Keynote Speakers

 


Prof. Robert Minasian (IEEE Fellow & OSA Fellow)

The University of Sydney, Australia 

 

Short bio: Professor Minasian is a Chair Professor with the School of Electrical and Information Engineering at the University of Sydney, Australia. He is also the Director of the Fibre-optics and Photonics Laboratory. His research has made key contributions to microwave photonic signal processing. He is recognized as an author of one of the top 1% most highly cited papers in his field worldwide. Professor Minasian has contributed over 380 research publications, including Invited Papers in the IEEE Transactions and Journals. He has 70 Plenary, Keynote and Invited Talks at international conferences. He has served on numerous technical and steering committees of international conferences. Professor Minasian was the recipient of the ATERB Medal for Outstanding Investigator in Telecommunications, awarded by the Australian Telecommunications and Electronics Research Board. He is a Life Fellow of the IEEE, and a Fellow of the Optical Society of America.
 

 

 

Prof. Erchin Serpedin (IEEE Fellow )

Texas A&M University, USA 

 

Short bio: Erchin Serpedin is a professor in the Department of Electrical and Computer Engineering at Texas A&M University, College Station. He is the author of 4 research monographs, 1 textbook, 17 book chapters, 180 journal papers and 280 conference papers. His current research interests include signal processing, machine learning, artificial intelligence, cyber security, smart grids, bioinformatics, biomedical engineering, and wireless communications. He served as an associate editor for more than 12 journals, including journals such as the IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing, IEEE Transactions on Communications, IEEE Signal Processing Letters, IEEE Communications Letters, IEEE Transactions on Wireless Communications, IEEE Signal Processing Magazine, Signal Processing (Elsevier), Physical Communications (Elsevier), and IEEE Signal Processing Magazine, and as a Technical Chair for six major conferences. He served also as editor-in-chief of EURASIP Journal on Bioinformatics and Systems Biology, an online journal edited by Springer-Nature. He is an IEEE Fellow.

Title: Detection of Electricity Stealth Cyber-Attacks in Smart Grids via Machine Learning 
Abstract: Currently, power grids are subject to false data injection attacks and electricity stealth cyber-attacks that may lead to undesirable consequences. This talk will provide an overview on the recent advances in deep learning techniques for detecting electricity stealth cyber-attacks and false data injections in smart power grids. The performance of various deep learning architectures will be assessed and compared with the detection performance of shallow architectures and model-based approaches.

 


 

Prof. Sri Krishnan,

Ryerson University, Toronto, Canada

 

Speech Title :
Signal Analysis for Connected Healthcare  (Read more)

 

Short bio: Sri Krishnan joined Ryerson University, Toronto, Canada in 1999, and currently he is a Professor of Electrical and Computer Engineering, and a Co-director of the Institute for Biomedical Engineering, Science and Technology (iBEST). He is a Fellow of the Canadian Academy of Engineering. From 2007-2017 he was a Canada Research Chair in Biomedical Signal Analysis. Sri Krishnan has published 325 papers in refereed journals and conferences, and six of his papers have won best paper awards. Sri Krishnan is a recipient of many awards including the 2016 Outstanding Canadian Biomedical Engineer Award, 2013 Achievement in Innovation Award from Innovate Calgary, 2011 Sarwan Sahota Distinguished Scholar Award, 2007 Young Engineer Achievement Award from Engineers Canada.

 

Title: Audio Scene Analysis and its Various Applications
Abstract: Audio scene analysis provides contextual and content-based information of the surrounding environment, and benefits human-machine interactions for many voice-based and machine "hearing" applications.  The field is poised to benefit tremendously from the ubiquitous nature of audio data and fast growing deployment of AI models.
Specifically in this talk, the following modules will be covered:
(1) Audio scene definition, opportunities, and challenges;
(2) Audio scene models and analysis;
(3) Feature analysis and machine learning approaches;
(4) Applications in the vertical domains of healthcare, 
consumer and organizational sectors, and robotics.