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 Associate Professor in the Departments of Medical Biophysics and Computer Science, 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.

The Hoffman group 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.

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For a list of Dr. Hoffman's publications, please visit PubMed, Scopus, Publons or ORCID.

Associate Professor, Department of Medical Biophysics, University of Toronto
Associate Professor, Department of Computer Science, University of Toronto
Faculty Affiliate, Vector Institute
Member, Collaborative Graduate Program in Genome Biology and Bioinformatics, University of Toronto