معرفی
Dan Lizotte is an Associate Professor with appointments in the Department of Computer Science and the Department of Epidemiology and Biostatistics at Western University. He is also affiliated with the Schulich Interfaculty Program in Public Health, cross-appointed to the Department of Statistics and Actuarial Sciences, and serves as Associate Faculty in the Rotman Institute of Philosophy. His work bridges computer science, statistics, and public health, focusing on the application of advanced computational methods to healthcare decision-making.
Dr. Lizotte earned his BCS from New Brunswick and completed his MSc and PhD in Computer Science at Alberta. His educational background in computer science forms the foundation for his interdisciplinary research that applies machine learning and statistical techniques to health data.
Professor Lizotte's research aims to adapt and improve reinforcement learning, machine learning, and statistical techniques for application to health data, providing stakeholders with evidence for non-myopic health decision making. He is particularly interested in problems involving multiple outcomes, causal inference, and outlier detection in public health and primary health care. His work spans artificial intelligence, biostatistics, and public health, with a growing focus on health equity and intersectionality in AI applications.
Analysis of Dr. Lizotte's recent publications reveals a strong focus on applying machine learning and statistical methods to healthcare challenges. His work spans predictive modeling for chronic diseases, AI implementation in primary care, intersectionality in health research, and reinforcement learning for decision support. Recent trends show increasing attention to health equity, the application of AI to marginalized populations, and methodological innovations in handling complex health data with multiple outcomes.
Dr. Lizotte has secured significant research funding including:
- Machine learning methodology for sequential decision support from largescale longitudinal data (NSERC; 2018-2024)
- Reinforcement Learning Methodology for Decision Analysis and Support in Long-term Care (NSERC; 2021-2022)
- Beyond Supervised Learning: Artificial Intelligence Tools to Help Public Health Stakeholders Serve Marginalized Populations (CIHR; 2019-2023)
- Artificial Intelligence for Public Health (AI4PH) Training Platform [co-Applicant with lead Dr. Laura Rosella] (CIHR; 2022-2026)
Dr. Lizotte leads the Biostatistical & Computational Methods research cluster, which focuses on developing and applying advanced statistical and computational techniques to health data. His team works at the intersection of computer science, statistics, and public health to create decision support tools that can handle complex, real-world healthcare scenarios with multiple outcomes and considerations.
