About Me

Research Interests

My research focuses on machine learning, particularly deep learning. Most of my research is focused on building fair and safe machine learning and AI systems.

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.

AI Safety

  • By this same token, AI algorithms that are intended to operate in practice cannot endanger their users or people in general.
  • To be safely usable, a model must be robust to reward misspecification and able constructively interact with humans.
  • I am interested in thinking about AI safety and transparency and its practical implications on a technical and policy level.



I was a TA for the following courses:

Miscellaneous Slides

Some presentations I've made for various reasons:



Paper Summaries


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