Random Matrix Theory (RMT) is being found to be valuable in an increasing number of disciplines of science and engineering. In some disciplines it has become quite routine to use limiting densities based on RMT to decide between signal and noise. Such examples  can be found in wireless communication, cell biology and even in big data analytics. RMT for patterned matrices are of great interest in some fields such as high dimensional time series. Limiting distributions for eigenvalues of sample correlation matrices from various extremely behaved populations, finds many applications in real data. In this session we would like to showcase some applications and theoretical developments of wide interest.

Confirmed speakers:
Arup Bose (bosearu@gmail.com), Indian Statistical Institute, India.
Alexey Onatskiy (ao319@cam.ac.uk), University of Cambridge, UK.
Rajat Hazra (r.s.hazra@math.leidenuniv.nl), Leiden University, The Netherlands.
Johannes Heiny (johannes.heiny@math.su.se), Stockholm University, Sweden.