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Teaching Staff

Biography

Carina Albuquerque has been collaborating with NOVA Information Management School in the field of Machine Learning and Data Mining since February 2017. She holds a PhD in Information Management, a Master's degree in Advanced Analytics from NOVA IMS, and during her studies, she actively collaborated with the Champalimaud Foundation in implementing convolutional neural networks for image detection and classification. Graduated in Information Systems and Technologies from NOVA IMS, pursued also a post-graduate degree in Marketing & Business Intelligence from the European University. Her research interests primarily revolve around the exploration of Machine Learning and Deep Learning techniques, focused on understanding their potential in solving complex real-world problems across various domains. In particular, her research is dedicated to the development, implementation, and application of these methods. It also occasionally integrates research projects associated with NOVA IMS and provides consulting services in machine learning and information management.

Scientific Publications

Henriques, R., Oliveira, L., Santos, R., & Albuquerque, C. (2023)

Implementing Team-Based Learning In Data Science Education: Enhancing Student Satisfaction And Performance. In L. Gómez Chova, C. González Martínez, & J. Lees (Eds.), 15th  International Conference on Education and New Learning Technologies July 3rd  -5th  , 2023 Palma, Spain (pp. 6720-6729). (EDULEARN23 Proceedings; No. 2023). IATED Academy. https://doi.org/10.21125/edulearn.2023.1770

Albuquerque, C., Henriques, R., & Castelli, M. (2022)

A stacking-based artificial intelligence framework for an effective detection and localization of colon polyps. Scientific Reports, 12, 1-12. [17678]. https://doi.org/10.21203/rs.3.rs-1862362/v1, https://doi.org/10.1038/s41598-022-21574-w

Albuquerque, C., Vanneschi, L., Henriques, R., Castelli, M., Póvoa, V., Fior, R., & Papanikolaou, N. (2021)

Object detection for automatic cancer cell counting in zebrafish xenografts. PLoS ONE, 16(11), 1-28. [e0260609]. https://doi.org/10.1371/journal.pone.0260609