Mohammad received his Ph.D. degree (2012) in Computer and Communication sciences from the École Polytechnique Fédérale de Lausanne (EPFL) Switzerland, and under the direction of Prof. Pierre Vandergheynst. His thesis focused on compressive sampling and source separation strategies for multichannel data. In 2013, he worked as a CNRS researcher in Applied Mathematics Research Center (CEREMADE), Université Paris Dauphine France, under the direction of Prof. Gabriel Peyré. In 2014, he joined the DSP team at Rice University, Houston Texas, as a SNSF postdoc fellow under the direction of Prof. Richard Baraniuk. He is currently with the Institute for Digital Communications at University of Edinburgh and working on projects “compressive quantitative MR imaging” and “randomized methods for large-scale optimization” with Prof. Mike Davies.
His research interests include signal and image processing, machine learning, low dimensional signal models, compressed sensing, source separation, optimization algorithms for large-scale machine learning: theoretical and applied in medical/biological imaging (magnetic resonance fingerprinting, mass spectroscopy), remote sensing (hyperspectral imagery), multi-array cameras, and low-power/complexity sensor networks.