Michael Brudno
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2020
Michael Brudno is a Computer Scientist specializing in Computational methods for the analysis of Medical data (Computational Medicine), working as a Professor of Computer Science at the University of Toronto and the Chief Data Scientist for the University Health Network (UHN). He is also a faculty member at the Vector Institute for Artificial Intelligence and the Scientific Director of HPC4Health, a private computing cloud for Ontario hospitals.
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
- Singh, D., Nagaraj, S., Mashouri, P., Drysdale, E., Fischer, J., Goldenberg, A., & Brudno, M. (2022). Assessment of Machine Learning-Based Medical Directives to Expedite Care in Pediatric Emergency Medicine. JAMA network open, 5(3).
- Wang, J., Yang, J., Zhang, H. et al. PhenoPad: Building AI enabled note-taking interfaces for patient encounters. npj Digit. Med. 2022; 5(12).
- Dursi, LD., Bozoky Z., de Borja R., et al. CanDIG: Federated network across Canada for multi-omic and health data discovery analysis. Cell Genomics. 2021; 1(100033).
- Skreta, M., Arbabi, A., Wang, J. et al. Automatically disambiguating medical acronyms with ontology-aware deep learning. Nat Commun. 2021; 12(5319).
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.