Alessandro Perelli was born in Senigallia (Italy), in 1985. He received his Master of Science degree in "Electronic Engineering" in October 2010, his Bachelor of Science in "Electronic Engineering" in 2007, both at the University Polytechnic of Marche, Ancona (Italy).
In 2014 he pursued the Ph.D. degree in Electronic Engineering at the Department of Electronics Computer Science and Systems DEIS - University of Bologna.
His research interests are in multiresolution and adaptive signal processing, non-uniform Fourier analysis and compressed sensing applied on ultrasound signals in structural health monitoring applications.
Since December 2018, he is an H. C. Ørsted COFUND Postdoctoral Fellow at the Technical University of Denmark - DTU and my research is based on the funded fellowship called DRIFT - Deep and Randomized Imaging For Tomography.
From 2014 to 2018, he was a Research Associate at the University of Edinburgh (UK) at the Institute for Digital Communications working on signal/image processing techniques for exploring the use of low dimensional structure to reduce the computational cost of solving computational imaging problems.
His main research interests are primarily related to computational imaging, and machine learning, in particular for polyenergetic 3D/4D X-ray CT reconstruction. His particular specialism and passionate interests span to randomized linear algebra, stochastic iterative algorithms such as stochastic gradient descent, iterative sketching, probabilistic graphical models and variational inference. He is 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.
In 2014 he pursued the Ph.D. degree in Electronic Engineering at the Department of Electronics Computer Science and Systems DEIS - University of Bologna.
His research interests are in multiresolution and adaptive signal processing, non-uniform Fourier analysis and compressed sensing applied on ultrasound signals in structural health monitoring applications.
Since December 2018, he is an H. C. Ørsted COFUND Postdoctoral Fellow at the Technical University of Denmark - DTU and my research is based on the funded fellowship called DRIFT - Deep and Randomized Imaging For Tomography.
From 2014 to 2018, he was a Research Associate at the University of Edinburgh (UK) at the Institute for Digital Communications working on signal/image processing techniques for exploring the use of low dimensional structure to reduce the computational cost of solving computational imaging problems.
His main research interests are primarily related to computational imaging, and machine learning, in particular for polyenergetic 3D/4D X-ray CT reconstruction. His particular specialism and passionate interests span to randomized linear algebra, stochastic iterative algorithms such as stochastic gradient descent, iterative sketching, probabilistic graphical models and variational inference. He is 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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