PhD Candidate, University of British Columbia
I am a PhD candidate in Biomedical Engineering at the University of British Columbia, in the de Boer Lab. Almost every model of gene regulation is trained on data that was generated for some other reason: an atlas, a consortium characterisation, whatever happened to get measured. The field has built remarkable models on top of leftovers, and has rarely built the experiment for the model.
I am working on addressing that gap. I design experiments whose purpose is to synthesize the most suitable data for training models, at a throughput worth training on.
Better data is only half of it. Every experiment and dataset carries its own bias, and a model will fit that bias as readily as the biology, so the rest of my work is on ensuring the models learn causal structure rather than the correlations an assay left behind, on a faithful reporting of the model’s performance, and on knowing when to trust a prediction and the interpretation we draw from it. Trust is what makes them worth using.
I am always looking for students to work with. I have supervised six co-op and PhD students in the de Boer Lab, and motivated undergraduates and high-school students are welcome to get in touch.
Vancouver, Canada
Current work
My work spans the path from data to application: how training data is generated, how models are trained and evaluated, and when their predictions and interpretations can be trusted.
Most data used to train models of gene regulation were generated to characterise biology, not to train models. I work on developing technologies to synthesize sequence libraries and to design experiments whose purpose is to train models.
Model performance depends on the architecture, the training strategy and how the training data are handled. I work on all three, focusing primarily on the data.
Standard evaluations overstate what a model has learned when related sequences appear in both the training and test sets. I work on detecting this homology and accounting for it, so that evaluation separates learned regulatory logic from memorization.
Aggregate benchmark scores say little about whether an individual prediction, such as a single variant effect, is correct. I work on estimating the reliability of individual predictions.
Most biological conclusions from these models are drawn through interpretation methods such as attribution and perturbation, which are themselves rarely tested. I work on assessing how faithfully they report what a model has learned.
Topics I am interested in
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