Prof. Amir H Gandomi, ARC DECRA Fellow, ACM Member, IEEE Senior Member, Sigma Xi Full Member, SEM Life Member, ASME Member, ASCE Associate Member, XPRIZE Visioner, MSSANZ Management Committee Member, EAI Fellow, University of Technology Sydney, Australia; Obuda University, Hungary
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, USA and a distinguished research fellow at BEACON center, Michigan State University, USA. Prof. Gandomi has published over three hundred journal papers and 12 books which collectively have been cited 37,000+ times (H-index = 85). 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 as the top 1,000 researchers (top 0.01%) and top 50 researchers in AI and Image Processing subfield in 2021! He also ranked 17th in GP bibliography among more than 15,000 researchers. He has received multiple prestigious awards for his research excellence and impact, such as 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.
Speech Title: Evolutionary Intelligence for Information Processing
Abstract: Evolutionary Intelligence (EI) has been widely used during the last two decades and has remained a highly-researched topic, especially for complex real-world problems. The 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 EC 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, evolutionary optimization will be presented including key applications in the design 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.
Assoc. Prof. Philip W. T. Pong, IEEE Senior Member, IET Fellow, IOP Fellow, EI Fellow, IMMM Fellow, HKIE Fellow, NS Fellow, CPhys, CEng, R.P.E., Chartered Energy Engineer, New Jersey Institute of Technology, USA
Biography: Philip W. T. Pong received a B.Eng. from the University of Hong Kong (HKU) in 2002 with 1st class honours. Then he obtained a PhD in engineering at the University of Cambridge in 2005. He was a postdoctoral researcher at the Magnetic Materials Group at the National Institute of Standards and Technology (NIST) for three years. Currently he is an Associate Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT). His research interest focuses on the fault detection, predictive maintenance, and anomaly detection of power grid. He is the Founding Director of the Green Technology Research and Training Laboratory, leading the research and education activities of offshore wind energy at NJIT. Philip Pong is a Fellow of the Institution of Engineering and Technology (FIET), a Fellow of the Institute of Physics (FInstP), a Fellow of the Energy Institute (FEI), a Fellow of the Institute of Materials, Minerals and Mining (FIMMM), a Fellow of the Hong Kong Institution of Engineers (FHKIE), a Fellow of the NANOSMAT Society (FNS), a chartered physicist (CPhys), a chartered engineer (CEng), a chartered energy engineer, a registered professional engineer (R.P.E. in Electrical, Electronics, Energy), and a Senior Member of IEEE (SMIEEE). He serves on the editorial boards for several IEEE and SCI journals.
Speech Title: Contactless Magnetic Sensing in Condition Monitoring and Anomaly Detection for Smart Grid: New Possibilities and Alternatives
Prof. Wei Xiang, IEEE Senior Member, Fellow of the IET in UK and Engineers Australia, La Trobe University, Australia
Biography: Wei Xiang (S’00–M’04–SM’10) Professor Wei Xiang is Cisco Research Chair of AI and IoT and Director of the Cisco-La Trobe Centre for AI and IoT at La Trobe University. Previously, he was Foundation Chair and Head of Discipline of IoT Engineering at James Cook University, Cairns, Australia. Due to his instrumental leadership in establishing Australia’s first accredited Internet of Things Engineering degree program, he was inducted into Pearcy Foundation’s Hall of Fame in October 2018. He is an elected Fellow of the IET in UK and Engineers Australia. He received the TNQ Innovation Award in 2016, and Pearcey Entrepreneurship Award in 2017, and Engineers Australia Cairns Engineer of the Year in 2017. He was a co-recipient of four Best Paper Awards at WiSATS’2019, WCSP’2015, IEEE WCNC’2011, and ICWMC’2009. He has been awarded several prestigious fellowship titles. He was named a Queensland International Fellow (2010-2011) by the Queensland Government of Australia, an Endeavour Research Fellow (2012-2013) by the Commonwealth Government of Australia, a Smart Futures Fellow (2012-2015) by the Queensland Government of Australia, and a JSPS Invitational Fellow jointly by the Australian Academy of Science and Japanese Society for Promotion of Science (2014-2015). He was the Vice Chair of the IEEE Northern Australia Section from 2016-2020. He was an Editor for IEEE Communications Letters (2015-2017), and is currently an Associate Editor for IEEE Communications Surveys & Tutorials, IEEE Internet of Things Journal, IEEE Access, and Nature journal of Scientific Reports. He has published over 250 peer-reviewed papers including 3 books and 200 journal articles. He has severed in a large number of international conferences in the capacity of General Co-Chair, TPC Co-Chair, Symposium Chair, etc. His research interest includes the Internet of Things, wireless communications, machine learning for IoT data analytics, and computer vision.
Speech Title: When Artificial Intelligence Meets the Internet of Things: Motivations, Challenges, and Applications
Abstract: Artificial Intelligence of Things (AIoT) is a newly emerging technology that combines IoT and AI technologies to enable decision making and analytics at IoT devices. IoT enables networks of physical objects that are equipped with sensors, software, and other technologies to exchange data with other devices and systems over the internet, while AI enables data analytics and automated decision making. This talk will start with the motivations of combining AI and IoT technologies as well as the associated challenges. Then Prof. Wei Xiang will talk about his experience in setting up Australia’s first accredited IoT Engineering program at James Cook University, as well as establishing Australia’s only industry-sponsored research centre that specialises in synergizing between AI and IoT technologies at La Trobe University. Before concluding the talk, Prof. Wei Xiang will talk about a wide range of applications and use cases his AIoT Centre has been working on in Australia.
Prof. Haibin Zhu, ACM Senior Member, IEEE Senior Member, I2CICC Fellow, Sigma Xi Full Member, CAST-USA Life Member, Nipissing University, Canada
Biography: Dr. Haibin Zhu is a Full Professor and the Coordinator of the Computer Science Program, the Founding Director of the Collaborative Systems Laboratory, a member of the University Budget Plan committee, Arts and Science Executive Committee, Nipissing University, Canada. He is also an affiliate professor of Concordia Univ. and an adjunct professor of Laurentian Univ., Canada. He has accomplished (published or in press) over 230+ research works including 40+ IEEE Transactions articles, six books, five book chapters, four journal issues, and four conference proceedings. He is a fellow of I2CICC (International Institute of Cognitive Informatics and Cognitive Computing), a senior member of ACM and IEEE, a full member of Sigma Xi, and a life member of CAST-USA (Chinese Association of Science and Technology, USA).
He is serving as Vice President, Systems Science and Engineering (SSE) (2023-2024), a member-at-large of the Board of Governors (2022-), and a co-chair (2014-) of the technical committee of Distributed Intelligent Systems of IEEE Systems, Man and Cybernetics (SMC) Society (SMCS), Editor-in-Chief of IEEE SMC Magazine (2022), Associate Editor (AE) of IEEE Transactions on SMC: Systems (2019-), IEEE Transactions on Computational Social Systems(2019-), Frontiers of Computer Science (2021-), and IEEE Canada Review (2019-). He was AE of IEEE SMC Magazine (2018-2021), Associate Vice President (AVP), SSE (2021), IEEE SMCS, a Program (Co-)Chair for many international conferences, and a PC member for 130+ academic conferences.
He is the founding researcher of Role-Based Collaboration and the creator of the E-CARGO model. His research monograph E-CARGO and Role-Based Collaboration can be found https://www.amazon.com/CARGO-Role-Based-Collaboration-Modeling-Problems/dp/1119693063. The accompanying Java/Python codes can be downloaded from GitHub: https://github.com/haibinnipissing/E-CARGO-Codes. He has offered 15+ keynote speeches for international conferences and 90+ invited talks internationally. His research has been being sponsored by NSERC, IBM, DNDC, DRDC, and OPIC.
Speech Title: Group Role Assignment with Constraints (GRA+): A New Category of Assignment Problems
Abstract: Assignment problems require more modelling investigations to meet the requirements of the real-world applications. Other than two conventional categories, i.e., generalized assignment and quadratic assignment, not many efforts have been put into further investigation theoretically. The reasons might be: 1) commercial optimization software platforms are developed and can solve most engineering problems if they are specified within the problem categories of the platform; 2) the generalized assignment modelling method is too abstract to express complex real-world problems.
Role-Based Collaboration (RBC) and the E-CARGO (Environment – Classes, Agents, Roles, Groups, and Objects) model have been proposed and verified as a promising approach to facilitating complex problem solving. It utilizes roles as underlying mechanisms to support collaboration. It is divided into several phases: role negotiation, role assignment, role execution, and role transfer. Role assignment can be categorized into three phases: agent evaluation, group role assignment, and role transfer. Agent evaluation rates the qualification of an agent for a role. It requires a check on the capabilities, experiences, and credits of agents based on role specifications. Qualifications are the basic requirements for possible role-related activities. It is a fundamental and challenging problem that requires advanced methodologies, such as information classification, data mining, pattern search, matching and multiple attribute decision making (MADM).
In GRA, there are many constraints we need to consider. These constraints may come from current states of roles, agents, and objects and future dynamic role execution situations, including conflicts, cooperation, limitations, preferences, and feasibility. Solutions to the GRA with constraints (GRA+) problems can be more easily applied to various applications. GRA+ also initiates a strong requirement for pertinent qualification (Q) matrices, which can be composed by MADM.
This talk illustrates a new category of assignment problems by reviewing and extending the problems related to GRA from a novel vision. We discuss GRA+ problem instances and provide a generalized formalization of this category of problems, i.e., one highly abstract optimization problem, which is specified for the first time in the field. These problems will inspire much potential research on related topics including MADM.
Prof. Haijun Zhang, IEEE Fellow, IET Fellow, University of Science and Technology Beijing, China
Biography: Haijun Zhang is currently a Full Professor and Associate Dean in School of Computer and Communications Engineering at University of Science and Technology Beijing, China. He was a Postdoctoral Research Fellow in Department of Electrical and Computer Engineering, the University of British Columbia (UBC), Canada. He serves/served as Track Co-Chair of VTC Fall 2022 and WCNC 2020/2021, Symposium Chair of Globecom'19, TPC Co-Chair of INFOCOM 2018 Workshop on Integrating Edge Computing, Caching, and Offloading in Next Generation Networks, and General Co-Chair of GameNets'16. He serves/served as an Editor of IEEE Transactions on Communications, and IEEE Transactions on Network Science and Engineering. He received the IEEE CSIM Technical Committee Best Journal Paper Award in 2018, IEEE ComSoc Young Author Best Paper Award in 2017, IEEE ComSoc Asia-Pacific Best Young Researcher Award in 2019, IEEE ComSoc Distinguished Lecturer.
Speech Title: Artificial Intelligence based 6G Network
Abstract: This talk will identify and discuss technical challenges and recent results related to the Artificial Intelligence based 6G networks. The talk is mainly divided into four parts. In the first part, will introduce 6G networks and Artificial Intelligence, discuss about the 6G mobile networks architecture, and provide some main technical challenges in AI based resource management in 6G mobile networks. In the second part, will focus on the issue of Artificial Intelligence based resource management in 6G wireless networks and provide different recent research findings. In the third part, will address the machine learning and deep learning method based 6G networks and address some key research problems. In the last part, will summarize by providing a future outlook of Artificial Intelligence based 6G.
Prof. Wanyang Dai, Nanjing University, China
Biography: Wanyang Dai is a Distinguished Professor in Nanjing University, Chief Scientist in Su Xia Control Technology. He is the current President & CEO of U.S. based (Blockchain & Quantum-Computing) SIR Forum (Industrial 6.0 Forum), President of Jiangsu Probability & Statistical Society, Chairman of Jiangsu BigData-Blockchain and Smart Information Special Committee. He received his Ph.D. in mathematics and systems & industrial engineering from Georgia Institute of Technology in USA. He was an MTS and principal investigator in U.S. based AT&T Bell Labs (currently Nokia Bell Labs) with some project won “Technology Transfer” now called cloud system. He was the Chief Scientist in DepthsData Digital Economic Research Institute. He published numerous influential papers in big name journals including Operations Research, Operational Research, Queueing Systems, Computers & Mathematics with Applications, Communications in Mathematical Sciences, and Journal of Computational and Applied Mathematics. He received various academic awards and has presented over 50 keynote/plenary speeches in IEEE/ACM, big data and cloud computing, quantum computing and communication technology, computational and applied mathematics, biomedical engineering, mathematics & statistics, and other international conferences. He has been serving as IEEE/ACM conference chairs, editors-in-chief and editorial board members for various international journals ranging from artificial intelligence, machine learning, data science, wireless communication, pure mathematics & statistics to their applications.
Speech Title: Programmable quantum computer operations and chips with applications in quantum artificial intelligence
Abstract: We derive a general spherical coordinate formula for a quantum state of n-qubit register and study n-qubit operation rules on a (n+1)-manifold with the target to help developing a (cold atom, photon or other techniques based) programmable quantum computer. The newly developed angle-based n-qubit operation rules are simple and efficient, which reduce the complicated quantum multiplication and division operations to simple addition and subtraction operations just like those used in a conventional computer. The rules for n-qubit operations are realized through measurement-based feedback controls and quantum entanglements in order to develop scalable quantum computer chips and implement quantum-cloud computing. The performance models are derived through the scaling limits (called reflecting Gaussian random fields on a manifold) for n-qubit quantum computer-based queueing systems. Applications of our developed quantum techniques in quantum artificial intelligence appeared in various areas such as genomics and blockchain will also be addressed.
The Keynot Speaker list above is continuously updated.