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Posts
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ALLEO
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Multidisciplinar work (photography, dance, music and plastic arts, among others) studying movement as an external element of the body.
portfolio
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publications
Identification of Predictive Models Including Polymorphisms in Cytokines Genes Associated with Post-Transplant Complications after Identical HLA-Allogeneic Stem Cell Transplantation
Published in 64th Annual Meeting and Exposition of the American Society of Hematology, Blood, 2022
Recommended citation: Paula Muñiz Sevilla, María Martínez-García, Mi Kwon, Rebeca Bailén, Gillen Oarbeascoa, Diego Carbonell, Julia Suárez González, María Chicano Lavilla, Cristina Andres, Juan Carlos Triviño, Javier Anguita, José Luis Díez-Martín, Pablo Martínez Olmos, Carolina Martinez-Laperche, Ismael Buño; Identification of Predictive Models Including Polymorphisms in Cytokines Genes Associated with Post-Transplant Complications after Identical HLA-Allogeneic Stem Cell Transplantation. Blood 2022; 140 (Supplement 1): 4795–4796. doi: https://doi.org/10.1182/blood-2022-168461 https://ashpublications.org/blood/article/140/Supplement%201/4795/490797/Identification-of-Predictive-Models-Including
Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data
Published in Machine Learning for Health (ML4H) Symposium 2022, 2022
Recommended citation: Moreno-Pino, F., Martínez-García, M., Olmos, P. M., & Artés-Rodríguez, A. (2022). Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data. arXiv preprint arXiv:2211.10371. https://arxiv.org/abs/2211.10371
Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data
Published in arXiv (PREPRINT currently under review), 2023
Recommended citation: María Martínez-García, Fernando Moreno-Pino, Pablo M. Olmos, and Antonio Artés-Rodríguez. Sleep activity recognition and characterization from multi-source passively sensed data. arXiv preprint arXiv:2301.10156. https://arxiv.org/abs/2301.10156
Identification of Biomarkers and Risk Factors for Inmune Effector Cell-Associated Neurotoxicity Syndrome (ICANS) in CD19-directed CAR T-Cell Therapy: a Retrospective Machine Learning-based Analysis
Published in EHA 2023 Hybrid Congress, 2023
Recommended citation: M. Gómez-Llobell; M. Martínez-García; C. Serra-Smith; D. Gómez-Costas; G. Oarbeascoa; D.Carbonell; J. Anguita; A. Pérez-Corral; M. Pion; VA Pérez-Fernández; I. García- Fernández; M.Bastos; P. Fernández-Caldas; I. Gómez-Centurión; A. Alarcón; E. Catalá; D. Conde; J. Gayoso; P.Olmos; C. Martínez-Laperche; J. García-Domínguez; Y. Fernández; R. Bailén; M. Kwon; IIdentification of Biomarkers and Risk Factors for Inmune Effector Cell-Associated Neurotoxicity Syndrome (ICANS) in CD19-directed CAR T-Cell Therapy: a Retrospective Machine Learning-based Analysis. EHA2023.
Handling ill-conditioned omics data with deep probabilistic models
Published in IEEE Journal of Biomedical and Health Informatics, 2023
Recommended citation: M. Martínez-García and P. M. Olmos, "Handling Ill-conditioned Omics Data with Deep Probabilistic Models," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2023.3279493. https://ieeexplore.ieee.org/document/10132455
Identification of predictive models including polymorphisms in cytokines genes and clinical variables associated with post-transplant complications after identical HLA-allogeneic stem cell transplantation
Published in Frontiers in Immunology, 2024
Recommended citation: Muñiz, P., Martínez-García, M., Bailén, R., Chicano, M., Oarbeascoa, G., Triviño, J. C., ... & Buño, I. (2024). Identification of predictive models including polymorphisms in cytokines genes and clinical variables associated with post-transplant complications after identical HLA-allogeneic stem cell transplantation. Frontiers in Immunology, 15, 1396284. https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1396284/full
Improved Variational Inference in Discrete VAEs using Error Correcting Codes
Published in Uncertainty in Artificial Intelligence (UAI), 2025
Recommended citation: Martínez-García, M., Villacrés, G., Mitchell, D. & Olmos, P.M.. (2025). Improved Variational Inference in Discrete VAEs using Error Correcting Codes. Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 286:2973-3012. https://proceedings.mlr.press/v286/martinez-garcia25a.html
talks
Handling ill-conditioned omics data with deep probabilistic models.
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Presented the DBLR (Deep Bayesian Logistic Regression) model at the 9ª Jornada de Investigación e Innovación IiSGM.
Handling ill-conditioned omics data with deep probabilistic models.
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Presented the DBLR (Deep Bayesian Logistic Regression) model at the Cambridge ELLIS Machine Learning Summer School 2022.
Application of Probabilistic Machine Learning models in clinical research. Case studies carried by our research group.
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Introduction to Probabilistic Machine Learning and presentation of the last results achieved by our research groups (Grupo de Genética y Clínica de las Neoplasias Hematológicas y el Trasplante Hematopoyético and Grupo de Teoría de la Señal).
Improved Variational Inference in Discrete VAEs using Error Correcting Codes.
Published:
Presented the work Improved Variational Inference in Discrete VAEs using Error Correcting Codes.
Improved Variational Inference in Discrete VAEs using Error Correcting Codes.
Published:
Presented the work Improved Variational Inference in Discrete VAEs using Error Correcting Codes.
teaching
Teacher Assistant in Deep Learning
Master Course, Universidad Carlos III de Madrid, Master in Applied Artificial Intelligence, 2022
The fundamental objective of this subject is for the student to know and learn to use learning schemes based on advanced neural networks, with special emphasis on computer vision applications, treatment of temporal signals and text, and the adjustment of probabilistic models for the generation of artificial data.
Teacher Assistant in Neural Networks
Master Course, Universidad Carlos III de Madrid, Master in Applied Artificial Intelligence, 2022
The fundamental objective is that the student learns to design decision machines based on neural networks for basic learning problems in tabular and multimedia data, paying special attention to regularization and validation techniques. Likewise, the student will learn to use automatic differentiation software packages for model training and experimental simulation.
Teacher Assistant in AI in Health
Master Course, Universidad Carlos III de Madrid, Master in Applied Artificial Intelligence, 2023
The objectives of the matter are to present the possibilities and limitations of the application of AI in the field of health, to present problems in the field of health in which AI techniques can be applied and develop the capacity to apply AI techniques in some health problems.
Master Thesis Co-director. ‘Unsupervised Genetic Study of Post-Transplant Complications after Identical HLA-Allogeneic Stem Cell Transplantation’
Master Thesis, Universidad Carlos III de Madrid, Master in Applied Artificial Intelligence, 2023
Master Thesis Co-director. ‘Deep Survival Analysis in AML patients’
Master Thesis, Universidad Carlos III de Madrid, Master in Applied Artificial Intelligence, 2023
Teacher Assistant in Natural Language Processing
Course, Fundación BBVA, 2024
Deep Probabilistic Generative Models
Seminar, Saarland University, 2025
With the development of neural networks and increased computational power, deep generative modeling has emerged as one of the leading directions in AI. We are shifting from traditional discriminative tasks (such as classification, segmentation, or clustering), which focus on modeling conditional distributions, to a more comprehensive framework aimed at modeling the joint distribution of the data itself. Discriminative models alone can be insufficient for robust decision-making and the development of intelligent systems, as it is also necessary to understand the underlying data-generating process and be able to express uncertainty about the environment.
Advanced Time Series Analysis: From Probabilistic to Foundational Models
Seminar, Saarland University, 2025
Time series analysis studies data that change as a function of time, such as stock market prices, weather patterns, or household electricity consumption. This seminar covers advanced techniques for analyzing time series, starting with probabilistic methods and progressing to state-of-the-art deep learning approaches, including neural architectures and foundation models. We will also explore connections between time series and other modalities, such as text and images/videos, to offer a comprehensive view of the field. The aim is for students to critically assess existing methods, understand their strengths and limitations, and identify potential directions for future research.