ABDAI 2022 Speakers

 

 

Prof. Yonghong Peng

(Keynote Speaker)

Head and Director of the University RKE Centre for Advanced Computational Science (CfACS)

Manchester Metropolitan University, UK

 

 

Biography: Prof. Yonghong Peng is currently the Professor of Artificial Intelligence (AI) and the Head and Director of the University Research and Knowledge Exchange (RKE) Centre at Manchester Metropolitan University (MMU) which conducts research on AI, Data Science, Cyber Security and Mathematical Modelling (fluid dynamics). Prior to my current role, I was the Professor of Data Science and the founding director of the Centre for Research and Innovation in Data Science (CRI-DS) at the University of Sunderland. I also co-founded the Joint Lab for Research and Innovation in Data Science (JLRI-DS) with Sichuan University and industrial partners. The JLRI-DS was recognised by British Council as one of four most innovative research and innovation partnerships between UK and China in 2019.

 

My research concerns the advancement of AI technologies that helps improve the performance of data science life cycle, including - data preparing and selection, knowledge generation and reasoning, and the decision making or decision-making support. Another side of my research concerns the integration of human behaviour and knowledge into the process of decision making to power the Artificial Intelligence systems. For both we are developing a new human-machine cooperative AI (HMCI) core, including the investigation of how AI systems can better empower people in applying AI, and how human-intelligence can be better embedded into the evolution process of AI systems so the Artificial Intelligence and Human Intelligence can be integrated as a partner. This HMCI core will hopefully lead to the development of an innovative AI as a Service (AaaS) system for a variety of applications.

 

 

Prof. Ji Zhang

(Keynote Speaker)

IET Fellow, IEEE Senior Member, Australian Endeavour Fellow, Queensland International Fellow, Izaak Walton Killam Scholar

The University of Southern Queensland, Australia

 

 

Biography: Prof. Ji Zhang is currently a Full Professor in Computer Science at the University of Southern Queensland (USQ), Australia. Prof. Zhang is an IET Fellow, IEEE Senior Member, Australian Endeavour Fellow, Queensland Fellow (Australia) and Izaak Walton Killam Scholar (Canada). He is the vice-chair of the Computer Society of IEEE Queensland and a COVID'19 Expert of the Australian Academy of Science. He served as the Principal Advisor for Research in Division of ICT Services, USQ from 2010-2013.

 

Prof. Zhang's research interests include data science, Big Data analytics, data mining, health informatics and computational intelligence. He has published over 230 papers, many appearing in top-tier international journals including IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Cybernetics, IEEE Transactions on Dependable and Secure Computing (TDSC), ACM Transactions on Knowledge Discovery from Data (TKDD), ACM Transactions on Intelligent Systems and Technology (TIST), Information Sciences, WWW Journal, Journal of Intelligent Information Systems (JIIS), Bioinformatics, Knowledge and Information Systems (KAIS) and top international conferences such as AAAI, IJCAI, VLDB, ACM CIKM, ACM SIGKDD, IEEE ICDE, IEEE ICDM, WWW Conference, COLING, PAKDD and DASFAA. He has received three best paper awards.

 

Prof. Zhang's research work has been supported by over 30 various external competitive research grants offered by Australian Government, Australian Academy of Science, Queensland Government and European Union.

 

Prof. Zhang was a Visiting Professor at Tsukuba University, Japan, Nanyang Technological University (NTU), Singapore and Michigan State University (MSU), USA.

 

 

Prof. Yingxu Wang

(Keynote Speaker)

IEEE Fellow

University of Calgary, Canada

 

 

 

 

 

 

 

 

 

 

 

 

 

Keynote Leture: Is AI Merely Driven by Big Data in Machine Learning and Intelligence Generation?

Abstract:The emergence of Abstract Sciences (AS) [1], [2], [3], [4], [5], [6], [7], [8] has triggered the synergy of contemporary disciplines of data, information, knowledge, and intelligence sciences in a coherent and hierarchical framework. Two of the fundamental queries in AS are: a) If human or machine intelligence may merely be generated from big data? and b) How may data-regression-based machine intelligence produce explainable, distinguishable, and casual wisdom? These questions have led to a series of basic research [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21] on the essences of intelligence and its aggregation from data, information, and knowledge to derivable intelligent behaviors. They also reveal the differences between data-driven and inference-driven intelligence where the former is a one-off reflexive regression of collective factors, but the latter is rigorously constrained by both sufficient and necessary conditions for causal inference based on rigorous rules [9, 15, 29, 37] and cumulatively acquired knowledge [10], [12].
For instances: a) Given a logical AND-gate with 10,000 input-pins, a training based on the first 29,999 sets of big data in the given space will lead to a false learning result that would recognize the AND-gate as a dummy device because its output is always zero no matter what the inputs would be; and b) All big data would indicate that humans get older along the counter-clockwise rotation of the earth. Therefore, data-driven learning may result in a false conclusion that the aging process may be reversed if a person moves to another planet that rotates otherwise. Both failed learning cases indicate a vital risk of the data-driven mechanism of learning in AI, because it dissatisfies the necessary and sufficient inferencing causality for rigorous machine learning towards generating trustworthy machine intelligent.

This keynote lecture will present basic research advances and their theoretical foundations for dealing with the aforementioned challenges. It explains how AI [22], [23], [24], [25], [26], [27], [28] and big data [29], [30], [31], [32], [33] engineering may learn from intelligence science underpinned by contemporary Intelligent Mathematics (IM) [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44] such as inference algebra (IA) [15] and big-data algebra (BDA) [29]. Applications of the findings and fundamental theories will be elaborated towards the development of unprecedentedly non-pretrained and non-preprogrammed autonomous AI [5]

Biography: Dr. Yingxu Wang is professor of cognitive systems, brain science, software science, and intelligent mathematics. He is the founding President of International Institute of Cognitive Informatics and Cognitive Computing (I2CICC).  He is FIEEE, FBCS, FI2CICC, FAAIA, and FWIF. He has held visiting professor positions at Univ. of Oxford (1995, 2018-22), Stanford Univ. (2008, 16), UC Berkeley (2008), MIT (2012), and distinguished visiting professor at Tsinghua Univ. (2019-22). He received a PhD in Computer Science from the Nottingham Trent University, UK, in 1998 and has been a full professor since 1994. He is the founder and steering committee chair of IEEE Int’l Conference Series on Cognitive Informatics and Cognitive Computing (ICCI*CC) since 2002. He is founding Editor-in-Chiefs and Associate Editors of 10+ Int’l Journals and IEEE Transactions. He is Chair of IEEE SMCS TC-BCS on Brain-inspired Cognitive Systems, and Co-Chair of IEEE CS TC-CLS on Computational Life Science. His basic research has been across contemporary science disciplines of intelligence, mathematics, knowledge, robotics, computer, information, brain, cognition, software, data, systems, cybernetics, neurology, and linguistics. He has published 600+ peer reviewed papers and 38 books/proceedings. He has presented 63 invited keynote speeches in international conferences. He has served as honorary, general, and program chairs for 40 international conferences. He has led 10+ international, European, and Canadian research projects as PI. He is recognized by Google Scholar as world top 1 in Software Science, top 1 in Cognitive Robots, top 7 in Autonomous Systems, top 2 in Cognitive Computing, and top 1 in Knowledge Science with a h-index 59. He is recognized by Research Gate as among the world’s top 2.5% scholars with a remarkable readership record of 480,300+.

 

 

 

 

 

 

 

 

BDAI Past Speakers

Prof. Dan Zhang

York University, Canada

Prof. Vasant Honavar

The Pennsylvania State University, USA

Prof. Cheng-Zhong Xu

University of Macau, Macau, China

Prof. Seiji Hashimoto

GUNMA UNIVERSITY, Japan

Prof. Jingsong Bao

Donghua University, China

Prof. Lefei Zhang

Wuhan University, China

 

Prof. DP Sharma

AMUIT under UNDP & Academic Ambassador, Cloud Computing (AI), IBM, USA

Prof. Amir H. Gandomi

University of Technology Sydney, Australia

Assoc. Prof. Simon James Fong

University of Macau, Macau S.A.R., China

Dr. Haijun Shan

Zhejiang Lab, China 

Prof. Deze Zeng

China University of Geosciences, China