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   At LIONS, we are concerned with optimized information extraction from signals or data volumes. We therefore develop mathematical theory and computational methods for information recovery from highly incomplete data.

We divide our research into two synergistic theory thrusts: information scalable optimization and data acquisition, and learning theory and methods for low-dimensional signal models. These research directions dovetail in order to develop a unified theory and practical toolset for adaptive representations, sampling and computational methods for high-dimensional data that feature structured geometric and combinatorial foundations.


PhD defense of Ilija Bogunovic

On Friday 25th of January 2019, Ilija Bogunovic, a PhD student at LIONS lab, successfully defended his PhD thesis. The thesis, entitled

Prestigious NRF fellowship for Jonathan Scarlett

LIONS’ former postdoctoral researcher Jonathan Scarlett, who is currently an Assistant Professor in the 

Baran Gözcü at EPFL PhD summit

LIONS' PhD student Baran Gözcü has been selected to take part in the Engineering PhD summit, a workshop, for final year PhD studen

LIONS in spotlight @ NIPS2018

Three papers from LIONS lab have been accepted to the thirty-Second Annual Conference on Neural Information Processing Systems conference (NIPS 2018). This year NIPS conference received a record-breaking 4856 submissions of which 1011 we

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