Keynote Speakers

Concept Drift Detection, Understanding and Adaptation

Abstract: Concept drift is known as an unforeseeable change in underlying streaming data distribution over time. The phenomenon of concept drift has been recognized as the root cause of decreased effectiveness in data stream-based machine learning applications. A promising solution for coping with persistent environmental change and avoiding machine learning performance degradation is to build a concept drift detection and adaptive system. This talk will present a set of methods and algorithms that can effectively and accurately detect, understand, and adapt concept drift in data streams. The main contents include two novel competence models to indirectly measure variations in data distribution through changes in competence. By detecting changes in competence, differences in data distribution can be accurately detected and quantified; algorithms for determining a drift region to identify where a concept drift takes place in a data stream, and a local drift degree measurement that can continuously monitor regional density changes; and a concept drift adaptation method. These new methods, algorithms and techniques can be applied to data-driven prediction and decision-making in complex data stream environments.

Bio: Distinguished Professor Jie Lu is an internationally renowned scientist in the areas of computational intelligence, primarily known for her work in fuzzy transfer learning, concept drift detection, decision support systems, and recommender systems. She is an IEEE Fellow, IFSA Fellow, Australian Laureate Fellow; the Director of Australian Artificial Intelligence Institute (AAII) which has over 250 staff and students working on 50+ research projects and the Associate Dean (Research Excellence) in the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). She has published six research books and over 400 papers in Artificial Intelligence, IEEE TPAMI, IEEE TCybernetics, IEEE TNNLS, IEEE TFS and other leading journals and leading conference proceedings e.g., ICML, NeurIPS, IJCAI, AAAI, KDD; has won 20 ARC Discovery and Linkage projects and large industry projects; supervised 50 PhD students to completion. She serves as Editor-In-Chief for Knowledge-Based Systems (Elsevier) and Editor-In-Chief for International journal of computational intelligence systems (Springer). She has delivered over 40 keynote speeches at international conferences and chaired 20 international conferences. She has received many national and international awards including two IEEE Transactions on Fuzzy Systems Outstanding Paper Awards (2019 and 2022).

Urban Computing: Building Intelligent Cities Using AI and Big Data

Abstract: Urban computing connects ubiquitous sensing technologies, advanced data management, analytics models, and novel visualization methods, to create win-win-win solutions that improve urban environment, life quality, and city operation systems. This talk presents the vision and framework of urban computing, introducing the challenges and the state-of-the-art solutions in each layer of the framework. Based on the vision of urban computing, we have built an intelligent city operation system which has been deployed in over 20 cities as a digital foundation to empower Big Data-driven applications, such as logistic optimizations, traffic/crowd flow predictions, community demand and supply predictions, hazardous chemical management, and public resource allocations.

Bio: Dr. Yu Zheng is the Vice President of JD.COM and head JD Intelligent Cities Research. Before Joining JD.COM, he was a senior research manager at Microsoft Research. He was the Editor-in-Chief of ACM Transactions on Intelligent Systems and Technology from 2016 to 2021, and has served as the program co-chair of ICDE 2014 (Industrial Track), CIKM 2017 (Industrial Track) and IJCAI 2019 (industrial track). He is also a keynote speaker of AAAI 2019, KDD 2019 Plenary Keynote Panel and IJCAI 2019 Industrial Days. His monograph, entitled Urban Computing, has been used as the first text book in this field. In 2013, he was named one of the Top Innovators under 35 by MIT Technology Review (TR35) and featured by Time Magazine for his research on urban computing. Zheng was named an ACM Distinguished Scientist in 2016 and elevated to an IEEE Fellow in 2020 for his contributions to spatio-temporal data mining and urban computing.

Knowledge Graph Construction, Reasoning, and Manipulation: a Case Study in Education Domain

Abstract: In recent years, knowledge graphs (KGs) have attracted tremendous interest and attention from both industry and academia, as evidenced by the many types of KGs developed including encyclopedia KGs, commonsense KGs, and KGs for medical science, covering a wide range of applications domains like search engines, question-answering and recommendations. For different application domains, however, the ways of constructing, reasoning, and manipulating KGs are quite different. In this talk, I shall introduce a collaborative project of building a university curriculum platform (called K-Cube) based on educational KGs. Among various functions and components, K-Cube supports a novel course KG construction framework guided by a standard ontology. To reduce the redundancy, we learn a backbone based on related Wiki data items and hierarchy, thereby avoiding to use named-entity recognition. As part of the reasoning, we design a machine reading comprehension task with pre-defined questions to extract relations, thereby improving the accuracy. Furthermore, KG Views are devised to support more advanced applications such as deriving instruction plans, for which two-way synchronization is supported to accommodate editing changes on the source KG and/or the derived views. In addition, KG manipulation operations including visualization (in both 2D and 3D spaces), navigation, and utilization have been developed and are to be introduced through an experimental prototype of KCube we have implemented. The ample facilities of K-Cube greatly accommodate learning path/material recommendations, effective content exploration, and efficient course management, among other advantages.

Bio: Qing Li is a Chair Professor and Head of the Department of Computing, the Hong Kong Polytechnic University. He received his B.Eng. from Hunan University (Changsha), and M.Sc. and Ph.D. degrees from the University of Southern California (Los Angeles), all in computer science. His research interests include multi-modal data management, conceptual data modeling, social media, Web services, and e-learning systems. He has authored/co-authored over 500 publications in these areas. He is actively involved in the research community and has served as an associate editor of a number of major technical journals including IEEE Transactions on Artificial Intelligence (TAI), IEEE Transactions on Cognitive and Developmental Systems (TCDS), IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Internet Technology (TOIT), Data Science and Engineering (DSE), and World Wide Web (WWW) Journal, in addition to being a Conference and Program Chair/Co-Chair of numerous major international conferences. He also sits/sat in the Steering Committees of DASFAA, ER, ACM RecSys, IEEE U-MEDIA, and ICWL. Prof. Li is a Fellow of IEEE, IET, AAIA, and a distinguished member of CCF (China).

Machines Behaving Badly

Abstract: How can we ensure that AI is used for good instead of evil? Where do we draw the line between acceptable and bad behaviour? What are the moral choices we must make in programming AI? How do you cut through all the noise and hype and decide what you should and should not do as an AI researcher?

Bio: Toby Walsh is an ARC Laureate Fellow and Scientia Professor of AI at UNSW and CSIRO Data61. He is Chief Scientist of UNSW.AI, UNSW's new AI Institute. He is a strong advocate for limits to ensure AI is used to improve our lives, having spoken at the UN, and to heads of state, parliamentary bodies, company boards and many others on this topic. This advocacy has led to him being "banned indefinitely" from Russia. He is a Fellow of the Australia Academy of Science, and was named on the international "Who's Who in AI" list of influencers. He has written three books on AI for a general audience, the most recent is "Machines Behaving Badly: the morality of AI".

Operationalising Responsible AI: CSIRO Data61’s Approach

Abstract: Although artificial intelligence (AI) is solving real-world challenges and transforming industries, there are serious concerns about its ability to behave and make decisions in a responsible way. To address the responsible AI challenges, a number of AI ethics principles frameworks have been published recently, which AI systems are supposed to conform to. However, without further best practice guidance, practitioners are left with nothing much beyond truisms. In addition, significant efforts have been put on algorithm-level solutions which mainly focus on a subset of mathematics-amenable ethical principles (such as privacy and fairness). However, issues (including ethical issues) can occur at any step of the development lifecycle crosscutting many AI, non-AI and data components of systems beyond AI algorithms and models. To close the gap in operationalising responsible AI and make Australia’s adoption of AI safe, secure and reliable, CSIRO Data61 has started a responsible AI science initiative and established a responsible AI research team since 2021. The aim of the team is to develop Innovative software engineering tools and technologies that professional developers, end-users and other stakeholders of AI systems can use to make both AI solutions and their development processes responsible. In this talk, we will introduce our research projects and early outcomes on operationalising responsible AI.

Bio: Dr. Qinghua Lu is a principal research scientist and leads the Responsible AI science team at CSIRO’s Data61, Australia. She received her PhD from University of New South Wales in 2013. Her current research interest includes responsible AI, software engineering for AI, software architecture, and blockchain. She has published 100+ academic papers in international journals and conferences. She is leading a few major Responsible AI projects at CSIRO’s Data61. Her recent paper “Towards a Roadmap on Software Engineering for Responsible AI“ won the ACM Distinguished Paper Award.