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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
portfolio
publications
Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances
Parras, J., Apellániz, P. A., & Zazo, S. (2021). Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances. Sensors, 21(15), 5011.
An online learning algorithm to play discounted repeated games in wireless networks
Parras, J., Apellániz, P. A., & Zazo, S. (2022). An online learning algorithm to play discounted repeated games in wireless networks. Engineering Applications of Artificial Intelligence, 107, 104520.
Multi-modal analysis and federated learning approach for classification and personalized prognostic assessment in myeloid neoplasms
D'Amico, S., Dall'Olio, L., Rollo, C., Alonso, P., Prada-Luengo, I., Dall'Olio, D., ... & Gastone, C. (2022). Multi-modal analysis and federated learning approach for classification and personalized prognostic assessment in myeloid neoplasms.
Inverse Reinforcement Learning: A New Framework to Mitigate an Intelligent Backoff Attack
Parras, J., Almodóvar, A., Apellániz, P. A., & Zazo, S. (2022). Inverse reinforcement learning: a new framework to mitigate an Intelligent Backoff Attack. IEEE Internet of Things Journal, 9(24), 24790-24799.
MOSAIC: an artificial intelligence–based framework for multimodal analysis, classification, and personalized prognostic assessment in rare cancers
D'Amico, S., Dall'Olio, L., Rollo, C., Alonso, P., Prada-Luengo, I., Dall'Olio, D., ... & Castellani, G. (2024). MOSAIC: an artificial intelligence–based framework for multimodal analysis, classification, and personalized prognostic assessment in rare cancers. JCO Clinical Cancer Informatics, 8, e2400008.
Synthetic tabular data validation: A divergence-based approach
Apellániz, P. A., Jiménez, A., Galende, B. A., Parras, J., & Zazo, S. (2024). Synthetic tabular data validation: A divergence-based approach. IEEE Access.
Deep Learning as a New Framework for Passive Vehicle Safety Design Using Finite Elements Models Data
Lahoz Navarro, M., Jehle, J. S., Apellániz, P. A., Parras, J., Zazo, S., & Gerdts, M. (2024). Deep Learning as a New Framework for Passive Vehicle Safety Design Using Finite Elements Models Data. Applied Sciences, 14(20), 9296.
Leveraging the variational Bayes autoencoder for survival analysis
Apellániz, P. A., Parras, J., & Zazo, S. (2024). Leveraging the variational Bayes autoencoder for survival analysis. Scientific Reports, 14(1), 24567.
CR-SAVAE: A Parametric Method for Survival Analysis with Competing Risks
Apellániz, P. A., Parras, J., & Zazo, S. (2024, August). CR-SAVAE: A Parametric Method for Survival Analysis with Competing Risks. In 2024 32nd European Signal Processing Conference (EUSIPCO) (pp. 1526-1530). IEEE.
An improved tabular data generator with VAE-GMM integration
Apellániz, P. A., Parras, J., & Zazo, S. (2024, August). An improved tabular data generator with VAE-GMM integration. In 2024 32nd European Signal Processing Conference (EUSIPCO) (pp. 1886-1890). IEEE.
Data Driven Research through the European RADeep Registry and the Use of Artificial Intelligence Towards Personalized Medicine in Sickle Cell Disease
Gimbert, A. C., Reidel, S., de Apellániz, P. A., Alvarez, F., Galende, B. A., Beneitez, D., ... & del Mar Mañú-Pereira, M. (2024). Data Driven Research through the European RADeep Registry and the Use of Artificial Intelligence Towards Personalized Medicine in Sickle Cell Disease. Blood, 144, 1138.
An Artificial Intelligence-Based Federated Learning Platform to Boost Precision Medicine in Rare Hematological Diseases: An Initiative By GenoMed4all and Synthema Consortia
Asti, G., D'Amico, S., Carota, L., Piscia, D., Casadei, F., Merleau, N. S. C., ... & Alvarez, F. (2024). An Artificial Intelligence-Based Federated Learning Platform to Boost Precision Medicine in Rare Hematological Diseases: An Initiative By GenoMed4all and Synthema Consortia. Blood, 144, 4989.
GenoMed4All, a Federated Learning Platform for Clinical and Omics Data
Piscia, D., Apellániz, P. A., Arroyo, B., Barrio, S., Moreno, F., Parras, J., ... & Beltran, S. (2024, December). GenoMed4All, a federated learning platform for clinical and omics data. In EUROPEAN JOURNAL OF HUMAN GENETICS (Vol. 32, pp. 1640-1640). CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND: SPRINGERNATURE.
Survival Model Optimization via Federated Learning: A Study Combining Simulations and Experiments
Casadei, F., Carota, L., Asti, G., D’Amico, S., Piscia, D., Zazo, S., ... & Giampieri, E. (2024, December). Survival Model Optimization via Federated Learning: A Study Combining Simulations and Experiments. In 2024 IEEE International Conference on Big Data (BigData) (pp. 7658-7667). IEEE.
Improving Synthetic Data Generation through Federated Learning in Scarce and Heterogeneous Data Scenarios
Apellániz, P. A., Parras, J., & Zazo, S. (2025). Improving synthetic Data Generation through Federated Learning in scarce and heterogeneous data scenarios. Big Data and Cognitive Computing, 9(2), 18.
Membership Inference Attacks and Differential Privacy: a study within the context of Generative Models
Galende, B. A., Apellániz, P. A., Parras, J., Zazo, S., & Uribe, S. (2025). Membership Inference Attacks and Differential Privacy: a study within the context of Generative Models. IEEE Open Journal of the Computer Society.
Development, implementation, and validation of an open-source Federated Learning platform to accelerate innovation and boost personalized medicine in rare and ultra-rare haematological diseases: an initiative by GenoMed4All Consortium
Asti, G., Apellániz, P. A., Carota, L., Casadei, F., Piscia, D., Delleani, M., ... & Álvarez Garcia, F. (2025). Development, implementation, and validation of an open-source Federated Learning platform to accelerate innovation and boost personalized medicine in rare and ultra-rare haematological diseases: an initiative by GenoMed4All Consortium. medRxiv, 2025-08.
Deep Generative Models Meet Federated Learning: A Healthcare-Centered Review
Ceresi, A., Galende, B. A., Guinea-Pérez, J., Apellániz, P. A., Hernández-Peñaloza, G., & Álvarez, F. (2025). Deep Generative Models Meet Federated Learning: A Healthcare-Centered Review. Authorea Preprints.
Interpretable Clinical Classification with Kolmogorov-Arnold Networks
Almodóvar, A., Apellániz, P. A., Garrido, A., Fernández-Salvador, F., Zazo, S., & Parras, J. (2025). Interpretable clinical classification with Kolmogorov-Arnold networks. arXiv preprint arXiv:2509.16750.
CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks
Almodóvar, A., Apellániz, P. A., Zazo, S., & Parras, J. (2025). CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks. arXiv preprint arXiv:2509.22467.
Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks
Garrido, A., Almodóvar, A., Apellániz, P. A., Parras, J., & Zazo, S. (2025). Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks. arXiv preprint arXiv:2507.07804.
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios
Patricia A. Apellániz, Ana Jiménez, Borja Arroyo Galende, Juan Parras, Santiago Zazo, Artificial inductive bias for synthetic tabular data generation in data-scarce scenarios, Neurocomputing, Volume 652, 2025, 131122, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2025.131122.
Development and validation of synthetic data generation over a federated learning computing framework to accelerate innovation and boost personalized medicine in hematological diseases
Asti, G., Delleani, M., Apellániz, P., Isasa, I., Martinez Duarte, D., Arroyo Galende, B., ... & Della Porta, M. (2025). Development and validation of synthetic data generation over a federated learning computing framework to accelerate innovation and boost personalized medicine in hematological diseases.
Optimizing AI models for haematological malignancies with federated learning: simulations and experiments
Carota, L., Casadei, F., Asti, G., Piscia, D., Biondi, R., Sala, C., ... & Castellani, G. (2026). Optimizing AI models for haematological malignancies with federated learning: simulations and experiments. Physica Medica: European Journal of Medical Physics, 142.
Advancing Cancer Research with Synthetic Data Generation in Low-Data Scenarios
Apellániz, P. A., Galende, B. A., Jiménez, A., Parras, J., & Zazo, S. (2025). Advancing Cancer Research with Synthetic Data Generation in Low-Data Scenarios. IEEE Journal of Biomedical and Health Informatics.
Enhancing survival analysis through federated learning in non-IID and scarce data scenarios
Apellániz, P. A., Parras, J., & Zazo, S. (2026). Enhancing survival analysis through federated learning in non-IID and scarce data scenarios. Computers in Biology and Medicine, 204, 111558.
Kolmogorov-Arnold causal generative models
Almodóvar, A., Elizo, M., Apellániz, P. A., Zazo, S., & Parras, J. (2026). Kolmogorov-Arnold causal generative models. arXiv preprint arXiv:2603.20184.
Experimenting Federated AI Models for Hematological Diseases
Piscia, D., Apellániz, P. A., D'Amico, S., Biondi, R., Sala, C., Merleau, N. S., ... & Giampieri, E. (2026, March). Experimenting Federated AI Models for Hematological Diseases. In Artificial Intelligence for Biomedical Data: First International Workshop, AIBio 2025, Held in Conjunction with the European Conference on Artificial Intelligence, ECAI 2025, Bologna, Italy, October 25–26, 2025, Proceedings (p. 17). Springer Nature.
talks
Talk 1 on Relevant Topic in Your Field
Talk at UC San Francisco, Department of Testing, San Francisco, California
Tutorial 1 on Relevant Topic in Your Field
Tutorial at UC-Berkeley Institute for Testing Science, Berkeley CA, USA
Talk 2 on Relevant Topic in Your Field
Talk at London School of Testing, London, UK
Conference Proceeding talk 3 on Relevant Topic in Your Field
Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA
teaching
Teaching experience 1
Undergraduate course at University 1, Department, City, Country
Teaching experience 2
Workshop at University 1, Department, City, Country