The Hoffman Lab at the Princess Margaret Cancer Centre and the University of Toronto develops machine learning techniques to better understand chromatin biology. These models and algorithms transform high-dimensional functional genomics data into interpretable patterns and lead to new biological insight. A key focus of the lab is to train a new generation of computational biologists.
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Dr. Michael Hoffman
Michael Hoffman creates predictive computational models to understand interactions between genome, epigenome, and phenotype in human cancers. He implemented the genome annotation method Segway, which simplifies interpretation of large multivariate genomic datasets, and was a linchpin of the NIH ENCODE Project analysis. He is a principal investigator at Princess Margaret Cancer Centre and Assistant Professor in the Department of Medical Biophysics, University of Toronto. He was named a CIHR New Investigator and has received several awards for his academic work, including the NIH K99/R00 Pathway to Independence Award, and the Ontario Early Researcher Award. Michael enjoys kickball (or "soccer baseball" as it is called in Canada), agritourism, the Marvel Cinematic Universe, and making ice cream (favorite flavor: maple walnut). |
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Luomeng Tan
Luomeng’s thesis work focuses on methodology development on biological sequencing data and optimization. She previously worked on analyzing transcription factor binding sites using CUT&RUN sequencing data and optimizing the analysis pipeline. She now works on the prediction of histone modification from DNA methylation data. |
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Mary Agopian
Mary is interested in the application of computational medicine approaches to the expansive field of immunology. Her thesis focuses on designing internal controls for an immune sequencing method, with the goal of developing a robust and quantifiable tool to be used in the field. She received her H.B.Sc. in Microbiology and Immunology from McGill University. Outside the lab, you can find her running a yoga session, performing ballet, and reading. |
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Yahan Zhang
Yahan is interested in applying machine learning techniques to predict gene regulation, with a special focus on cell-free DNA. She earned her H.B.Sc. in Computer Science and Biology from McGill University. Beyond her work in the lab, she finds joy in reading, exercising, and hiking. |