ACTIVE PROJECTS
European Laboratory for Learning and Intelligent Systems
The ELLIS Unit Madrid brings together leading machine learning researchers from the six public universities in Madrid: Universidad Autónoma de Madrid, Universidad Carlos III de Madrid, Universidad Complutense de Madrid, Universidad de Alcalá, Universidad Politécnica de Madrid, and Universidad Rey Juan Carlos. The unit focuses on developing innovative, interpretable, probability-based causal machine learning techniques suited for dynamic environments, as well as advancing quantum technologies for intelligent systems. Its research has found practical applications in fields such as biometrics, computer vision, healthcare, renewable energy, climate science, robotics, and smart vehicles.
IDEA-CM: Inteligencia Artificial para la Industria 4.0
Ayudas I+D Tecnologías; Comunidad de Madrid; 2025-2029
The primary aim of the project Artificial Intelligence for Industry 4.0: Data Generation, Advanced Modelling, Optimisation and Interpretability (IDEA-CM) is to integrate AI into industrial processes in order to enhance efficiency, productivity, sustainability, safety, and product customisation. The main challenges in achieving this include real-time operation, scalability, interpretability, robustness, and integration with existing systems. The IDEA-CM project is developing advanced AI algorithms to address these challenges, with a focus on real-world applications and compliance with key requirements such as interpretability and low computational complexity. The team, made up of members from Madrid’s public universities, ensures both the scientific success of the initiative and its contribution to improving global competitiveness.
Smartcrisis 2.0 and HR23-00421 Multisite Smartphone-based Ecological Momentary Intervention for suicide prevention
Fundación «la Caixa»; 2024-2027
This research project addresses suicide prevention—a critical public health challenge—by evaluating the effectiveness of combining Ecological Momentary Assessment (EMA) and Ecological Momentary Intervention (EMI) via smartphones to reduce suicide re-attempts. By capturing real-time patient data through both active and passive EMA, and delivering timely therapeutic responses via EMI, the study seeks to define and intervene upon the digital phenotype of suicidal behaviour. The project is being conducted across seven hospitals in four regions of Spain (Catalonia, Andalusia, Asturias, and Madrid), covering a catchment area of 2.5 million people, or 5% of the national population.
Una solución integral de medicina personalizada para el cuidado de pacientes de cáncer basada en el control dietético, de hábitos de vida y de bienestar emocional – LUMICare
Agencia Estatal de Investigación (AEI); 2024 – 2027
LUMICare is a clinical research project aimed at developing and validating an integrated personalised medicine solution for cancer care, targeting lifestyle, diet, and emotional wellbeing. Building on prior work in patient-led precision oncology (PLPO), LUMICare combines two key components: LUMICA 1.0, an algorithm that generates personalised dietary and supplement regimens based on tumour genetics, comorbidities, therapies, and microbiota composition; and eB2 MindCare, a mobile- and cloud-based system for passive and active patient monitoring, which uses wearable devices and AI to track physiological and behavioural data. The project involves a clinical study with 100 female cancer patients and aims to assess the impact of these interventions on quality of life, therapeutic efficacy, and molecular markers such as the metabolome and microbiome.
The initiative is led by CNIO, UC3M, and the Gynaecologic Oncology Unit at Hospital 12 de Octubre, with contributions from two academic spin-offs: TNC Nutrición Terapéutica (specialised in biomarker validation and regulatory development) and Evidence-Based Behavior (eB2), which provides the monitoring infrastructure. LUMICare is aligned with the 2023 Spanish Health Mission goals, addressing the urgent need for personalised medical devices in elderly and frail cancer patients. In this underserved population, traditional pharmacological approaches often lead to high toxicity and suboptimal outcomes. By leveraging low-cost, non-invasive interventions such as nutrition and behavioural changes, LUMICare seeks to fill a major gap in clinical oncology, especially for older adults for whom standard therapies are frequently inadequate.
Prevención de reCAídas en tRastornos dE conducta. – PreCARE
Agencia Estatal de Investigación (AEI); 2023 – 2026
The main objective of PreCARE is to develop a solution for prevention and intervention against mental health relapses and their improvement in care by combining:
a) Development of methods for profiling patients, detecting behavioral changes and explaining these changes for these pathologies.
b) Implementation of these methods in the eB2 MindCare platform and their application to health care
c) Development and validation of the solution in four units (addictions, dual pathology, rehabilitation and return to the community and eating disorders) in two hospitals.
Integrating logitudinal patient-generated data and multi-omic profiling for comprehensive precision oncology in Womens’ cancers
Instituto de Salud Carlos III PERTE Salud (CNIO); 2023 – 2025
A comprehensive precision oncology approach that integrates personalized genomics and individualized PDU collection will allow an unprecedented level of understanding of cancer processes, tackling the features that drive patient disease trajectories and outcomes, eliciting truly precision interventions. We term this new wave of precision oncology Patient-Led Precision Oncology (PLPO), and we expect PLPO will help achieve the 2 overarching goals: improve our current predictive ability, and break current efficacy plateaus. Our specific objectives are:
-To capture and integrate the PDU in a cohort of patients with women’s cancers.
-To establish patient disease trajectories and identify features that forecast individual outcomes.
-To gain biological knowledge resolution (molecular taxonomy) of seemingly identical outcomes across patients.
-To develop Patient Digital Twin (PDT) models enabling testing interventions that pinpoint individual actionable features that improve
outcomes, as a potential tool for mid-term clinical implementation in the advanced cancer patient clinical decision tree.
This integrative project combines translational and clinical oncology, engineering, data science and novel artificial intelligence (AI) approaches, in order to transition from the current genomics-centered precision oncology approach to PLPO, a model in which the integration of individual longitudinal, long-term continuous patient monitoring achieves a comprehensive personalized oncology.
Completed research projects
Psiquiatría Computacional y Modelos Integrales de Comportamiento (PRACTICO-CM)
CAM. Consejería de Educación e Investigación; 2019-2021
Human behavior is understood in many different ways from different fields and sciences. A first notion of behavior has to do with the physical actions carried out by a person in a certain context, a framework within which we would include mobility and other physical activity. On a second level, people, as belonging to an ultra-social species like ours, interact with each other in a social context. Finally, for a sychologist or psychiatrist, behavior, and especially its alterations, are linked to manifestations of mental disorders, which are usually studied with reference to behavioral patterns considered “normal” in a certain sense. The project is based on the hypothesis that these three visions of human behavior are the projection onto different domains of the same entity, and therefore there is a connection between them that allows explaining and predicting to a certain extent what is observed in one domain from the others. Our goal is to test this hypothesis and, above all, to advance its application by means of a multidisciplinary approach and team.
Machine learning and massive computation for personalised medicine and quantitative climate analysis (CLARA)
Retos Investigación 2018. Ministerio de Ciencia, Innovación y Universidades; 2019-2021
In this project we aim at devising classes of dynamical probabilistic models, with allied computational inference methods, which can be used to solve real-world problems in personalised medicine and quantitative climate prediction. While these two fields may look far apart, the key issues to be addressed in terms of model learning and computational inference are of the same kind. We advocate a common methodological approach to problems in both areas and expect a considerable degree of cross fertilization, with ideas and techniques that appear in one field and then can be successfully exploited in the other.
Machine Learning Frontiers in Precision Medicine (MLFPM2018)
European Commission Research Executive Agency; 2019-2022
The goal is to exploit the insights for Precision Medicine, which hopes to offer personalized preventive care and therapy selection for each patient. A technology with transformational potential in analysing this health data is Machine Learning. Machine Learning strives to discover new knowledge in form of statistical dependencies in large datasets. Machine Learning is key to making the vision of Precision Medicine a reality. To meet this challenge, Europe urgently needs a new generation of scientists with knowledge in both machine learning and in health data analysis, who are extremely rare at a global scale. Our ETN’s goal is to close this gap, by bringing together leading European research institutes in Machine Learning and Statistical Genetics, both from the private and public sector, to train 15 early stage researchers. These scientists will help to shape the future of this important topic and increase Europe’s competitiveness in this domain.
Creación de un algoritmo que caracterice el comportamiento humano mediante agregación de datos (Deep-Darwin)
Ayudas Fundación BBVA a Equipos de Investigación Científica 2018; 2019-2021
The objective of this project is the creation of an algorithm that characterizes the behavior of people through the aggregation of data on a large scale to know their mental state and to be able to help patients who are in psychiatric treatment in a more efficient way. To achieve this, they will collect data from psychiatric patients – who have previously given their consent and guaranteeing their privacy – in collaboration with the Fundación Jiménez Díaz University Hospital. The impact that this investigation can have is to allow an evaluation of a patient’s condition automatically and passively, meaning that the patient does not have to do anything. The psychiatrist can know how the behavior of this person outside the medical consultation and, for example, to be aware of their reaction to a medical therapy to see if it works or there is a change in pattern and from there take the decision you consider timely.