Research Interests
I'm interested in exploring different ways we can train models to learn from biological data, in particular the vast amounts of unlabeled data now available. I believe that by reasoning about the nature of this data and how it encodes biology we can develop better training tasks and architectures for decoding its meaning. I hope my work will help us to better understand life and ultimately allow us to guide and engineer it to improve our lives.
Right now I am particularly interested in deep learning techniques for understanding microbial life. Despite being surrounded by an immense diversity of microbes at all times, we know most from their genomes alone! I am also particularly interested in joint-embedding approaches for self-supervised learning of biological sequence representations.
Background
I joined the Moses lab in September 2023, completing my MSc of Computer Science in January 2025 before beginning my PhD. During my undergraduate studies at McMaster University (BEng Software Engineering, 2023) I performed research in the labs of Dr. Nathan Magarvey (McMaster) and Dr. Kieran Campbell (Lunenfeld-Tanenbaum Research Institute). I have also previously completed data science/machine learning internships at the Royal Bank of Canada and at Borealis AI.
"It is better to use one's head for a few minutes than a computing machine for a few days!"
- Francis
Crick,
1955