Dr Alessandro Perelli

Visiting Researcher




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Research Institute: 

  • Digital Communications

Research Theme: 

  • Signal and Image Processing


Alessandro Perelli is a Postdoctoral Research Associate at the University of Edinburgh (UK) in the Institute for Digital Communications since July 2014, working on signal/image processing techniques for computational imaging. 

Alessandro Perelli received the Bachelor of Science and the Master of Science degree in Electrical and Electronic Engineering respectively in 2007 and 2010, both at the Università Politecnica delle Marche, Ancona (Italy). From September 2012 to June 2013 he has been a Visiting Research Scholar at the University of Leeds, Ultrasound Group of Dr. Steven Freear. He received the Ph.D. degree in Computer Science and Electronic Engineering at the Department of Electrical, Electronics and Information Engineering, University of Bologna in 2014.

Currently, he is working on developing and analyzing new classes of low dimensional signal models and associated algorithms, addressing sensing and signal processing beyond just reconstruction including detection, classification and statistical estimation. He is exploring the use of low dimensional structure to reduce the computational cost of solving such signal processing problems. 

Academic Qualifications: 

  • Bachelor of Science in Electrical and Electronic Engineering
  • Master of Science in Electrical and Electronic Engineering 
  • Ph.D. in Computer Science and Electronic Engineering

Research Interests: 

Computational Imaging - Computed Tomography Reconstruction

  • this research is focused on the development of novel Compressed Sensing inspired algorithms for fast Computed Tomography (CT) image reconstruction for both medical and security applications. He was involved in an industrial project on X-ray Computed Tomography for explosives detection, funded by the U.S. Department of Homeland Security (DHS), in collaboration with GE Global Research (GRC) about exploiting compressive sensing techniques for dual energy scanners and to reduce the number of views in radiotherapy.

Solving inverse problems with large datasets

  • this research is focused on iterative algorithms for convex optimization and approximate message passing algorithms for Computational imaging
  • randomized linear algebra, stochastic iterative algorithms such as stochastic gradient descent/coordinate descent, iterative sketching, probabilistic graphical models and variational inference.
  • dealing with all aspects of the image reconstruction, from the theoretical mathematical modelling, through the algorithm development, to the handling and processing of experimental data. 

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