I am broadly interested in how small molecules in the human body and our environment affect our risk of developing cancer and our response to anti-cancer therapies. The human body contains thousands of small molecules, and is exposed to thousands more in daily life. This complex chemical ecosystem reflects both the endogenous metabolism of human cells as well as xenobiotic exposures from our diets, our microbiome and our natural and built environments. Collectively, the small molecules from these sources influence our risk of developing cancer, determine how we respond to anticancer therapies and provide molecular biomarkers that could be used in the clinic to make diagnoses and select treatments.

At present, however, most of these small molecules remain unknown. Remarkably, this is not for lack of an experimental technique capable of measuring them. Mass spectrometry typically detects thousands of small molecules in routine experiments. Instead, the challenge is computational: the overwhelming majority of the detected small molecules cannot be identified. This profusion of unidentified chemical entities has been dubbed the “dark matter” of the metabolome.

I am interested in developing new computational tools to illuminate this metabolic dark matter in small molecule mass spectrometry data, with the ultimate goal of identifying every small molecule detected in a mass spectrometer. I believe that this technology could lead to transformative advances in cancer diagnosis and treatment, in much the same way as the maturation of DNA sequencing technology has created new opportunities for personalized medicine by enabling cancer genomics. I am particularly interested in the roles that unidentified small molecules may play in the development and progression of cancer through connections to genetic risk factors and the human microbiome.

My work takes diverse approaches depending on the biological question at hand. These include developing new computational methods, collecting and analyzing new datasets or carrying out meta-analyses of vast quantities of data deposited to public repositories.

I completed my MD/PhD at the University of British Columbia, where my doctoral work focused on developing machine-learning tools for mass spectrometry-based proteomics and metabolomics. I was also a visiting PhD student at the EPFL in Switzerland, where I was trained in single-cell and spatial transcriptomics. Prior to that, I completed my undergraduate degree at McMaster University, and helped translate my research on natural products discovery into a successful start-up company, Adapsyn Bioscience.

My work has been recognized with a number of awards, including the International Birnstiel Award, inclusion in the Forbes “30 Under 30” list, the Dan David Prize, and the Borealis AI fellowship.