Since the dawn of humanity, our species has tried to decipher the world around us, through art, literature, music or science.  The skills developed and the tools used are different, as different as the audiences targeted.  But the goals are basically the same: to dealing with complexity using the tools at hand.

The objective of the project is to advance in the integration of 3D models of the body in the development of digital products and applications, developing innovative tools that allow their 3D and / or 4D analysis for the clothing, health and wellness, audiovisual and orthopedic sectors, or any other sector that is interested in incorporating digital information from users.

Peer-to-peer ridesharing (P2P-RS) enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers. It is a prominent collective intelligence application producing significant benefits both for individuals (reduced costs) and for the entire community (reduced pollution and traffic), as we showed in a recent publication where we proposed an online approximate solution algorithm for large-scale P2P-RS [Bistaffa et al., “A Computational Approach to Quantify the Benefits of Ridesharing for Policy Makers and Travellers”, IEEE T-ITS].

The brain generates activity in the form of oscillations. Brain waves span from very slow rhythms, typical of sleep, to faster oscillations during attention and cognitive processing. Moreover, changes of brain oscillations are markers of some neurological diseases. Given the dynamism of brain activity, these events are far from stationary and thus their identification in real time is a daunting task.

A project funded by Instituto de Salud Carlos III and led by IFIC researchers is carrying out radiological imaging analysis using machine learning techniques with the aim of enhancing patient diagnose and evolution assessment.

Algorithms that detect anomalies have to learn normal behaviour to be able to identify anomalous behaviour. If broad features of the expected anomalies are known the use of supervised Machine Learning (ML) is in order. But by definition, the most interesting anomalies are those unexpected, and in that case, unsupervised ML should be used. However, unsupervised strategies are substantially less powerful than possible supervised methods –a catch 22 situation.

Machine learning is one of the keys in the development of modern Earth Observation satellite missions. Model training requires precise Earth simulation as basis of applications such as vegetation monitoring, prospecting for minerals, soil use and climate change studies, among others.

Dark Machines (www.darkmachines.org) is an initiative to develop and apply machine learning methods to accelerate dark matter searches. It is composed of more than 300 high-energy physicists, astroparticle physicists and astrophysicists, from theory and experiment, as well as computer scientists.

Low back pain (LBP) is a very prevalent pathology and a frequent cause of disability. It is associated with rising costs for the health system and for society in developed countries, affecting 70% of the general population at some time in their lives, with an annual incidence of 40%

The multidisciplinary group lead by María de la Iglesia-Vayá from the Prince Felipe Research Center (CIPF) uses Artemisa to develop the first massive and open-access data repository of lumbar MRI for International collaborative research.