Track 5

Machine Learning for Signal Processing

1. Track Chairs:
Prof. Wai Lok Woo, Northumbria University, UK
Prof. Gexiang Zhang, Chengdu University of Information Technology, China
Dr. Hamid Esmaeili Najafabadi, University of Calgary, Canada

2. Track Topics:
Potential topics of interest include but are not limited to:

▪ Data Mining with Its Applications
▪ Deep Learning for Signal and Information Processing
▪ Machine Learning for Signal Processing
▪ Nonlinear Signal Processing
▪ Analog and Mixed Signal Processing
▪ Parallel and Distributed Signal Processing
▪ Non-Stationary Signal Processing
▪ Higher Order Statistics Theory with Applications
▪ Blind Signal Processing with Applications
▪ Natural Language Processing

3. Introduction of Track Chairs

Track Chair 1: Prof. Wai Lok Woo, Northumbria University, UK
Personal Website:

Professor Wai Lok Woo is the Chair in Machine Learning with Northumbria University, UK. He is the Head of Research for Artificial Intelligence and Digital Technology. He received the B.Eng. degree in electrical and electronics engineering and the M.Sc. and Ph.D. degrees from Newcastle University, UK, in 1993, 1995, and 1998, respectively. His major research is in the mathematical theory and algorithms for data science and analytics. This includes areas of artificial intelligence, machine learning, data mining, latent component analysis, multidimensional signal and image processing. He has an extensive portfolio of relevant research supported by a variety of funding agencies. He has been awarded more than £3M as principal investigator in designing AI for IoT applications. He has published over 400 papers on these topics at international journals and conference proceedings. He is interested to answer the global question of how the integration of IoT sensors and machine learning advances humanity and sustains the ecosystem in the current digital transformation era. Prof Woo is a Fellow of the Institution Engineering Technology (IET).

Track Chair 2: Prof. Gexiang Zhang, Chengdu University of Information Technology, China
Personal Website:

Gexiang Zhang received his Ph.D. degree in 2005 from Southwest Jiaotong University, Chengdu, China. Now he is a full professor at School of Control Engineering, Chengdu University of Information Technology, Chengdu, China. He was a visiting professor in Department of Computer Science, The University of Sheffield, UK, a senior visiting professor at the Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Spain, and a visiting professor in Department of Chemistry, New York University, USA.

He is the President of International Membrane Computing Society (IMCS), a Fellow of the IET, Senior Member of the IEEE. Managing Editor of Journal of Membrane Computing and editorial board member of Axioms and International Journal of Parallel, Emergent and Distributed Systems. He is the co-winner of Grigore Moisil Prize of the Romanian Academy in 2019. He is listed in World’s Top 2% Scientists in 2020 and in Highly Cited Chinese Researchers by Elsevier in 2021. Research areas include signal processing, image processing, artificial intelligence, natural computing, robotics, power systems, and their interactions. He is the author/co-author of more than 200 publications, two monographs, and (lead) guest editor/co-editor of more than 10 volumes/proceedings.

Track Chair 3: Dr. Hamid Esmaeili Najafabadi, University of Calgary, Canada

Personal Website:

Hamid Esmaeili Najafabadi received B.S., M.S., and Ph.D. in Electrical Eng. in 2005, 2008, 2017 from Isfahan University of Technology, Amirkabir University of Technology, and University of Isfahan, respectively. He has collaborated with several industrial groups and research institutes, including the ICTI, Cheetah group, DRDC, and Backer Hughes. Currently, he is a postdoc associate at the University of Calgary. He has been the program committee member of several conferences, including FUSION 2020, 2021, 2022, and CIE 2021. He is also serving as a guest editor for IEEE JSTARS. Currently, his main research focus includes data science, deep learning, and generative adversarial networks.