The realm of statistical mechanics has been enlarged to describe systems, such as glass forming materials, where structural disorder plays the predominant role. Interestingly the spectrum of applications of this new physics goes much beyond the scope of condensed matter and extends to the currently booming field of data science.
In this talk I will focus on the challenge of signal-reconstruction from noisy collections of data, omnipresent in machine learning applications and in classical inference problems. By leveraging tools and ideas from glass physics, I will give some examples on how we can describe, predict, and enhance the performances of algorithms introduced to tackle these reconstruction problems.
Meeting ID: 838 2931 8876
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