Blockchain, Internet-Of-Things, and Ubiquitous AI for Metaverse
Dates: 24 Jul, 25 Jul 2023 | 9am – 5.30pm | Online
Duration: 2 Days
Course Overview
The metaverse is a new topic. Therefore, executives and experts who know enough about the Metaverse and how to handle that currently are scarce. Any Metaverse skill would be welcomed by the job market at least for a decade. The ICT industry, business, and service sectors, all need Metaverse experts, in particular, experts who know how to gain from other disruptive technologies (e.g. AI, IoT, and Blockchain) in the metaverse. Upon completion of this course, participants would be able to:
- Articulate the applications of ubiquitous AI (UbAI), IoT, and Blockchain in the Metaverse
- Realise the merits of applying disruptive technologies in the Metaverse
- Recognise roadmaps of implementation of disruptive technologies (Blockchain, IoT, UbAI) in the Metaverse.
This module is part of Professional Certificate in Infrastructure of the Metaverse.
Course Description
During this course, participants will be introduced to the applications of Blockchain, Internet of Things (IoT), and ubiquitous Artificial Intelligence (AI) in the Metaverse. The metaverse is a very new technology. It is attempting to give more role to the virtual world. As the origin of the Metaverse is Information and Communications Technology (ICT), its fusion with other modern disruptive ICT technologies may be a considerable advantage for the Metaverse community. We are going to show students how Ubiquitous AI, Blockchain, and IoT can be employed in the Metaverse. The applications, tools, and methodologies will be described and practical aspects would be devised too.
Minimum Entry Requirement
- Diploma holder
- Basic ICT knowledge
- Basic Metaverse knowledge
Course Convener
(Click their photos to view their short biographies)
Dr Amirhassan Monajemi
Dr Amirhassan Monajemi
Dr Amirhassan Monajemi is a Senior Lecturer in AI and Machine Learning with the School of Computing (SoC) at the National University of Singapore (NUS). Prior to SoC, he was a Senior Lecturer in NUS School of Continuing and Lifelong Education (SCALE) teaching AI and Data Science to adult learners. Before joining the NUS, he was with the Faculty of Computer Engineering, University of Isfahan, Iran, where he was serving as a professor of AI, Machine Learning, and Data Science. He was born in Isfahan, Iran. He studied towards BSc and MSc in Computer Engineering at Isfahan University of Technology (IUT), and Shiraz University respectively. He got his PhD in computer engineering, pattern recognition and image processing, from the University of Bristol, Bristol, England, in 2005. His research interests include AI, Machine Learning, Machine Vision, IoT, Data Science, and their applications.
He has taught the artificial intelligence courses, including AI, Advanced AI, Expert Systems, Decision Support Systems, Neural Networks, and Cognitive Science since 2005 at both undergraduate and postgraduate levels. He was awarded the best university teacher of the province in 2012. He also has studied Learning Management Systems, E-Learning, and E-Learning for workplaces since 2007.
Dr Monajemi has registered a few patents in the fields of AI, Machine Vision, and Signal Processing applications, including an AI and machine vision-based driver drowsiness detection system and a low power consuming spherical robot. He also has published more than a hundred research papers in peer-reviewed, indexed journals and international conferences (IEEE, Elsevier, Springer, and so on), and supervised several Data Science, IoT, and AI industrial projects in various scales, including Isfahan intelligent traffic system delivery and testing, and red light runners detection. He is experienced in different sub-domains of Artificial Intelligence and Machine Learning, from theory to practice, including Deep Learning, Logic, and Optimisation.
Course Fees
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
To enquire, email soc-ace@nus.edu.sg
To register, click Register
Course Code: TGS-2022017238 / TGS-2022017239 (Synchronous e-learning)
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Singapore Citizens
39 years old or youngerSingapore Citizen
40 years old or older