I have two main interests:

Fairness in Machine Learning

  • Algorithms are used to make or assist with many decisions which impact our lives in fields like marketing, medicine, law, or finance. We need to ensure that algorithmic decisions aren't biased in undesirable ways.
  • Fair algorithms could be used to prevent illegal discrimination by companies and justice systems, make more accurate predictions about clinical treatments , or provide more diverse content filtering methods on social media.
  • I am interested in exploring and defining algorithmic fairness, as well as applying and integrating it with deep learning methods.

Semi-Supervised Learning

  • Many recent advances in deep learning require large amounts of labelled or annotated data ("supervised learning"). Getting these labels or annotations can be expensive in time, money, or human effort.
  • In semi-supervised learning, the goal is to develop methods which can work on partially-labeled datasets, thereby removing the need for vast quantities of labelled data.
  • This helps those with terabytes of unlabelled data (e.g. Google, Facebook) to leverage it better and allows those without the resources to collect a large number of labels (e.g. everybody else) to benefit from deep learning technology.
  • I am interested in improving semi-supervised learning using generative modelling methods (like this) and various forms of data augmentation.


I'm also a musician - I love writing, singing, and playing music. A couple of years ago I wrote the songs for a musical in the Toronto Fringe Festival and I'm currently working on some more music, to be released soon-ish (at the time of writing this webpage). I also play jazz piano and love to improvise. If you're interested, check out my Youtube channel!

Contact Me

lastname at cs dot toronto dot edu