Keynote 1

Speaker: Xin Yao (School of Data Science, Lingnan University, Hong Kong SAR)

Time: October 26 8:20-9:00

Biosketch of the speaker: https://scholar.google.co.uk/citations?user=UUtYPl4AAAAJ&hl=en

Short Bio:

Xin Yao is Tong Tin Sun Chair Professor of Machine Learning at Lingnan University, Hong Kong, China, and a part-time Professor of Computer Science at the University of Birmingham, UK. His major research interests include evolutionary computation, ensemble learning and trustworthy artificial intelligence. He has been working on ensemble approaches to online learning and class imbalance learning for many years. He is an IEEE fellow, a former (2014-15) President of IEEE Computational Intelligence Society (CIS) and a former (20003-08) Editor-in-Chief of IEEE Transactions on Evolutionary Computation. His research work won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010, 2016 and 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE Transactions on Neural Networks Outstanding Paper Award, and many other best paper awards. He received a Royal Society Wolfson Research Merit Award in 2012, the IEEE CIS Evolutionary Computation Pioneer Award in 2013, and the 2020 IEEE Frank Rosenblatt Award.

Title: Online Learning of Data Streams with Concept Drift

Abstract:

Data stream mining is a challenging task because the data come only one or a chunk at a time. An online learner has to learn while operating continuously. Such a scenario occurs in numerous condition monitoring applications, e.g., fault diagnosis. To make the situation more challenging, the underlying distribution of the data stream may change over time (i.e., concept drift). This talk first describes the learning-in-the-model-space framework, which can be used effectively to learning data streams with few assumptions. Online fault diagnosis will be used as an example to illustrate how learning-in-the-model-space can facilitate detecting and classifying unknown faults. Then the talk will present an ensemble approach to tackling concept drift, i.e., adapting ensemble diversity after a drift is detected in order to learn new concept quickly and more accurately. Finally, the talk will introduce a new method for detecting both real and virtual drifts more accurately.

Keynote 2

Speaker: Dipti Srinivasan (National University of Singapore, Singapore)

Time: October 26 9:10-9:50

Short Bio:

Dipti Srinivasan is a Professor in the Department of Electrical & Computer Engineering, where she also heads the Centre for Green Energy Management & Smart Grid (GEMS). Prior to joining NUS, Professor Srinivasan worked as a Postdoctoral Fellow at University of California at Berkeley from 1994 to 1995. Her research interests are primarily focused on the areas of smart grid and computational intelligence, as well as renewable energy systems and multiobjective optimization. She has secured over S$20 Million in external funding for research projects in these areas. Her current research focuses on the development of novel computational intelligence-based models and methodologies to aid the integration of the new Smart Grid technologies into the existing infrastructure so that power grid can effectively utilize pervasive renewable energy generation and demand-side management programs, while accommodating stochastic load demand. She has authored more than 400 publications which have been highly cited.

Professor Dipti is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and was awarded the IEEE Singapore Outstanding Volunteer award in 2020 and Outstanding Engineer award in 2010. Professor Dipti was recently awarded the 2022 IEEE PES Wanda Reder Pioneer in Power Award for her “Leadership and valuable contributions to the power engineering profession, education and excellent volunteerism”. She is currently serving as an Associate Editor of IEEE Transactions on Sustainable Energy, IEEE Transactions on Smart Grid, IEEE Transactions on Artificial Intelligence, and journal of Solar Energy.

At the ECE department of National University of Singapore, she has been teaching courses in the areas of Sustainable Energy systems, Smart Grid, and computational intelligence methods. She was the winner of NUS Annual Teaching Excellence Award in year 2007, 2008 and 2009, and was placed on the Honours list in 2010. She is the recipient of Engineering Educator Award from the Faculty of Engineering, NUS in 2011 and 2012.

Title: Data Analytics for Uncertainty Capturing and Management in Smart Energy Systems

Abstract:
This lecture will explore the critical role of data analytics in capturing and managing uncertainties within smart energy systems. The discussion will focus on three key areas: (1) the challenge of modeling uncertainties in renewable energy generation, particularly from rooftop photovoltaic (PV) systems; (2) addressing the variability in energy demands at both the community and building levels; and (3) strategies for managing uncertainties to ensure the efficient and reliable distribution of energy. Real-world examples will be presented to illustrate how these techniques are applied in practice. Additionally, the lecture will highlight emerging challenges and innovative solutions for uncertainty management as smart energy systems evolve in complexity.

Keynote 3

Speaker: Z. Jane Wang (University of British Columbia, Canada)

Time: October 26 10:20-11:00

Short Bio:

Z. Jane Wang received the B.Sc. degree from Tsinghua University in 1996 and the M.Sc. and Ph.D. degrees from the University of Connecticut in 2000 and 2002, respectively, all in electrical engineering. She has been Research Associate at the University of Maryland, College Park from 2002 to 2004. Since 2004, she has been with the ECE dept. at the University of British Columbia (UBC), Canada, and she is currently Professor. She is Fellow of IEEE and the Canadian Academy of Engineering (FCAE). Her research interests are in the broad areas of signal processing and machine learning, with current focuses on digital media security and biomedical data analytics. She has been key Organizing Committee Member for numerous IEEE conferences and workshops （e.g. Technical Chair or General Chair for IEEE ICIP 2020, 2025 and 2026）. She has been Associate Editor for the IEEE TSP, SPL, TMM, TIFS, TBME, and SPM, and Editor-in-Chief for the IEEE Signal Processing letters.

Title: 2D-Image Based Pose Estimation: Methods and Applications

Abstract:

Healthcare systems are complex and challenging, however deep learning has been revolutionizing healthcare. Keypoint-based pose estimation is an important learning topic with board applications in medical and healthcare systems. As 2D images are cost effective and easy to capture, keypoint detection using 2D images has been attracting increasing research interest. This talk gives a brief review of my group’s recent research efforts in exploring 2D-image based pose estimation, from both methodology and biomedical application aspects, by investigating: 3D human pose estimation; hand pose estimation; pose estimation based assessments in Parkinson’s Disease; and keypoint based pose assessments using horse images. Particularly, a critical concern in real-world problems is the scarcity of annotated data. We propose innovative strategies (e.g., self-supervision, partial annotation, data synthesis) for training deep learning models without or reducing the need for explicit annotated data. The talk will conclude by brainstorming future research directions.

Keynote 4

Speaker: Jie Lu (University of Technology Sydney, Australia)

Time: October 26 10:20-11:00

Short Bio:

Distinguished Professor Jie Lu is a world-renowned scientist in the field of computational intelligence, primarily known for her work in concept drift, fuzzy transfer learning, recommender systems, and decision support systems. She is an IEEE Fellow, IFSA Fellow, Australian Computer Society Fellow, and Australian Laureate Fellow. Professor Lu is the Director of the Australian Artificial Intelligence Institute (AAII) at University of Technology Sydney (UTS), Australia. She has published six research books and over 500 papers in leading journals and conferences; won 10 Australian Research Council (ARC) Discovery Projects as leading chief investigator and over 20 Linkage and industry projects; and has supervised 50 PhD students to completion. Prof Lu serves as Editor-In-Chief for Knowledge-Based Systems and International Journal of Computational Intelligence Systems. She is a recognized keynote speaker, delivering over 40 keynote speeches at international conferences. She is the recipient of NeurIPS Outstanding Paper Award (2022), two IEEE Transactions on Fuzzy Systems Outstanding Paper Awards (2019 and 2022), Australasian Artificial Intelligence Distinguished Research Contribution Award (2022), Australian NSW Premier’s Prize on Excellence in Engineering or Information & Communication Technology (2023) and the Officer of the Order of Australia (AO) in the Australia Day 2023.

Title: 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 many decision-related applications. A promising solution for coping with persistent environmental change and avoiding system performance degradation is to build a detection, understanding and adaptive system. This talk will present a set of methods and algorithms that can effectively and accurately detect, understand, and adapt concept drift. The main contents include:

1. concept drift detection: 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, then further described in unstructured data streams;
2. concept drift understanding: algorithms for determining a drift region to identify when and where a concept drift takes place in a data stream, and a local drift degree measurement that can continuously monitor regional density changes;
3. concept drift adaptation: methods and algorithms for model adaptation as well as solutions for redundancy removal. These techniques can be applied to data-driven real-time prediction and decision support in complex data stream environments.

Keynote 5

Speaker: Pau-Choo (Julia) (Chung, Taiwan China)

Time: October 27 9:10-9:50

Short Bio:

Pau-Choo (Julia) Chung (S’89-M’91-SM’02-F’08) received the Ph.D. degree in electrical engineering from Texas Tech University, USA, in 1991. She then joined the Department of Electrical Engineering, National Cheng Kung University (NCKU), Taiwan, in 1991 and has become a full professor in 1996. She served as the Head of Department of Electrical Engineering (2011-2014), the Director of Institute of Computer and Communication Engineering (2008-2011), NCKU. She was elected Distinguished Professor of NCKU in 2005 and received the Distinguished Professor Award of Chinese Institute of Electrical Engineering in 2012. She also served as the Dean of College of Electrical Engineering and Computer Science at NCKU, Taiwan.

Dr. Chung’s research interests include computational intelligence, machine learning, computational pathology, medical image analysis, and pattern recognition. She currently is focusing on Whole Slide Image (WSI) pathology image analysis and has built ALOVAS platform. She served as an Associate Editor of IEEE Transactions on Neural Network and Learning Systems and an Associate Editor of IEEE Transactions on Biomedical Circuits and Systems. Currently she is an Associate Editor of IEEE Transactions on Artificial Intelligence.

Dr. Chung served on two terms of ADCOM member (2009-2011, 2012-2014) in IEEE CIS, the Chair of CIS Distinguished Lecturer Program (2012-2013), the Chair of Women in Computational Intelligence (2014), and the Vice President for Members Activities of IEEE CIS Society. She also served on two terms of BoG member in IEEE Circuit and Systems (CAS) Society. She is the founder of Women in CAS (WiCAS) in IEEE CAS Society. She is a Member of Phi Tau Phi honor society and is an IEEE Fellow since 2008. Currently she serves as the Vice President for Education of IEEE CIS.

Title: AI-driven Pathology image analysis and its challenging issues

Abstract:

The current diagnosis of pathology images is performed by pathologists who examine tissue slides under a microscope at various magnification levels. This traditional approach is often time-consuming, subjective, and lacks precise quantification. Advances in AI-based image analysis have paved the way for digital pathology, where AI serves as a valuable tool in assisting the analysis of pathology images. In this talk, we will introduce several AI-driven approaches for pathology image analysis. We will also present ALOVAS, a platform designed for the visualization and analysis of pathology images. Furthermore, we will discuss the challenges associated with training AI models for pathology image analysis, including the labor-intensive process of labeling data and the development of methods that require fewer labeled images. Additionally, we will address the issue of performance degradation caused by variations across hospitals and different scanners.

Keynote 6

Speaker: Sansanee Auephanwiriyakul (Computer Engineering Department, Faculty of Engineering, Chiang Mai, Thailand)

Time: October 27 10:20-11:00

Short Bio:

(S’98–M’01–SM’09) received the B.Eng. (Hons.) degree in electrical engineering from the Chiang Mai University, Thailand (1993), the M.S. degree in electrical and computer engineering and Ph.D. degree in computer engineering and computer science, both from the University of Missouri, Columbia, in 1996, and 2000, respectively. After receiving her Ph.D. degree, she worked as a post-doctoral fellow at the Computational Intelligence Laboratory, University of Missouri-Columbia. She is currently an Associate Professor in the Department of Computer Engineering and a deputy director of the Biomedical Engineering Institute, Chiang Mai University, Thailand. Dr. Auephanwiriyakul is a senior member of the Institute of Electrical and Electronics Engineers (IEEE). She is an Associate Editor of the IEEE Transactions on Fuzzy System, the IEEE Transactions on Neural Networks and Learning Systems, IEEE Computational Intelligence Magazine, IEEE Transactions on Artificial Intelligence, Engineering Applications of Artificial Intelligence, and ECTI Transactions on Computer and Information Technology. She was a general chair of the IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2016). She was a general chair of the IEEE World Congress on Computational Intelligence (WCCI) 2024 (IEEE International Conference on Fuzzy Systems 2024) and will be a general co-chairs of the IEEE World Congress on Computational Intelligence (WCCI) 2026 (IEEE International Conference on Fuzzy Systems 2026). She was a Technical Program Chair, Organizing Committee in several major conferences including the IEEE International, Conference Fuzzy Systems. She is also a member of several important IEEE CIS technical committees. Her next task will be IEEE CIS VP-member activities for the year of 2025 – 2026.

Title: Fuzzy Pattern Recognition in Data Analysis

Abstract:

Data Analysis is a process to analyze data in terms of representing, describing, evaluating, interpreting the data using statistical methods. Data can come in the form of statistical representation or a vector of numbers in which numeric pattern recognition algorithms can deal with this type of data set. Another type of data can be in the form of syntactic data. For this type of data set, there is another research branch in pattern recognition called syntactic pattern recognition that is able to analyze it. Each sample in syntactic data set is normally represented as a string. The strings in the same data set can have different lengths. Also, the string does not have any mathematical meaning that we can calculated as if they are vectors of numbers.

One of the popular theories used in data analysis is Fuzzy set theory, an extension of the classical set introduced by Lotfi Zadeh in 1965. Since then, there are many theories and applications developed based on Fuzzy set theory. In this talk, there are three parts on the utilization of the Fuzzy pattern recognition in data analysis. First, we will show how to develop a fuzzy algorithm in a decision making when the data are a collection of fuzzy vectors (a vector of fuzzy numbers). Another is how to incorporate the Fuzzy set theory into a set of feature generation in the classification problem. The last part of the talk is how to incorporate the Fuzzy set theory into string grammar pattern recognition. All algorithms in this talk are developed at Computational Intelligence Research Laboratory, Chiang Mai University. In each part of the talk, we will show applications of these algorithms in several real-world problems, e.g., sign language translation system, face recognition, health applications and person identification.