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A cross-disciplinary project involving three EPFL units – the Hub for Advanced Image Reconstruction, the Galatea Lab and the AudioVisual Communications Laboratory (LCAV) – has received a grant from the Swiss National Science Foundation (SNSF) to study methods for speeding image capture in non-linear

EPFL researchers have developed a novel approach to network analysis that allows them to reveal and interpret, for the first time, interactions among multiple variables in data from neuroscience, economics, and epidemiology.

EPFL researchers have teamed up with Dartfish and the Lausanne Hockey Club to push the boundaries of sports-performance analysis by applying computer vision and machine-learning technology to action on the ice.

Researchers from EPFL and ETH Zurich, working together with the International Committee of the Red Cross (ICRC) and Bin Khalifa Unversity (Qatar), have developed a program that can generate population density estimates with unparalleled precision, and only needs a rough estimate at the regional leve

A team of scientists at EPFL have built a new neural network system that can help understand how animals adapt their movement to changes in their own body and to create more powerful artificial intelligence systems.

Scientists at EPFL and the Consiglio Nazionale delle Ricerche (CNR), the University Federico II, and CEINGE-Biotecnologie avanzate in Naples, Italy, have developed a new method to screen individual cells quickly and reliably without fluorescence labeling. Their work, published in the journal Nature