Moncef Gabbouj, MS, PhDProfessor & Director Tampere University- Faculty of Information Technology and Communication Sciences
& NSF IUCRC Center for Visual and Decision Informatics, Finland-Site
Dr. Moncef Gabbouj received his BS degree in electrical engineering in 1985 from Oklahoma State University, Stillwater, and his MS and PhD degrees in electrical engineering from Purdue University, West Lafayette, Indiana, in 1986 and 1989, respectively. Dr. Gabbouj is a Professor of Signal Processing at the Department of Computing Sciences, Tampere University, Tampere, Finland. He was Academy of Finland Professor during 2011-2015. He was a visiting professor at the Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, School of Electrical and Computer Engineering of Purdue University, West Lafayette, Indiana, and American University of Sharjah, UAE. Dr. Gabbouj is currently the TUT-Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics and member of the Science Council of Tampere University of Technology. His research interests include Big Data analytics, multimedia content-based analysis, indexing and retrieval, artificial intelligence, machine learning, pattern recognition, nonlinear signal and image processing and analysis, voice conversion, and video processing and coding. Dr. Gabbouj is an IEEE Fellow. He is a member of the IEEE Fourier Award Committee. He is member of the Academia Europaea and the Finnish Academy of Science and Letters. He served as Distinguished Lecturer for the IEEE Circuits and Systems Society in 2004-2005, and Past-Chairman of the IEEE-EURASIP NSIP (Nonlinear Signal and Image Processing) Board. He was chairman of the Algorithm Group of the EC COST 211quat. He served as associate editor of the IEEE Transactions on Image Processing, and was guest editor of Multimedia Tools and Applications, the European journal Applied Signal Processing. Dr. Gabbouj was the recipient of the 2017 Finnish Cultural Foundation for Art and Science Award, the 2015 TUT Foundation Grand Award, the 2012 Nokia Foundation Visiting Professor Award, the 2005 Nokia Foundation Recognition Award. He published two books and over 700 journal and conference papers and supervised 47 doctoral and 58 Master theses.
Advanced Machine Learning for Biomedical Signal Analytics
In this talk, we shall discuss how we approach biomedical signal analytics from a signal processing, pattern recognition and machine learning point of view to solve pertinent problems in the field. We present a hierarchical layered approach that exploits different types of sensor and non-sensor signals and design suitable representation, processing and analysis algorithms in order to apply machine learning, including deep and shallow learning. We shall then exploit the layered approach in a wide array of applications, with specific emphasis on ECG classification, where we shall present a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. The proposed system uses an adaptive implementation of 1D Convolutional Neural Networks (CNNs) which is used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. The proposed solution can be used for real-time ECG monitoring and early alert system on a light-weight wearable device.