Cassio de Campos (Eindhoven University of Technology, Netherlands)

Title: TBD

Abstract.

Naoki Masuyama (Osaka Metropolitan University, JAPAN)

Title: Adaptive Resonance Theory-based Clustering and its Extensions

Abstract. Clustering is a fundamental technique for extracting meaningful structure from data and has been applied in a wide variety of fields. Self-organizing growing clustering methods, such as Growing Neural Gas (GNG) and Self-Organizing Incremental Neural Networks (SOINN), can adaptively capture the geometric structure of data through topological networks composed of nodes and edges. However, these methods are not always well suited to continual learning scenarios, where new information must be incorporated without destroying previously acquired knowledge. Adaptive Resonance Theory (ART)-based clustering provides a principled framework for addressing this challenge through a balance between stability and plasticity. This talk introduces the fundamentals of ART-based clustering and discusses its recent extensions.

Bio

Naoki Masuyama received the B.E. degree in Aerospace Engineering from Nihon University, Funabashi, Japan, in 2010, the M.E. degree in Human Mechatronics Systems from Tokyo Metropolitan University, Hino, Japan, in 2012, and the Ph.D. degree in Computer Science from the Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia, in 2016. He was a Postdoctoral Research Fellow at the University of Malaya from 2016 to 2017. From October 2017 to March 2022, he was an Assistant Professor with the Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan. After the university integration in 2022, he continued as an Assistant Professor with the Department of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan, from April 2022 to September 2022. Since October 2022, he has been an Associate Professor with the Department of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University.
His research interests include clustering, data mining, and continual learning, with a particular focus on supervised and unsupervised continual learning methods that adaptively and efficiently extract useful information from dynamically changing environments. He has also worked on applications of continual learning to related areas such as evolutionary computation and explainable machine learning. He has authored more than 100 peer-reviewed papers, including publications in IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems, and IEEE Transactions on Evolutionary Computation. His work has been recognized by several awards, including the Springer Best Paper Award at EMO 2019, the Best Paper Award at IFSA 2023, and the Emerging Research Leader Award from the Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT) in 2024.