Juan Parras

Juan Parras

Doctor in Telecommunications Engineering, teaching at UPM and researching on applied Artificial Ingelligence and Signal Processing.

Publications

Asti, G., D’Amico, S., Carota, L., Piscia, D., Casadei, F., Merleau, N. S. C., … & Alvarez, F. (2024, November). An Artificial Intelligence-Based Federated Learning Platform to Boost Precision Medicine in Rare Hematological Diseases: An Initiative By GenoMed4all and Synthema Consortia. In 66th ASH Annual Meeting Abstracts, Blood, vol. 144, (pp. 4989-4990). Elsevier.

Gimbert, A. C., Reidel, S., de Apellániz, P. A., Alvarez, F., Galende, B. A., Beneitez, D., … & del Mar Mañú-Pereira, M. (2024, November). Data Driven Research through the European RADeep Registry and the Use of Artificial Intelligence Towards Personalized Medicine in Sickle Cell Disease. In 66th ASH Annual Meeting Abstracts, Blood, vol. 144, (pp. 1138-1140). Elsevier.

Apellániz, P. A., Parras, J., & Zazo, S. (2024). Leveraging the variational Bayes autoencoder for survival analysis. Scientific Reports.

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.

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.

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..

Ortiz-Toro, C.A., Cerrada-Collado, C., Moreno-Salinas, D., Chaos-García, D., García-Suárez, K.L., Otero, P., Vidal-Pérez, J.M, Luque-Nieto, M.A., Vázquez, A.I, Fraile-Ardanuy, J.J, Negro-Valdecantos, V., Jimenez-Yguacel, E., Aranda-Almansa, J., Zazo-Bello, S., José Zufiria, P. Magdalena, L., Parras, J., L. Gutiérrez, A. (2024, July). NauSim: An open source simulator for underwater drone control, development and deployment. In XLV Jornadas de Automática.

Apellániz, P. A., Jiménez, A., Arroyo Galende, B., Parras, J., & Zazo, S. (2024). Synthetic tabular data validation: a divergence-based approach. IEEE Access.

Jiménez, A., Merino, M. J., Parras, J., & Zazo, S. (2024). Explainable drug repurposing via path based knowledge graph completion. Scientific Reports.

Almodóvar, A., Parras, J., & Zazo, S. (2024). Propensity weighted federated learning for treatment effect estimation in distributed imbalanced environments. Computers in Biology and Medicine.

D’Amico, Saverio, et al (2024). MOSAIC: An Artificial Intelligence–Based Framework for Multimodal Analysis, Classification, and Personalized Prognostic Assessment in Rare Cancers. JCO Clinical Cancer Informatics.

Almodóvar, A., Parras, J., & Zazo, S. (2023, October). Federated learning for causal inference using deep generative disentangled models. In 1st Workshop on Deep Generative Models for Health, NeurIPS 2023.

Velasco, Pablo, et al (2023). The Relapsed Acute Lymphoblastic Leukemia Network (ReALLNet): A multidisciplinary project from the Spanish Society of Pediatric Hematology and Oncology (SEHOP). Frontiers in Pediatrics.

Parras, J., & Zazo, S. (2023, September). Negotiation strategies to improve distributed power allocation for self-organizing heterogeneous networks. In 2023 31st European Signal Processing Conference (EUSIPCO) (pp. 1743-1747). IEEE.

Sanz-Nogales, J.M, Parras, J., & Zazo, S. (2023). DDQN-based optimal targeted therapy with reversible inhibitors to combat the Warburg effect. Mathematical biosciences, 363, 109044.

Barreno, P., Parras, J., & Zazo, S. (2023). An efficient underwater navigation method using MPC with unknown kinematics and non-linear disturbances. Journal of Marine Science and Engineering. 2023, 11, 710.

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, vol 9, no. 24, pp.24790-24799, December 2022.

Pérez, M., Parras, J., Zazo, S., Álvarez, I. A. P. & Lluch, M. d. M. S. (2022). Using a Deep Learning Algorithm to Improve the Results Obtained in the Recognition of Vessels Size and Trajectory Patterns in Shallow Areas Based on Magnetic Field Measurements Using Fluxgate Sensors. IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 4, pp. 3472-3481, April 2022.

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.

Parras, J., Apellániz, P.A., & Zazo, S. (2021). Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances. Sensors 21(15), 5011.

Parras, J., Hüttenrauch, M., Zazo, S., & Neumann, G. (2021). Deep Reinforcement Learning for Attacking Wireless Sensor Networks. Sensors, 21(12), 4060.

Parras, J., & Zazo, S. (2021, June). Robust Deep Reinforcement Learning for underwater navigation with unknown disturbances. In 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3440-3444). IEEE.

Parras, J., & Zazo, S. (2020). The Threat of Intelligent Attackers Using Deep Learning: The Backoff Attack Case. In Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks (pp. 110-133). IGI Global.

Parras, J., & Zazo, S. (2020, May). A Graph Network Model for Distributed Learning with Limited Bandwidth Links and Privacy Constraints. In 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3907-3911). IEEE.

Parras, J., and Zazo, S. (2020). A distributed algorithm to obtain repeated games equilibria with discounting. Applied Mathematics and Computation, 367 (2020): 124785.

Parras, J., & Zazo, S. (2019). Repeated game analysis of a CSMA/CA network under a backoff attack. Sensors 19(24), 5393.

Parras, J., & Zazo, S. (2019). Using one class SVM to counter intelligent attacks against an SPRT defense mechanism. Ad-hoc networks, 94, 101946.

Tapia, D., Parras, J., & Zazo, S. (2019, September). Deep Reinforcement Learning for autonomous model-free navigation with partial observability. In 2019 27th European Signal Processing Conference (EUSIPCO) (pp. 1-5). IEEE.

Baldazo, D., Parras, J., & Zazo, S. (2019, September). Decentralized multi-agent deep reinforcement learning in swarms of drones for flood monitoring. In 2019 27th European Signal Processing Conference (EUSIPCO) (pp. 1-5). IEEE.

Parras, J., Zazo, S, Pérez-Álvarez, I. A., & Sanz González, J. L. (2019). Model free localization with Deep Neural Architectures by means of an underwater WSN. Sensors, 19(16), 3530.

Parras, J., & Zazo, S. (2019, July). Sequential Bayes factor testing: a new framework for decision fusion. In 20th International workshop on Signal processing advances in wireless communications (SPAWC), 2019 , (pp. 1-5). IEEE.

Parras, J., & Zazo, S. (2019). Learning attack mechanisms in Wireless Sensor Networks using Markov Decision Processes. Expert Systems with Applications, 122, 376-387.

Parras, J., & Zazo, S. (2018). Wireless Networks under a Backoff Attack: A Game Theoretical Perspective. Sensors 2018, 18(2), 404.

Parras, J., Zazo, S., Del Val, J., Zazo, J., & Macua, S. V. (2017). Pursuit-evasion games: a tractable framework for antijamming games in aerial attacks. EURASIP Journal on Wireless Communications and Networking, 2017(1), 69.

del Val, J., Zazo, S., Macua, S. V., Zazo, J., & Parras, J. (2016, August). Optimal attack and defence of large scale networks using mean field theory. In 24th European Signal Processing Conference (EUSIPCO), 2016 (pp. 973-977). IEEE.

Parras, J., del Val, J., Zazo, S., Zazo, J., & Macua, S. V. (2016, June). A new approach for solving anti-jamming games in stochastic scenarios as pursuit-evasion games. In Statistical Signal Processing Workshop (SSP), 2016 IEEE (pp. 1-5). IEEE.

Parras-Moral, J., Canadas-Quesada, F., Vera-Candeas, P., & Ruiz-Reyes, N. (2013). Audio restoration of solo guitar excerpts using a excitation-filter instrument model. Sound And Music Computing Conference 2013 (pp. 654-659). Logos Verlag.