Project Description
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RESEARCH AREAS:
Artificial Intelligence and machine learning Algorithmic Fairness - AI application in healthcare
CONTACT:
Rotman Institute of Philosophy
Western University
Western Interdisciplinary Research Building
London, Ontario, Canada
N6A 3K7
Kaitlyn Wade
Doctoral Student
Department of Computer Science, Western University
Kaitlyn Wade is a PhD student in the Department of Computer Science at Western University and is supervised by Dr. Dan Lizotte. With undergraduate degrees in Genetics & Biochemistry and Bioinformatics, she developed a deep interest in interdisciplinary research, particularly at the intersection of AI and healthcare. Her research focuses on fair representation learning for genetic and electronic health record data, modelling multimorbidity, and exploring fairness in sequential healthcare decision-making. Kaitlyn is passionate about promoting equity and inclusion in STEM and is actively involved in mentorship and community outreach activities. She is a mentor with the Graduate Sisters in Science program, serves on the Faculty of Science EDIDA Committee, and organizes the Retiring with Strong Minds seminar series, which provides graduate students with opportunities to share their research with older adults in the London community. In 2025, she received the DRI EDIA Champions Award from the Digital Research Alliance of Canada, through which she led workshops introducing equity-deserving researchers across Canada to high-performance computing and AI. She currently holds a Canadian Institutes of Health Research (CIHR) Doctoral Scholarship
Kaitlyn’s research is supervised by Dr. Dan Lizotte and lies at the intersection of artificial intelligence, healthcare, and equity. Her work focuses on algorithmic fairness and on how machine learning models can be designed and evaluated to support equitable decision-making. She is particularly interested in fair representation learning for genetic and electronic health record data and in fairness in sequential clinical decision-making. A central goal of her research is to understand how different formal definitions of fairness shape model behaviours and downstream outcomes, especially for historically underserved populations.