Ayşe Başar is a Professor in the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University. She serves as Director of the Data Science Lab and Program Director of the Data Science Masters Program. Her research integrates machine learning and Bayesian methods to build predictive models and recommender systems, with applications in software engineering and healthcare. BSc, Boğaziçi University MASc, University of Alabama in Huntsville PhD, London School of Economics Her research focuses on advancing machine learning through Bayesian inference, deep reinforcement learning, and explainability. She develops models that simulate expert decision-making, particularly in complex environments like emergency rooms and software development. Her work emphasizes uncertainty quantification , social learning in networks , and evidence-based decision support systems . The selected publications reflect a strong trend in applying machine learning to real-world problems—particularly in software engineering defect prediction, healthcare team dynamics, and social network analysis. Her work consistently uses probabilistic modeling and ensemble methods to improve prediction accuracy and interpretability. Paper among top 3 most cited in Empirical Software Engineering Journal (2009–2014) Best Paper Award, AAAI (2010) Best Paper Award, IFIP/IEEE IM Symposium (2019) Best Paper Award, ESEM (2013) Dr. Başar actively supervises students and leads impactful research collaborations with institutions like St. Michael’s Hospital and IBM. She has held leadership roles including Director of Big Data for the Provost’s Office and Program Director for Data Analytics Certificates. She is an affiliated scientist at St. Michael’s Hospital and a research fellow at IBM CAS. She leads the Data Science Lab , which focuses on developing advanced machine learning models for complex decision-making. The lab works on projects involving large-scale data integration, predictive analytics, and human-in-the-loop AI systems.
