Machine Learning in Photonic Systems group

The focus of the Machine Learning in Photonic Systems (MLPS) group is on the development and application of machine learning techniques to advance photonic classical and quantum measurement, communication and sensing systems. We work on the fundamental issues regarding machine learning and also have large focus on the experimental demonstrations on the significant advantages that machine learning techniques bring to the photonic systems. So far, we have been able to demonstrate that our method for frequency noise characterization significantly outperforms classical frequency noise characterization techniques in terms of accuracy and measurable frequency range. Moreover, our proposed method for frequency combs characterization brings some unique features not available by any other measurement technique.

We are currently working on:

  • Nonlinear Fourier transform
  • Frequency noise characterization of ultra-stable lasers
  • Characterization of frequency combs
  • Ultra-sensitive phase noise measurement for quantum sensing