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Teaching Staff


João Fonseca will soon complete his PhD at NOVA IMS, during which he worked with Professor Fernando Bação on synthetic data generation, focused on Imbalanced Learning and active learning, with applications on Land Use/Land Cover classification tasks. His PhD was funded with an MIT Portugal PhD Grant (2020 FCT-MPP2030). Recently, he worked as a Research Intern at New York University, Tandon School of Engineering, at the Center for Responsible AI, collaborating with Professor Julia Stoyanovich on multi-agent Algorithmic Recourse over time. In the past, he conducted research on Land Use/Land Cover classification methods to automatically update LULC maps of the Portuguese mainland. His work included the development of pipelines to systematize the preprocessing of Sentinel-2 satellite imagery for any given period of time. He also developed and deployed different types of algorithms for various tasks, such as data filtering, dimensionality reduction, feature extraction and classification. João Fonseca was also a volunteer at DSSG Solve, where he integrated a team of 4 data scientists. Their project focused on leveraging Twitter discourse to characterize narratives and identify unmet needs in response to Cyclone Amphan, which affected 18 million people around the bay of Bengal in May 2020. His previous work also includes the study of the potential of Big data in tourism management, at NOVA School of Business and Economics. He completed a Msc in Information Management at NOVA Information Management School and a Msc in Management at NOVA School of Business and Economics.

Scientific Publications

Fonseca, J., & Bacao, F. (2023)

Geometric SMOTE for imbalanced datasets with nominal and continuous features. Expert Systems with Applications, [121053].

Fonseca, J., & Bação, F. (2023)

Improving Active Learning Performance through the Use of Data Augmentation. International Journal of Intelligent Systems, 2023, 1-17.

Fonseca, J., & Bacao, F. (2023)

Tabular and latent space synthetic data generation: a literature review. Journal of Big Data, 10, 1-37. [115].

Fonseca, J., Bell, A., Abrate, C., Bonchi, F., & Stoyanovich, J. (2023)

Setting the Right Expectations: Algorithmic Recourse Over Time. In Proceedings of 2023 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ’23) [29] ACM - Association for Computing Machinery.,

Crayton, A., Fonseca, J., Mehra, K., Ng, M., Ross, R., Sandoval-Castaneda, M., & von Gnechten, R. (2020)

Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse. Cornell University (ArXiv).

Fonseca, J., & Bação, F. (2022)

Research Trends and Applications of Data Augmentation Algorithms. (pp. 1-23). Cornell University (ArXiv).

Fonseca, J., Douzas, G., & Bacao, F. (2021)

Improving imbalanced land cover classification with k-means smote: Detecting and oversampling distinctive minority spectral signatures. Information (Switzerland), 12(7), 1-20. [266].

Fonseca, J., Douzas, G., & Bacao, F. (2021)

Increasing the effectiveness of active learning: Introducing artificial data generation in active learning for land use/land cover classification. Remote Sensing, 13(13), 1-20. [2619].

Douzas, G., Bacao, F., Fonseca, J., & Khudinyan, M. (2019)

Imbalanced learning in land cover classification: Improving minority classes' prediction accuracy using the geometric SMOTE algorithm. Remote Sensing, 11(24), [3040].