Marc De Benedetti

Marc De Benedetti is an Assistant Professor, Teaching Stream in the Department of Computer Science at the University of Toronto's Mississauga campus within the Mathematical and Computational Sciences school. His work bridges computational methods with healthcare analytics, geophysical modeling, and educational innovation. He specializes in applying machine learning to medical datasets (e.g., SEER cancer data) for predictive modeling and has contributed to CAR T-cell therapy efficacy studies in oncology. His geoscience research focuses on spatial-temporal variability in climate systems, particularly in the Congo Basin and UK wind dynamics. De Benedetti also designs supplementary educational programs to support undergraduate STEM learners. His research emphasizes data-driven approaches to address challenges in healthcare, environmental science, and pedagogy.

Research interests include:

• Machine learning applications in healthcare survival prediction
• Immunotherapy comparative efficacy analysis
• Spatial variability in climate and hydrology systems
• Wind energy potential modeling
• Innovative undergraduate physics/mathematics tutoring strategies

His recent articles analyze treatment outcomes for blood cancers using CAR T-cell therapies, explore model resolution impacts on geophysical data accuracy, and develop predictive tools for cancer prognosis via SEER datasets. No scientific awards are explicitly mentioned in the provided materials. While no formal grants or student advisement records are listed, his work demonstrates strong engagement with both academic research and educational development initiatives.

His research locations include the Congo Basin for hydroclimatic studies and the UK for wind energy modeling. Collaborations likely span medical institutions for oncology studies and climatological agencies for environmental data analysis.