Frank Rudzicz

BSc, Meng, PhD



Frank Rudzicz is a scientist at the Li Ka Shing Knowledge Institute at St Michael’s Hospital, Director of Artificial Intelligence at Surgical Safety Technologies Inc., an associate professor of Computer Science at the University of Toronto, co-founder and President of WinterLight Labs Inc., faculty member at the Vector Institute for Artificial Intelligence, past-President of the international joint ACL/ISCA special interest group on Speech and Language Processing for Assistive Technologies, and past-Chair of the Standards Council of Canada’s subcommittee on Artificial Intelligence, and CIFAR Chair in Artificial Intelligence. He is the recent recipient of the Young Investigator award from the Alzheimer’s Society of Canada, the Early Researcher award from the Government of Ontario, the Excellence in Applied Research award from the National Speech-Language & Audiology Canada, and the Connaught Innovation Award.

His work is in machine learning in healthcare, normally applied to natural language processing and speech recognition, including automated cognitive assessments, assistive technologies, and AI for surgical safety. His research has appeared in popular media such as Scientific American, Wired, CBC, and the Globe and Mail, and in scientific press such as Nature.

Recent Publications

  1. Kostas, D, Aroca-Ouellette, S, Rudzicz, F. BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data. Front Hum Neurosci. 2021;15 :653659. doi: 10.3389/fnhum.2021.653659. PubMed PMID:34248521 PubMed Central PMC8261053.
  2. Hung, PS, Noorani, A, Zhang, JY, Tohyama, S, Laperriere, N, Davis, KD et al.. Regional brain morphology predicts pain relief in trigeminal neuralgia. Neuroimage Clin. 2021;31 :102706. doi: 10.1016/j.nicl.2021.102706. PubMed PMID:34087549 PubMed Central PMC8184658.
  3. Balagopalan, A, Eyre, B, Robin, J, Rudzicz, F, Novikova, J. Comparing Pre-trained and Feature-Based Models for Prediction of Alzheimer's Disease Based on Speech. Front Aging Neurosci. 2021;13 :635945. doi: 10.3389/fnagi.2021.635945. PubMed PMID:33986655 PubMed Central PMC8110916.
  4. Yeung, AY, Roewer-Despres, F, Rosella, L, Rudzicz, F. Machine Learning-Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation. J Med Internet Res. 2021;23 (4):e26628. doi: 10.2196/26628. PubMed PMID:33844636 PubMed Central PMC8074952.
  5. Hussein, T, Chauhan, PK, Dalmer, NK, Rudzicz, F, Boger, J. Exploring interface design to support caregivers' needs and feelings of trust in online content. J Rehabil Assist Technol Eng. ;7 :2055668320968482. doi: 10.1177/2055668320968482. PubMed PMID:33343921 PubMed Central PMC7727054.
  6. Robin, J, Harrison, JE, Kaufman, LD, Rudzicz, F, Simpson, W, Yancheva, M et al.. Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations. Digit Biomark. ;4 (3):99-108. doi: 10.1159/000510820. PubMed PMID:33251474 PubMed Central PMC7670321.
  7. Bensoussan, Y, Pinto, J, Crowson, M, Walden, PR, Rudzicz, F, Johns, M 3rd et al.. Deep Learning for Voice Gender Identification: Proof-of-concept for Gender-Affirming Voice Care. Laryngoscope. 2021;131 (5):E1611-E1615. doi: 10.1002/lary.29281. PubMed PMID:33219707 .
  8. Kostas, D, Rudzicz, F. Thinker invariance: enabling deep neural networks for BCI across more people. J Neural Eng. 2020;17 (5):056008. doi: 10.1088/1741-2552/abb7a7. PubMed PMID:32916675 .
  9. Abdalla, M, Abdalla, M, Hirst, G, Rudzicz, F. Exploring the Privacy-Preserving Properties of Word Embeddings: Algorithmic Validation Study. J Med Internet Res. 2020;22 (7):e18055. doi: 10.2196/18055. PubMed PMID:32673230 PubMed Central PMC7391163.
  10. Abdalla, M, Abdalla, M, Rudzicz, F, Hirst, G. Using word embeddings to improve the privacy of clinical notes. J Am Med Inform Assoc. 2020;27 (6):901-907. doi: 10.1093/jamia/ocaa038. PubMed PMID:32388549 PubMed Central PMC7309261.
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