Marzia Angela Cremona

Chair in Statistical Learning 2025-2030

Marzia A. Cremona is an Associate Professor in the Department of Operations and Decision Systems (Faculty of Business Administration) at Université Laval and a researcher in the Population Health and Optimal Health Practices axis at the CHU de Québec Research Center. She is a member of the Institute for Intelligence and Data (IID), the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), and the Center for Medical Genomics at Pennsylvania State University (USA).

She earned her PhD in Mathematical Models and Methods for Engineering from Politecnico di Milano (Italy) in 2016. Before joining Université Laval in 2019, she worked at Pennsylvania State University (USA). Her multidisciplinary research focuses on machine learning and applied statistics. Her work, funded by several agencies including NSERC, FRQS, and SSHRC, has been published in 22 peer-reviewed articles and presented in over 60 oral presentations. In recent years, she has developed and taught courses on machine learning in business administration and finance, as well as introductory courses in simulation and data visualization.

The Chair in Statistical Learning focuses on the development of statistical and machine learning methods for complex data in the biomedical and social sciences. In particular, it emphasizes functional data, which vary along a continuum and can be represented as curves or surfaces. The overall goal is to extend the application of functional data analysis techniques in AI to produce methods grounded in solid statistical foundations that can extract relevant information from such data across multiple domains.

The chair’s research program is organized around four main axes aimed at broadening the application of functional data analysis methods in the biomedical and social sciences. Axes 1 and 2 focus on developing supervised and unsupervised learning methods for functional data, applicable across various domains, while axes 3 and 4 concentrate on leveraging AI methods to address specific problems in the biomedical and social sciences.