Speakers

Biography:  

As a highly accomplished computer scientist and data mining expert with over 35 years of experience, I have demonstrated exceptional leadership, expertise, and vision in the field.

Over the course of my career, I have achieved numerous successes and made significant contributions to the industry. I began as an entry-level computer programmer in 1988 and advanced to become a Professor of Data Mining at the Faculty of Computer Science and Information Technology at the University of Malaya. I obtained my tertiary academic qualifications from Oklahoma City University and the University of Malaya, and I have published more than 90 academic papers in top-tier journals, including Information Fusion and the International Journal of Information Management.

I have a remarkable H-index and number of citations in Web of Science, Scopus, and Google Scholars databases, and I have supervised numerous students at all levels of study. My areas of research include data warehouse, data mining, deep learning, IoT, activity recognition, wearable sensors, accelerometers, heart arrhythmia, electrocardiograph, supraventricular premature beat, multivariate time series, edge computing, task scheduling, data streams, mobile computing, speaker verification, language recognition, clustering algorithms, MapReduce, stock market, and sentiment analysis.

Title：Modern Data Mining on Big Data and Digital Management: A 2025 Perspective

Abstract: As digital transformation accelerates in 2025, data mining techniques have evolved to meet the increasing complexities of big data and digital management. This talk will focus on how Large Language Models (LLMs), Small Language Models (SLMs), and Generative AI (GenAI) can be applied in a real-world scenario, particularly for small and medium-sized enterprises (SMEs). AI and ML continue to revolutionize automation in pattern detection, predictive analytics, and decision intelligence. The integration of cloud and edge computing has enabled real-time data processing, reducing operational costs and enhancing efficiency. Industries such as finance, healthcare, and property development leverage these technologies to drive informed decision-making. However, challenges persist, including governance, compliance, ethical AI, bias mitigation, scalability, and interoperability. Standardized data exchange protocols are essential to streamline integration and maintain regulatory compliance. AI-powered decision intelligence systems are transforming strategic planning and operations. Despite increasing automation, human expertise remains crucial in interpreting complex data patterns, necessitating human-centric AI approaches. This paper explores the latest trends, challenges, and opportunities in data mining and digital management, demonstrating how emerging technologies can be leveraged for innovation and competitive advantage in the evolving digital landscape.

Prof. Jianhua Zhang, Oslo Metropolitan University, Norway

Biography: Prof. Jianhua Zhang has been Professor in Computer Science at Oslo Metropolitan University (OsloMet), Norway since 2018. Before joining OsloMet, he spent a stint working at a French IT company, Vekia (Lille, France) as Scientific Director. He was Professor at East China University of Science and Technology (Shanghai, China) between 2007 and 2017. He received PhD from Ruhr University Bochum, Germany and did postdoc. at The University of Sheffield, UK. He was Guest Scientist at Dresden University of Technology, Germany from 2002 to 2003 and Visiting Professor at Technical University of Berlin, Germany during 2008-2015. His research interests include computational intelligence, machine learning and pattern recognition, data modeling and analytics, intelligent systems and control, modeling and control of complex systems, biomedical signal processing and data analysis, and neurocomputing (in particular, neuroergonomics and affective computing). He serves as Chair of IFAC (International Federation of Automatic Control) Technical Committee on Human-Machine Systems for two consecutive terms (2017-2023), Vice Chair of IEEE Norway Section, and Vice Chair of IEEE CIS Norway Chapter. He is on editorial board of 4 international scientific journals, including Frontiers in Neuroscience, Cognitive Neurodynamics, and Cognition, Technology and Work. He was chair or keynote speaker for a number of international scientific conferences.

Title：Stock Market Forecasting via Transformer Models

Abstract: This talk examines the effectiveness of various models, such as traditional models like ARIMA and Linear Regression and advanced machine learning (ML) models like Long Short-Term Memory (LSTM) networks, Prophet, and Transformers, in predicting stock prices. Ensemble learning, which combine predictions of different models to reduce their bias and variance, are also examined. Furthermore, optimizing ML models for stock price prediction requires hyperparameter tuning to ensure that models are effective and efficient in capturing the complexities and volatility of real-world financial markets. To maximize accuracy while minimizing computational costs, different hyperparameter (such as learning rate, number of layers in the network, and batch size) tuning strategies are investigated, including grid search, random search, and Bayesian optimization.
The extensive real stock data analysis experiments showed that the transformer model stacked with linear regression achieves superior prediction performance. We found that it is important to adapt model architectures to specific stock market characteristics and that ensemble learning methods can improve the accuracy and reliability of stock time series forecasting. The findings of this study may provide useful insight into the proper selection of model class for stock closing price dynamics as well as informed stock investment decisions and portfolio management.

Assoc.Prof. Yonghui Wu,  FudanUniversity, China

Biography:  Dr. Yonghui Wu, associate professor at Fudan University, visiting scholar at Stony Brook University, and adjunct professor at Quanzhou University of Information Engineering, the chair of the ICPC Asia Training Committee. He won three medals in ACM ICPC World Finals for Fudan University. His book series “Collegiate Programming Contests and Education” has been published in simplified and traditional Chinese and English: the former by respective publishers of mainland China and Taiwan, and the latter, the first book’s translation, by CRC Press. Since 2013, he has been giving lectures not only in China, but also in other countries.

Title：Constructions of Teaching Materials, Curriculums, and the Teaching System Cross-Region for "Solving Problems by Programming"

Assoc. Prof. Minghan Li,  Soochow University, China

Biography:  Minghan Li, holds a Ph.D. in Computer Science and is currently an associate professor at Soochow University, Suzhou, China. He graduated with his doctorate from Université Grenoble Alpes, France, between 2020 and 2023, and previously obtained his bachelor's degree in Information Engineering from Xi'an Jiaotong University and a master's degree in Computer Technology from Xidian University. As a first author, he has published multiple papers in key journals and international conferences in his field, including the ACM Transactions on Information Systems and ACM SIGIR, and he has two authorized software copyrights. Dr. Li received the French IDEX International Mobility Scholarship, and conducted a visiting scholar at the National University of Singapore. He has served as a reviewer for high-level journals such as Information Processing & Management, ACM Transactions on Information Systems, and Artificial Intelligence, and was a PC member for ECIR 2024. His current research focuses on information retrieval (search algorithms) with large language models, dialogue systems, and question-answering systems. Additionally, he possesses extensive programming experiences with enterprise and multiple platforms.

Title：Information Retrieval: Evolving from Pre-trained to Large Languages Models

Abstract: Since the introduction of the Transformer architecture, pre-trained language models have demonstrated significant efficacy across various tasks in natural language processing. In the domain of information retrieval (IR), particularly concerning text relevance ranking, numerous models have been proposed, continually updating the algorithms used in everyday search engines. This report will introduce a variety of algorithms based on pre-trained language models, including interaction-based, representation-based approaches and others. Furthermore, given the recent emergence of large language models (LLMs) in the field of artificial intelligence, the report will also cover IR algorithms based on LLMs, as well as the role of IR in augmenting dialogue and question-answering capabilities of LLMs.