Keynote Speaker

 

Subhas Mukhopadhyay, Professor
IEEE Fellow, IET Fellow, IETE Fellow
Macquarie University, Australia

Biography: Subhas holds a B.E.E. (gold medallist), M.E.E., Ph.D. (India) and Doctor of Engineering (Japan). He has over 34 years of teaching, industrial and research experience. Currently he is working as a Professor of Mechanical/Electronics Engineering, Macquarie University, Australia and is the Discipline Leader of the Mechatronics Engineering Degree Programme. His fields of interest include Smart Sensors and sensing technology, instrumentation techniques, wireless sensors and network (WSN), Internet of Things (IoT), Mechatronics etc. He has supervised over 55 postgraduate students and over 150 Honours students. He has examined over 75 postgraduate theses. He has been co-inventor of 12 patents and published over 450 papers in different international journals and conference proceedings, written ten books and fifty two book chapters and edited eighteen conference proceedings. He has also edited forty books with Springer-Verlag and thirty five journal special issues. He has organized over 20 international conferences as either General Chairs/co-chairs or Technical Programme Chair. He has delivered 440 presentations including keynote, invited, tutorial and special lectures. As per Scholargoogle, his total citation is 22156 and h-index is 74.
He is a Fellow of IEEE (USA), a Fellow of IET (UK), a Fellow of IETE (India). He is a Topical Editor of IEEE Sensors journal. He is also an associate editor of IEEE Transactions on Instrumentation and Measurements and IEEE Reviews in Biomedical Engineering (RBME). He was a Distinguished Lecturer of the IEEE Sensors Council from 2017 to 2022. He chairs the IEEE Instrumentation and Measurement Society NSW chapter.
More details can be available at https://scholar.google.com.au/citations?user=8p-BvWIAAAAJ&hl=en; https://orcid.org/0000-0002-8600-5907;
http://web.science.mq.edu.au/directory/listing/person.htm?id=smukhopa

Speech Title: Recent Advances in Sensing and Machine Vision for Mechatronics

Abstract: The advancement of sensing technologies, embedded systems, wireless communication technologies, nano-materials, miniaturization, vision sensing and processing speed makes it possible to develop smart mechatronics and machine systems. This seminar will discuss recent research and developmental activities on different sensors and sensing system along with machine visions at Macquarie University as applicable to Mechatronics, robotics and drones for home, health and environmental monitoring.

 

Rajkumar Buyya, Professor
FIEAust Fellow
Director, Cloud Computing and Distributed Systems (CLOUDS) Lab,
The University of Melbourne, Australia
CEO, Manjrasoft Pvt Ltd, Melbourne, Australia

Biography: Dr. Rajkumar Buyya is a Redmond Barry Distinguished Professor and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia. He is also serving as the founding CEO of Manjrasoft, a spin-off company of the University, commercializing its innovations in Cloud Computing. He has authored over 850 publications and seven textbooks including "Mastering Cloud Computing" published by McGraw Hill, China Machine Press, and Morgan Kaufmann for Indian, Chinese and international markets respectively. Dr. Buyya is one of the highly cited authors in computer science and software engineering worldwide (h-index=166 g-index=365, and 145,500+ citations). He has been recognised as a "Web of Science Highly Cited Researcher" for seven times since 2016, "Best of the World" twice for research fields (in Computing Systems in 2019 and Software Systems in 2021/2022/2023) as well as "Lifetime Achiever" and "Superstar of Research" in "Engineering and Computer Science" discipline twice (2019 and 2021) by the Australian Research Review.
Software technologies for Grid, Cloud, and Fog computing developed under Dr.Buyya's leadership have gained rapid acceptance and are in use at several academic institutions and commercial enterprises in 50+ countries around the world. Manjrasoft's Aneka Cloud technology developed under his leadership has received "Frost New Product Innovation Award". He served as founding Editor-in-Chief of the IEEE Transactions on Cloud Computing. He is currently serving as Editor-in-Chief of Software: Practice and Experience, a long-standing journal in the field established 50+ years ago. He has presented over 700 invited talks (keynotes, tutorials, and seminars) on his vision on IT Futures, Advanced Computing technologies, and Spiritual Science at international conferences and institutions in Asia, Australia, Europe, North America, and South America. He has recently been recognized as a Fellow of the Academy of Europe. For further information on Dr.Buyya, please visit his cyberhome: www.buyya.com

Speech Title: Recent Advances in Cloud and Quantum Computing

Abstract: Computing is being transformed to a model consisting of services that are delivered in a manner similar to utilities such as water, electricity, gas, and telephony. In such a model, users access services based on their requirements without regard to where the services are hosted or how they are delivered. Cloud computing paradigm has turned this vision of "computing utilities" into a reality. It offers infrastructure, platform, and software as services, which are made available to consumers as subscription-oriented services. Cloud application platforms need to offer (1) APIs and tools for rapid creation of elastic applications and (2) a runtime system for deployment of applications on geographically distributed Data Centre infrastructures (with Quantum computing nodes) in a seamless manner.
The Internet of Things (IoT) paradigm enables seamless integration of cyber-and-physical worlds and opening opportunities for creating new class of applications for domains such as smart cities, smart robotics, and smart healthcare. The emerging Fog/Edge computing paradigms support latency sensitive/real-time IoT applications with a seamless integration of network-wide resources all the way from edge to the Cloud.
This keynote presentation will cover (a) 21st century vision of computing and identifies various IT paradigms promising to deliver the vision of computing utilities; (b) innovative architecture for creating elastic Clouds integrating edge resources and managed Clouds, (c) Aneka 5G, a Cloud Application Platform, for rapid development of Cloud/Big Data/AI applications and their deployment on private/public Clouds with resource provisioning driven by SLAs, (d) a novel FogBus software framework with Blockchain-based data-integrity management for facilitating end-to-end IoT-Fog/Edge-Cloud integration for execution of sensitive IoT applications, (e) experimental results on deploying Cloud and Big Data/ IoT applications in engineering, and health care (e.g., COVID-19), deep learning/Artificial intelligence (AI), satellite image processing, and natural language processing (mining COVID-19 research for new insights) on elastic Clouds, (f) QFaaS: A Serverless Function-as-a-Service Framework for Quantum Computing, and (g) directions for delivering our 21st century vision along with new directions for future research in Cloud, Edge, and Quantum computing.

 

Saman Halgamuge, Professor
Fellow of IEEE, IET and AAIA
University of Melbourne, Australia

Biography: Prof Saman Halgamuge is a Fellow of IEEE, a Professor in the Department of Mechanical Engineering of School of Electrical, Mechanical and Infrastructure Engineering. He is also one of the IEEE Distinguished Speakers appointed for the theme Computational Intelligence.  He has previously served as Director/Head, Research School of Engineering of the Australian National University (2016-18) and as a member of Australian Research Council (ARC) College of Experts for Engineering, Information and Computing Sciences (2016-18). He was the founding Director of the PhD training centre Melbourne India Postgraduate Program (MIPP) of University of Melbourne and contributed as Associate Dean (2013-15) and Assistant Dean (2008-13) in International Engagement in the Melbourne School of Engineering. He is also a member of various International advisory committees including the Visiting Committee of Chinese University of Hong Kong (2018) and Research Advisory Council of University of Technology PETRONAS (2015-18). He is an honorary Professor of Australian National University and an honorary member of ANU Energy Change Institute. His research interests are in AI and Data Engineering including Inclusive Learning algorithms and Active data gathering sensor systems, Unsupervised Deep Learning, Big Data Analytics focusing on applications in Mechanical Engineering, Energy and Bioengineering. These applications vary from Sensor Networks in Irrigation, Smart Grids, and Sustainable Energy generation to Bioinformatics and Neuro-Engineering. He supervised 50 PhD students as the primary supervisor. He has also been a keynote speaker for 40 research conferences. His citations and h-factor can be extracted from is https://scholar.google.com.au/citations?hl=en&user=9cafqywAAAAJ&view_op=list_works.

Speech Title: Explainable AI: From Mathematical to Textual Explanations

Abstract: In this keynote, I discuss explainable AI (XAI) also known as “white-box” models. I elaborate on model design of XAI enabling their optimisation for greater accuracy and transparency. I also describe the recent works in our group on XAI.

 

 

Invited Speaker

 

Prof. Amir H. Gandomi
University of Technology Sydney, Australia

Biography: Amir H. Gandomi is a Professor of Data Science and an ARC DECRA Fellow at the Faculty of Engineering & Information Technology, University of Technology Sydney. He is also affiliated with Obuda University, Budapest, as a Distinguished Professor. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at Stevens Institute of Technology and a distinguished research fellow at BEACON center, Michigan State University. Prof. Gandomi has published over three hundred journal papers and 12 books which collectively have been cited 44,000+ times (H-index = 94). He has been named as one of the most influential scientific minds and received the Highly Cited Researcher award (top 1% publications and 0.1% researchers) from Web of Science for six consecutive years, from 2017 to 2022. In the recent most impactful researcher list, done by Stanford University and released by Elsevier, Prof Amir H Gandomi is ranked among the top 1,000 researchers (top 0.01%) and top 50 researchers in AI and Image Processing subfield in 2021! He has received multiple prestigious awards for his research excellence and impact, such as the 2023 Achenbach Medal and the 2022 Walter L. Huber Prize, the highest-level mid-career research award in all areas of civil engineering. He has served as associate editor, editor, and guest editor in several prestigious journals, such as AE of IEEE Networks and IEEE IoTJ. Prof Gandomi is active in delivering keynotes and invited talks. His research interests are global optimisation and (big) data analytics using machine learning and evolutionary computations in particular.

Title: Navigating the Impact of AI in Engineering: A Deep Dive into EI for Automated Computing

Abstract: Artificial Intelligence has been widely used during the last two decades and has remained a highly-researched topic, especially for complex real-world problems. Evolutionary Intelligence (EI) techniques are a subset of artificial intelligence, but they are slightly different from the classical methods in the sense that the intelligence of EI comes from biological systems or nature in general. The efficiency of EI is due to their significant ability to imitate the best features of nature which have evolved by natural selection over millions of years. The central theme of this presentation is about EI techniques and their application to complex real-world problems. On this basis, first I will talk about an automated learning approach called genetic programming. Applied evolutionary learning will be presented, and then their new advances will be mentioned. Here, some of my studies on big data analytics and modelling using EI and genetic programming, in particular, will be presented. Second, EI will be presented including key applications in the optimization of complex and nonlinear systems. It will also be explained how such algorithms have been adopted to engineering problems and how their advantages over the classical optimization problems are used in action. Optimization results of large-scale towers and many-objective problems will be presented which show the applicability of EI. Finally, heuristics will be explained which are adaptable with EI and they can significantly improve the optimization results.