Francesco Camastra was born in Polignano a Mare, Italy, in 1960. He obtained an M.S. degree in physics from the University of Milan, Milan, Italy, in 1985, and a Ph.D degree in computer science from the University of Genova, Genoa, Italy, in 2004. From May 1987 to January 2006, he was in the R&D Department of Elsag in Genova, Italy. In 2005, he was an Adjoint Professor at the University of Pisa, Pisa, Italy, and was the recipient of the Eduardo R. Caianiello Award 2005, for the best Ph.D. thesis on neural networks. From February 2006 to July 2013, he was with the Applied Science Department of the Parthenope University of Naples, Naples, Italy. At present, he is Ricercatore Universitario (UK Lecturer) with the Science and Technology Department of the same university. He was the recipient of PR Award 2008, as coauthor of the best paper published in 2008. He was included in the Top Reviewers of Pattern Recognition Letters from 2008 to 2012. He published 46 papers in peer-reviewed journals and proceedings of conferences and 1 book. He served as a Reviewer for ACM Computing Surveys, AI Communications, Applied Soft Computing, Chaos, Computer Vision and Image Understanding, Cytokine, Data and Knowledge Engineering, Eurasip Journal on Advances on Signal Processing, IEEE Signal Processing Letters, IEEE Transactions on Image Processing, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Signal Processing, IEEE Transactions on Systems, Man and Cybernetics-B, IET Computer Vision, Information Processing and Management, Information Sciences, International Journal of Neural Systems, Journal of Classification, Journal of Machine Learning Research, Journal of Visual Communication and Image Representation, Neural Processing Letters, Neural Computation, Neurocomputing, Pattern Recognition, Pattern Recognition Letters, and Theoretical Computer Science. He is a Member of the IEEE. His research interests are machine learning, kernel methods, manifold learning, and clustering methods.
Biography Updated on 4 December 2013