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Michael Brudno

Michael Brudno

Appointment

Canada CIFAR AI Chair

Pan-Canadian AI Strategy

Connect

University of Toronto

Google Scholar

About

Michael Brudno is a Canada CIFAR AI Chair at the Vector Institute, a professor in the School of Computer Science at the University of Toronto, and the chief data scientist at the UHN.

Brudno’s work focuses on the capture of structured phenotypic data from clinical encounters, using both refined user interfaces, and mining of unstructured data (based on machine learning methodology), and the analysis of omics data (genome, transcriptome, epigenome) in the context of the structured patient phenotypes, mostly for rare diseases. 

His overall research goal is to enable the seamless automated analysis of patient omics data based on automatically captured information from a clinical encounter, thus streamlining clinical workflows and enabling faster and better treatments.

Awards

  • Alfred P. Sloan Research Fellow, 2010-2012
  • Ontario Early Researcher Award (ERA), 2009-2014
  • European Conference in Computer Systems (Eurosys) Best Paper Award, 2009
  • Canada Research Chair (CRC) in Computational Biology, 2006-2011; 2011-2016
  • Intelligent Systems in Molecular Biology (ISMB) Best Paper Award, 2004

Relevant Publications

  • Skreta, M., Arbabi, A., Wang, J., & Brudno, M. (2020). Training without training data: Improving the generalizability of automated medical abbreviation disambiguation. In Machine Learning for Health Workshop (pp. 233-245). PMLR.

  • Chang, W. H., Mashouri, P., Lozano, A. X., Johnstone, B., Husić, M., Olry, A., … & Brudno, M. (2020). Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes. Genetics in Medicine, 1-10.

  • Wang, J., Xiao, X., Wu, J., Ramamurthy, R., Rudzicz, F., & Brudno, M. (2020). Speaker attribution with voice profiles by graph-based semi-supervised learning. Proc. Interspeech 2020, 289-293.

  • Arbabi, A., Adams, D. R., Fidler, S., & Brudno, M. (2019). Identifying clinical terms in medical text using Ontology-Guided machine learning. JMIR medical informatics, 7(2), e12596.

Institution

University Health Network (UHN)

University of Toronto

Vector Institute

Department

School of Computer Science

Education

  • PhD (Computer Science), Stanford University
  • MSc (Computer Science), Stanford University
  • BA (Computer Science and History), University of California, Berkeley

Country

Canada

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