Diamantaras Konstantinos
Professor Department of Information and Electronic Engineering International Hellenic University, Vice Rector for Internationalization, Public Relations and e-government
Dr. Konstantinos Diamantaras received his Diploma in Electrical Engineering from the National Technical University of Athens (NTUA) and his Ph.D. from the Department of Electrical Engineering at Princeton University, USA, in 1992. He subsequently worked as a postdoctoral researcher at Siemens Corporate Research in Princeton, USA, as well as at the Department of Electrical and Computer Engineering of Aristotle University of Thessaloniki (AUTH). Since 1998, he has been a faculty member of the Department of Informatics at the Technological Educational Institute (TEI) of Thessaloniki, where he has held the position of Full Professor since 2006. His research interests include machine learning, signal processing, parallel processing, and image and video processing. Dr. Diamantaras is a Senior Member of IEEE. He served as Chair of the IEEE Signal Processing Society Machine Learning for Signal Processing Technical Committee (MLSP-TC) during 2009–2010 and has been a member of the committee from 2005 to the present. He also served as a member of the IEEE Signal Processing Theory and Methods (SPTM) Technical Committee from 2005 to 2008. He is co-author of the book Principal Component Neural Networks: Theory and Applications, published by John Wiley in 1996, and has also published two additional books in Greek on Neural Networks and Parallel Processing. He currently serves as Associate Editor for the international scientific journals IEEE Transactions on Signal Processing, IEEE Signal Processing Letters, and Journal of Signal Processing Systems (Springer). In the past, he also served as Associate Editor for IEEE Transactions on Neural Networks. In 1997, he received the IEEE Best Paper Award in the field of Neural Networks and Signal Processing. He has also served as a technical committee member and conference chair for various conferences in the fields of Signal and Image Processing, Machine Learning, and Neural Networks.