AMLDS 2026 Speakers

Prof. Witold Pedrycz

IEEE Life Fellow

University of Alberta, Edmonton, Canada

Bio: Witold Pedrycz (IEEE Life Fellow) is Professor in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society. His main research directions involve Computational Intelligence, Granular Computing, and Machine Learning, among others. Professor Pedrycz serves as an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer).

Prof. Loi Lei Lai

IEEE Life Fellow, IET Fellow

Chairman, DRPT International Incorporated, Australia

Bio: Professor Loi Lei Lai received his B.Sc. (First Class Hons.) and Ph.D. from the University of Aston, Birmingham, UK, in 1980 and 1984 respectively. His D.Sc. is from City, University of London, London, UK, in 2005. All degrees are in electrical and electronic engineering. Professor Lai is currently Chairman of DRPT International Incorporated, Australia. He was a Pao Yue Kong Chair Professor with Zhejiang University, Hangzhou, China; Professor & Chair of Electrical Engineering with City, University of London, London, UK and University Distinguished Professor with Guangdong University of Technology, Guangzhou, China. Professor Lai received an IEEE Third Millennium Medal, the IEEE Power and Energy Society (IEEE/PES) UKRI Power Chapter Outstanding Engineer Award in 2000, a special award from City, University of London in 2005 and is its honorary graduate, the IEEE/PES Energy Development and Power Generation Committee Prize Paper in 2006 and 2009 (Papers published in IEEE Transactions on Energy Conversion), the IEEE Systems, Man, and Cybernetics Society (IEEE/SMCS) Outstanding Contribution Award in 2013 and 2014, the Most Active Technical Committee Award in 2015 and 2024, and his research team received a Best Paper Award in the IEEE International Smart Cities Conference in October 2020. Professor Lai is an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics: Systems, Editor-in-Chief of the IEEE Smart Cities Newsletter; a member of the IEEE Smart Cities Board of Governor; Member-at-large, IEEE Technical Activities Board (TAB) Committee on Standards & Chair of the Seed Funding ad hoc, Chair of IEEE/SMCS Standards Committee, IEEE/SMCS Fellow Committee Evaluator, IEEE/SMCS Distinguished Lecturer and IEEE/PES Lifetime Achievement Award Committee Assessor. He was a member of the IEEE Smart Grid Steering Committee; the Director of Research and Development Center, State Grid Energy Research Institute, Beijing, China; a Vice President for Membership and Student Activities of IEEE/SMCS; and a Fellow Committee Evaluator and Distinguished Lecturer for the IEEE Industrial Electronics Society. His current research and development interests are in smart cities, smart grid, clean energy and computational intelligence. He is an IEEE Life Fellow and IET Fellow.

Speech Title: Impact of Artificial Intelligence and Data Analytics on Sustainability and Daily Life

Abstract: This talk covers a few important topics from smart cities such as smart energy, smart transportation, smart mobility, smart health, smart education and standards development based on machine learning and data science to enhance citizen’s daily life and sustainability. In smart energy with advanced intelligent control and management approaches, this will play a key role in a carbon-neutral society. Power system operation, control and management could be enhanced with the introduction of batteries such as those from electric vehicles with smart charging. For transportation, this includes transportation technologies in conjunction with Big Data that can improve the transportation efficiency of a city. Major environmental, economic, and technological challenges, for example climate change, economic restructuring, pressure on public finances, digitalization of the retail and entertainment industries, and growth of urban and ageing populations have generated huge interest for cities to be run smartly with the integration of communication technologies and artificial intelligence (AI). AI also offers educators good support by organizing repetitive administrative tasks effectively and enabling more efficient analysis of student data. This allows teachers to spend more quality time adapting the curriculum to the needs of students. Some current international research and development activities will be reported, importance of standards activities and future directions will be discussed.

Prof. Giuseppe Di Fatta

Bio: Giuseppe Di Fatta is a Full Professor at the Free University of Bozen-Bolzano (Italy) since 2022. From 2006 to 2021, he was with the University of Reading (UK), where he also served as Head of the Department of Computer Science from 2016 to 2021. Between 2004 and 2006, he was at the University of Konstanz (Germany), where he was part of the initial development team of KNIME, a widely used data science and machine learning platform. From 2000 to 2004, he worked with the High-Performance Computing and Networking Institute of the National Research Council of Italy, and in 1999 he was a research fellow at the International Computer Science Institute (ICSI) in Berkeley, California. His research interests include machine learning algorithms, data science, and data-driven applications in both scientific and industrial domains. He has authored more than 140 peer-reviewed publications and has been a member of the IEEE since 2002 and a Fellow of the Higher Education Academy (UK) since 2009. He is also a member of the Technical Committee on Machine Learning (TC-ML) of the IEEE Systems, Man, and Cybernetics Society.

Speech Title: Multi-Task Deep Learning

Abstract: This keynote explores the principles and applications of Multi-Task Learning (MTL) within Deep Neural Networks (DNNs). MTL introduces an inductive bias by enabling multiple tasks to be learned jointly, with the goal of achieving better efficiency and superior performance compared to learning each task independently. The core idea is that related tasks can benefit from shared representations and domain information, leading to improved generalisation, reduced overfitting, and greater robustness in the presence of limited or sparse data. Beyond its classical formulation, MTL has demonstrated advantages even when task relationships are only partially known or implicitly defined. From this perspective, MTL can be viewed as a form of transfer learning in which knowledge is transferred across tasks during simultaneous training, in contrast to the sequential approach typical of other transfer learning paradigms. The talk will span both theoretical foundations and practical applications, illustrating how MTL principles translate into real-world deep learning systems and architectures. By examining MTL in the context of DNNs, this talk highlights its potential both as a framework for understanding deep learning dynamics and as a pathway toward more efficient, scalable, and interpretable models.