University of Barcelona (UB) is a renowned public university located in Barcelona, Catalonia. With over 63,000 students, it stands as one of Spain’s largest and oldest universities, established in 1450. UB is consistently ranked among the top universities in Spain and holds a favourable position in European rankings, currently ranking around the 50th place.

Within STAGE, the Barcelona Artificial Intelligence in Medicine (BCN-AIM) group, part of the Faculty of Mathematics and Computer Science at UB, takes responsibility for Work Package 6, personalised Ai-driven predictive modelling of aging without multi-morbidity, and other tasks within the rest of the work packages.

Machine Learning Researcher
Marina Camacho is a machine learning researcher at the Artificial Intelligence in Medicine Lab (BCN-AIM) at the University of Barcelona. She holds a BSc in Bioinformatics, a joint degree from the University of Barcelona, Polytechnic University of Catalonia, and Pompeu Fabra University (UPF). Additionally, she completed her MSc in Biomedical Computational Engineering at UPF.

Her primary research interest lies in developing reliable machine learning models for disease prediction and multi-morbidity trajectories based on exposome data from large-scale cohorts. Marina collaborates on several European projects, including EarlyCause, LongITools, and Youth-GEMs, contributing to the design and development of models.


Postdoc researcher
Esmeralda Ruiz obtained her Doctor of Engineering degree from the University of Freiburg, Germany. She completed her bachelor’s degree in computer engineering and a master’s degree in computer vision and artificial intelligence at the Autonomous University of Barcelona. Currently, she serves as a postdoctoral researcher at the Artificial Intelligence in Medicine Lab (BCN-AIM) at the University of Barcelona.

Her primary area of research centres on developing machine learning and deep learning techniques for medical data diagnosis and prognosis. Esmeralda focuses particularly on ensuring clinical interpretability for these models, aiming to enhance understanding and uncover new insights into the diseases being studied.



Noussair is a post-doctoral researcher whose research focuses on developing Artificial Intelligence models for mental health trajectories. Leveraging his expertise in AI, Noussair collaborates closely with mental health professionals to ensure the models are not only relevant and accurate but also address ethical and privacy concerns in real-world mental health care settings.
His objective is to create effective AI models capable of identifying patterns and risk factors for mental health disorders, delivering personalized interventions and treatments, and intervening early in the development of such disorders.


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