Invited Keynote Speakers

José C. Principe, Ph.D.

Keynote: Multiscale Causal Interactions in the Cortical Column during Behavior

Using a new transient model to quantify the gamma and beta rhythms in brain field potentials, this talk will show how to combine them with spike trains to represent causal multiscale neural interactions, which explain the complex processing in the 6 layers of the monkey cortical column during a visual behavioral task. 

Biography: Jose C. Principe (M’83-SM’90-F’00) is a Distinguished Professor of Electrical and Computer Engineering and Biomedical Engineering at the University of Florida where he teaches statistical signal processing, machine learning and brain computer interfaces. He is the Eckis Endowed Professor and the Founder and Director of the University of Florida Computational NeuroEngineering Laboratory (CNEL) www.cnel.ufl.edu. His primary area of interest is time series analysis in functional spaces, information theoretic learning and AI cognitive architectures, applied to neurotechnology. Dr. Principe is an IEEE, AAAS, IABME, AIMBE and NDA Fellow. He was awarded the IEEE Neural Network Pioneer Award from the Computational Intelligence Society, the IEEE Shannon Nyquist Technical Achievement Award from the Signal Processing Society, the IEEE EMBS Career Achievement Award from the Engineering Medicine and Biology Society, and the Teacher Scholar of the Year from the University of Florida. He was the past Chair of the Technical Committee on Neural Networks of the IEEE Signal Processing Society, Past-President of the International Neural Network Society, and Past-Editor in Chief of the IEEE Transactions on Biomedical Engineering. Dr. Principe has more than 900 publications and an H index of 94 (Google Scholar).  He directed 106 Ph.D. dissertations and 65 Master theses.  He wrote in 2000 an interactive electronic book entitled “Neural and Adaptive Systems” published by John Wiley and Sons and more recently co-authored several books on “Brain Machine Interface Engineering” Morgan and Claypool, “Information Theoretic Learning”, Springer, and “Kernel Adaptive Filtering”, Wiley.

Leontios Hadjileontiadis, Ph.D.

Keynote: Swarm Decomposition: A Pray-Predator Approach 

Signal decomposition aims at extracting and separating signal components from composite signals, which should preferably be related to semantic units. This is extended to separation of single components from mixed signals, where the composite signal consists of a sample-wise superposition from multiple components. Various approaches have been proposed in the literature, such as wavelet-based multiresolution analysis, synchro-squeezing transform, ensemble empirical mode decomposition, empirical wavelet transform, trying to take into consideration the embedded characteristics of the time series related to nonstationarity and nonlinear harmonic interactions. In this keynote, a recently introduced signal decomposition, namely Swarm Decomposition (SwD), will be presented. The main idea behind the SwD is the pray-predator relationship, where the signal to be decomposed is the pray and the swarm is the predator. Theoretical justifications, comparative analysis and practical examples will be presented, along with further extensions of the SwD in the case of multivariate signal decomposition.   

Biography: Dr. Hadjileontiadis (IEEE S’87–M’98–SM’11) received his Diploma degree in electrical engineering and the Ph.D. degree in electrical and computer engineering (ECE) from the Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece, in 1989 and 1997, respectively, the Ph.D. degree in music composition from the University of York, York, U.K., in 2004, and the Diploma degree in musicology from AUTH, in 2011. His research interests include advanced signal processing, machine learning, biomedical engineering, affective computing, serious games, and active and healthy ageing. He is working on signal processing in the fields of biomedical engineering (bioacoustics, ECG data compression, high density EEG-based 3D vector field tomography) affective computing (EEG-based emotion recognition), educational data analytics (blended-, affective-, collaborative-learning modeling), non-destructive testing data analysis (crack detection in beams and plates), behavioral modeling (swarm-based decomposition/transform) in the ECE, AUTH, and in the Dept. of Biomedical Engineering at Khalifa University, where he serves also as its Chair. Dr. Hadjileontiadis is a Senior Member of the IEEE. Google scholar: https://scholar.google.com/citations?user=OfAkcXkAAAAJ&hl=en.