Education
BSc in Statistics, University of Barcelona and Polytechnic University of Catalonia, 2018
Msc in Statistics and Operations Research, Polytechnic University of Catalonia and University of Barcelona, 2020
PhD in Artificial Intelligence, Polytechnic University of Catalonia and Technology Center of Catalonia, 2025
Titles and Appointments
Postdoctoral Researcher
Address
410 W 10th Street
Indianapolis, IN 46202
Research Keywords
Responsible AI, brain tumor classification, survival analysis, radiology, digital pathology
Links
Google Scholar
LinkedIn
GitHub
Research Summary/Bio
Carla Pitarch Abaigar is a postdoctoral researcher in the Division of Computational Pathology. She holds inter-university bachelor’s and master’s degrees in statistics from the Polytechnic University of Catalonia (UPC) and Universitat de Barcelona (UB). Prior to her current role, Carla spent three years as a biostatistician at the European Foundation for the Study of Chronic Liver Failure, where she developed her expertise in data management, survival analysis and bioinformatics. She later joined the Technology Center of Catalonia as a digital health researcher, embarking on an industrial PhD in artificial intelligence in collaboration with the UPC. Her research focused on developing a robust AI-driven system for glioma grading using radiological imaging. During this time, Carla also worked as an assistant professor at the UPC, teaching statistics. Her research interests lie in advancing robust, transparent and responsible AI methods for health care applications.
Featured publications
- Subías-Beltrán, P., Pitarch, C., Migliorelli, C., Marte, L., Galofré, M., & Orte, S. (2024). The Role of Transparency in AI-Driven Technologies: Targeting Healthcare. IntechOpen. DOI: 10.5772/intechopen.1007444
- Pitarch, C., Ungan, G., Julià-Sapé, M., & Vellido, A. (2024). Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology. Cancers, 16(2), 300. DOI: 10.3390/cancers16020300
- Pitarch, C., Ribas, V., & Vellido, A. (2023). AI-Based Glioma Grading for a Trustworthy Diagnosis: An Analytical Pipeline for Improved Reliability. Cancers, 15(13), 3369. DOI: 10.3390/cancers15133369