Dr. Kevin Dick
Adjunct Research Professor
- Ph.D.
- Email Dr. Kevin Dick
BIO
Dr. Kevin Dick (he/they) is an Investigator at the Children’s Hospital of Eastern Ontario Research Institute (CHEO-RI), Artificial Intelligence (AI) Data Scientist at BORN Ontario, and Adjunct Research Professor at Carleton University. He is the Principal Investigator of the Dick AI Lab, an interdisciplinary research lab advancing foundational AI methodology and applications in maternal-fetal and population health. Their principal areas of research focus on rare event prediction, the development of explainable AI (XAI) and privacy-preserving machine learning frameworks towards population-wide screening for early prediction of adverse pregnancy and neonatal outcomes.
Dr. Dick’s research bridges biomedical informatics, machine learning, and high-performance computing to design scalable, interpretable, and clinically integrated AI systems. Their work spans multimodal model development, health data interoperability, and the creation of next-generation AI infrastructures capable of linking provincial and national health data securely. He has pioneered frameworks that advance explainability, reproducibility, and transparency in population screening and medical AI.
As a mentor and collaborator, they supervise interdisciplinary teams of trainees, researchers, and engineers to advance theoretical AI methodology and develop real-world AI applications that enhance early screening, diagnosis, and patient outcomes. He is deeply committed to open science, equity in AI applications, and the responsible evolution of medical AI systems.
Dr. Dick earned his PhD in Biomedical Engineering and is the recipient of the Governor General’s Gold Medal. Their long-term vision is to build federated, ethically grounded AI ecosystems that transform how population health data is analyzed, shared, and acted upon to improve care across Canada and beyond.
Research Interests
- Rare event prediction
- Explainable AI (XAI)
- Privacy-preserving machine learning
- Multimodal models
- Health systems
- Equitable AI