Michael Hoffman creates predictive computational models to understand interactions between genome, epigenome, and phenotype in human cancers. His influential machine learning approaches have reshaped researchers’ analysis of gene regulation. These approaches include the genome annotation method Segway, which enables simple interpretation of multivariate genomic data. He is a Senior Scientist in and Chair of the Computational Biology and Medicine Program, 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.
Website: http://hoffmanlab.org/