Publications
Below is a full list of my publications. For an always up-to-date list, visit my Google Scholar profile.
Hjikakou, K., Cardenas-Cartagena, J., & Sabatelli, M. (2026). On the Generalisation of Koopman Representations for Chaotic System Control. Northern lights deep learning conference 2026.
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Assiotis, N., Hau, R., Oldenburg, V., Verbiest, R., Koellermeier, J., Sabatelli, M., & Cárdenas-Cartagena, J. (2025). Physics-Informed Graph Neural Networks for Air Pollution Forecasting in the Netherlands. ECAI workshop on machine learning meets differential equations: From theory to applications. PMLR, 47–70.
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Kamsteeg, I., Cardenas-Cartagena, J., Beers, F. van, Tashu, T. M., & Valdenegro-Toro, M. (2025). Confidence Calibration in Large Language Model-Based Entity Matching. Proceedings of the 2nd workshop on uncertainty-aware NLP (UncertaiNLP 2025), 120–137.
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Lende, M. van der, Sabatelli, M., & Cardenas-Cartagena, J. (2025). Interpretable Function Approximation with Gaussian Processes in Value-Based Model-Free Reinforcement Learning. Northern lights deep learning conference 2025.
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Müller, A., Cardenas-Cartagena, J., & Pollice, R. (2025). Uncovering Internal Prediction Mechanisms of Transformer-Based Chemical Foundation Models.
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O’Cuilleanain, T., Cardenas-Cartagena, J., & Sabatelli, M. (2025). Adversarial Attacks Through Value-Guided Transition Modeling in Deep Reinforcement Learning. Northern lights deep learning conference abstracts 2026.
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Todorov, A., Cardenas-Cartagena, J., Cunha, R. F., Zullich, M., & Sabatelli, M. (2025). Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning. Transactions on Machine Learning Research.
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Cardenas-Cartagena, J., Falzari, M., Zullich, M., & Sabatelli, M. (2024). Upside-down Reinforcement Learning for More Interpretable Optimal Control. arXiv preprint arXiv:2411.11457.
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Oldenburg, V., Cardenas-Cartagena, J., & Valdenegro-Toro, M. (2024). Forecasting Smog Clouds with Deep Learning: A Proof-of-Concept. ICML 2024 AI for science workshop.
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Cardenas-Cartagena, J., Beferull-Lozano, B., Elnourani, M., & Romero, D. (2022). Risk-Aware Particle Filtering for State Estimation in Recirculating Aquaculture Systems. 2022 asilomar conference on signals, systems, and computers, 829–833.
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Cardenas-Cartagena, J., Elnourani, M., & Beferull-Lozano, B. (2022). Forecasting Aquaponic Systems Behaviour with Recurrent Neural Networks Models. Proceedings of the northern lights deep learning workshop.
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Hosamo Hosamo, H., Imran, A., Cardenas-Cartagena, J., Ragnar Svennevig, P., Svidt, K., & Kofoed Nielsen, H. (2022). A Review of the Digital Twin Technology in the AEC-FM Industry. Advances in Civil Engineering, 2022, 17.
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Aguirre-Zapata, E., Cardenas-Cartagena, J., & Garcia-Tirado, J. (2017). Glycemic Monitoring in Critical Care Using Nonlinear State Estimators. IFAC-PapersOnLine, 50(1), 4430–4435.
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Cardenas-Cartagena, J., & Velásquez, V. H. J. (2017). Estimación de Posición En Robots móviles Usando Filtros de Partı́culas. Revista Politécnica, 13(25), 103–113.
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Torres, M. S., Cardenas-Cartagena, J., Arenas, L. A., Quintero, P. A., & Torres, V. R. (2017). Cocreation Laboratory in Health: Materialization Tool for Innovation Process in Colombian Public Hospitals. 2017 congreso internacional de innovacion y tendencias en ingenieria (CONIITI), 1–6.
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Torres-Montoya, S., Cardenas-Cartagena, J., Torres-Villa, R., & Quintero-Posada, Á. (2017). Laboratorio de Cocreación En Salud. Encuentro internacional de educación en ingenierı́a ACOFI.
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