Michael 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.
- Ontario Early Researcher Award
- Sloan Fellowship
- Outstanding Young Canadian Computer Scientist Award
- Skreta, M., Arbabi, A., Wang, J., & Brudno, M. (2020, April). 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.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.