Check my full CV for more information.
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.
Crayton A, Fonseca J, Mehra K, Ng M, Ross J, Sandoval-Castañeda M, von Gnecht R. (2021). Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse, in IJCAI 2021 Workshop on AI for Social Good.
Fonseca, J., Douzas, G., Bacao, F. (2021). Improving Imbalanced Land Cover Classification with K-Means SMOTE: Detecting and Oversampling Distinctive Minority Spectral Signatures. Information, 12(7), 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), 2619.
Fonseca, J., & Bacao, F. (2022). Research Trends and Applications of Data Augmentation Algorithms. arXiv preprint arXiv:2207.08817.
Fonseca, J., & Bacao, F. (2023). Improving Active Learning Performance through the Use of Data Augmentation. International Journal of Intelligent Systems, 2023.
Fonseca, J., & Bacao, F. (2023). Geometric SMOTE for imbalanced datasets with nominal and continuous features. Expert Systems with Applications, 234, 121053.
Fonseca, J., & Bacao, F. (2023). Tabular and latent space synthetic data generation: a literature review. Journal of Big Data, 10(1), 115.
Fonseca, J.*, Bell, A.*, Abrate, C., Bonchi, F., & Stoyanovich, J. (2023). Setting the Right Expectations: Algorithmic Recourse Over Time. In Equity and Access in Algorithms, Mechanisms, and Optimization (pp. 1-11).
Bell, A.*, Fonseca, J.*, Abrate, C., Bonchi, F., & Stoyanovich, J. (2024). Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity. arXiv preprint arXiv:2401.16088.
Bell, A.*, Fonseca, J.*, & Stoyanovich, J. (2024). The Game Of Recourse: Simulating Algorithmic Recourse over Time to Improve Its Reliability and Fairness. In SIGMOD/PODS'24: Companion of the 2024 International Conference on Management of Data. ACM.
Pliatsika, V.*, Fonseca, J.*, Akhynko, K., Shevchenko, I., & Stoyanovich, J. (2025). ShaRP: Explaining Rankings with Shapley Values. arXiv preprint arXiv:2401.16744. (Accepted at VLDB 2025)
Fonseca, J.*, Bell, A.*, & Stoyanovich, J. (2025). SAFENUDGE: Safeguarding Large Language Models in Real-time with Tunable Safety-Performance Trade-offs. arXiv preprint arXiv:2501.02018. (Submitted to EMNLP 2025 — awaiting meta-review)
Hwang, H., Bell, A., Fonseca, J., Pliatsika, V., Stoyanovich, J., & Whang, S. E. (2025). SHAP-based Explanations are Sensitive to Feature Representation. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (pp. 1588-1601).
Talk at NOVA School of Business and Economics, Carcavelos, Portugal
Debate Panel at Universidade NOVA de Lisboa, Lisbon, Portugal
Talk at Online Event, no physical location
Talk at Online Event, no physical location
Paper presentation at Online Event, no physical location
Paper presentation at , Boston, USA
Demo paper presentation at , Santiago, Chile
Congressional Demo at U.S. Capitol, Washington, D.C., USA
Educational Tool Demo at NYU Tandon School of Engineering, Brooklyn, NY, USA