KEYNOTE SPEAKER
Xiaoli Li, Singapore University of Technology and Design, Nanyang Technological University, Singapore
Speech Title:
Time-Series Sensor Data Analytics: From Cutting-Edge Research to Real-World Impact
Abstract:
The proliferation of sensors in industries such as manufacturing, aerospace, semiconductors, transportation, education and healthcare has created a critical demand for innovative AI solutions to analyze time-series sensor data. These solutions enable applications ranging from enhancing equipment availability and efficiency to enabling data-driven maintenance and intelligent system control. In this talk, we will explore recent advances in deep learning-based AI research tailored to address key challenges in equipment diagnostics and remaining useful life prediction. Key focus areas include achieving high prediction accuracy, compressing models for edge computing deployment, considering data privacy and overcoming domain adaptation barriers. By showcasing real-world use cases, we will demonstrate how these AI-driven approaches bridge the gap between academic research and industrial applications, driving more efficient and intelligent systems across diverse sectors.
Bio of the Keynote Speaker Xiaoli is currently a Full Professor and Head of the Information Systems Technology and Design Pillar at Singapore University of Technology and Design (SUTD). He previously led A*STAR's Machine Intellection Department, where he built and directed Singapore's largest AI and data science research group. He is also an Adjunct Full Professor at Nanyang Technological University, and a Fellow of both IEEE and AAIA. His research spans AI, data mining, machine learning, and bioinformatics, and has produced more than 390 peer-reviewed publications with over 30,000 citations, an h-index of 89, and more than ten best paper awards. He serves as Editor-in-Chief of the Annual Review of Artificial Intelligence and as an Associate Editor for leading journals such as IEEE Transactions on Artificial Intelligence and Knowledge and Information Systems. He has also played key leadership roles as conference chair or area chair at premier venues including AAAI, IJCAI, ICLR, NeurIPS, KDD, and ICDM. Beyond academia, Xiaoli brings extensive industry engagement experience, having established and led multiple joint labs and spearheaded more than ten major R&D collaborations with global partners in aerospace, telecommunications, insurance, and professional services. His contributions have earned him international recognition as one of the world’s top 2% scientists in AI (Stanford University) and as a Clarivate Highly Cited Researcher.
Bio of the Keynote Speaker Xiaoli is currently a Full Professor and Head of the Information Systems Technology and Design Pillar at Singapore University of Technology and Design (SUTD). He previously led A*STAR's Machine Intellection Department, where he built and directed Singapore's largest AI and data science research group. He is also an Adjunct Full Professor at Nanyang Technological University, and a Fellow of both IEEE and AAIA. His research spans AI, data mining, machine learning, and bioinformatics, and has produced more than 390 peer-reviewed publications with over 30,000 citations, an h-index of 89, and more than ten best paper awards. He serves as Editor-in-Chief of the Annual Review of Artificial Intelligence and as an Associate Editor for leading journals such as IEEE Transactions on Artificial Intelligence and Knowledge and Information Systems. He has also played key leadership roles as conference chair or area chair at premier venues including AAAI, IJCAI, ICLR, NeurIPS, KDD, and ICDM. Beyond academia, Xiaoli brings extensive industry engagement experience, having established and led multiple joint labs and spearheaded more than ten major R&D collaborations with global partners in aerospace, telecommunications, insurance, and professional services. His contributions have earned him international recognition as one of the world’s top 2% scientists in AI (Stanford University) and as a Clarivate Highly Cited Researcher.
Qing Li, The Hong Kong Polytechnic University
Speech Title:
KCUBE: A KG-based University Curriculum Framework for Student Advising and Career Planning
Abstract:
Knowledge representations and interactions are at the forefront of teaching, learning,
and career planning activities in all endeavors of education and career development. University
students are increasingly faced with a myriad of interdisciplinary topics that are seemingly unrelated
when unstructured knowledge representations are presented, especially during advising and career orientation
sessions. This is especially challenging in fast-changing technical domains such as Computer and Data Science
where university curricula are reviewed on an annual basis. This makes it increasingly difficult for instructors
and administrators to present both the big picture as well as the detailed knowledge components of degree programs
to students who face problems in choosing a career and/or establishing a plan of study and assessment. In this talk,
I shall introduce the KCUBE project, a knowledge graph (KG) framework equipped with virtual reality for structuring
and presenting both the overviews of the Computer Science curriculum taught at the Department of Computing in the
Hong Kong Polytechnic University, as well as for students to develop their study with the help of virual tutor/mentor.
We employ computational information storage and retrieval methods, machine learning, and interactive virtual reality to
facilitate users (instructors and students) to better understand, manipulate, and visualize abstract concepts and
relationships in the development of teaching and learning activities in our department.
Bio of the Keynote Speaker 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, with over 56,800 citations and H-index of 96 (source: Google Scholars). He is actively involved in the research community and has served as Editor-in-Chief of Computer & Education: X Realitty (CEXR) by Elsevier, associate editor of 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.
Bio of the Keynote Speaker 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, with over 56,800 citations and H-index of 96 (source: Google Scholars). He is actively involved in the research community and has served as Editor-in-Chief of Computer & Education: X Realitty (CEXR) by Elsevier, associate editor of 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.
Pietro Simone Oliveto, Southern University of Science and Technology (SUSTech), Shenzhen, China.
Speech Title:
Computational Complexity Analysis of Sexual Evolution for the Design of Better General Purpose Algorithms for AI
Abstract:
Large classes of the general-purpose optimisation algorithms at the heart of modern artificial intelligence and machine learning technologies are inspired by models of Darwinian evolution. In this talk we show how the foundational computational complexity analysis of such algorithms leads to an understanding of their behaviour and performance. Such understanding in turn allows informed decisions on how to set their many parameters and how to improve the algorithms to allow for the obtainment of better solutions in shorter time. We provide two concrete examples of how such analyses can lead to counter intuitive insights into how to design sexual evolution inspired algorithms (using populations and recombination) and how to set their parameters such that they can considerably outperform their single trajectory and mutation only (asexual) counterparts at hillclimbing unimodal functions, and at escaping from local optima. We conclude the talk by presenting experimental results that confirm the superiority of the designed algorithms that was proven for benchmark functions with significant structures, for classical combinatorial optimisation problems with practical applications.
Bio of the Keynote Speaker Pietro Oliveto is a Professor of Computer Science at the Southern University of Science and Technology (SUSTech) Shenzhen, China. He received the Laurea degree and PhD degree in computer science respectively from the University of Catania, Italy in 2005 and from the University of Birmingham, UK in 2009. He has been EPSRC PhD+ Fellow (2009-2010) and EPSRC Postdoctoral Fellow (2010-2013) at the University of Birmingham, UK and Vice-Chancellor's Fellow (2013-2016) and EPSRC Early Career Fellow (2015-2020) at the University of Sheffield, UK. Before moving to SUSTech he was Chair in Algorithms at the Department of Computer Science, University of Sheffield, UK.
His main research interest is the performance analysis, in particular the time complexity, of bio-inspired computation techniques including evolutionary algorithms, genetic programming, artificial immune systems, hyper-heuristics and algorithm configurators. He is currently building a Theory of Artificial Intelligence Lab at SUSTech.
He has guest-edited journal special issues of Computer Science and Technology, Evolutionary Computation, Theoretical Computer Science, IEEE Transactions on Evolutionary Computation and Algorithmica. He has co-Chaired the IEEE symposium on Foundations of Computational Intelligence (FOCI) from 2015 to 2021 and has been co-program Chair of the ACM Conference on Foundations of Genetic Algorithms (FOGA 2021) and Theory Track co-chair at GECCO 2022, GECCO 2023 and GECCO 2026. He is part of the Steering Committee of the annual workshop on Theory of Randomized Search Heuristics (ThRaSH), was Leader of the Benchmarking Working Group of the EU-COST Action ImAppNIO, is member of the EPSRC Peer Review College and recently completed his term as Associate Editor of IEEE Transactions on Evolutionary Computation.
Bio of the Keynote Speaker Pietro Oliveto is a Professor of Computer Science at the Southern University of Science and Technology (SUSTech) Shenzhen, China. He received the Laurea degree and PhD degree in computer science respectively from the University of Catania, Italy in 2005 and from the University of Birmingham, UK in 2009. He has been EPSRC PhD+ Fellow (2009-2010) and EPSRC Postdoctoral Fellow (2010-2013) at the University of Birmingham, UK and Vice-Chancellor's Fellow (2013-2016) and EPSRC Early Career Fellow (2015-2020) at the University of Sheffield, UK. Before moving to SUSTech he was Chair in Algorithms at the Department of Computer Science, University of Sheffield, UK.
His main research interest is the performance analysis, in particular the time complexity, of bio-inspired computation techniques including evolutionary algorithms, genetic programming, artificial immune systems, hyper-heuristics and algorithm configurators. He is currently building a Theory of Artificial Intelligence Lab at SUSTech.
He has guest-edited journal special issues of Computer Science and Technology, Evolutionary Computation, Theoretical Computer Science, IEEE Transactions on Evolutionary Computation and Algorithmica. He has co-Chaired the IEEE symposium on Foundations of Computational Intelligence (FOCI) from 2015 to 2021 and has been co-program Chair of the ACM Conference on Foundations of Genetic Algorithms (FOGA 2021) and Theory Track co-chair at GECCO 2022, GECCO 2023 and GECCO 2026. He is part of the Steering Committee of the annual workshop on Theory of Randomized Search Heuristics (ThRaSH), was Leader of the Benchmarking Working Group of the EU-COST Action ImAppNIO, is member of the EPSRC Peer Review College and recently completed his term as Associate Editor of IEEE Transactions on Evolutionary Computation.