New paper in Nature Photonics on the ultimate limit in extracting information by deep learning

In a new paper with Max, Lukas and Günther, recently published in Nature Photonics, we explored how artificial intelligence can extract the maximum possible information from distorted optical signals. Using neural networks trained on seemingly chaotic light patterns, we approached — and nearly reached — the theoretical limit of precision, which we showed to be defined by the Cramér–Rao bound. Many thanks go to our collaborators in France (Dorian Bouchet) and Scotland (Ilya Starshynov and Daniele Faccio) as well as to Oliver for creating the great feature image shown on the top left. Check here for TU Wien’s press release.

New paper on acoustic particle manipulation in Nature Physics

In a nice collaboration with the team of Romain Fleury at EPFL and our former Marie-Curie fellow Nicolas Bachelard from Bordeaux, we published an article in Nature Physics that demonstrates how audible sound can be used to steer and rotate objects in complex environments. See the News&Views article by Emmanuel Fort and the highlight in Physics World for more information. Here is also a video on how an origami lotus flower is moved through our sound technique.