Vincent Cheng Siong Lee

IEAust Fellow, IEEE Senior Member

Monash University, Australia

BIO:

Assoc Professor Lee’s multi-disciplinary research spans IT, Digital Health, Signal/Image Processing, FinTech, Learning Analytics, Explainable AI, Deep ML, Computer Vision, Multi-agent Autonomous Systems for sustainable environment development.

His 200+ papers published in SCImago Q1 journals: IEEE TSP, IEEE TKDE, IPM, Neurocomputing, Expert Systems with Applications, Automation-in-Construction, CACIE, Computer Methods & Program in biomedicine, etc; and in CORE A/A* Int-Conf proceedings: AAAI, IJCAI, ICDM, ICWS, ICDE, PAKDD, CIKM, ACM KDD, ACMMM, IEEE IC Signal Processing, etc.

Lee has delivered keynote speakers for several IEEE/ACM Flagship conferences and General Chair/Co-chair of steering and technical program Committees. He has supervised completion 28 PhDs.

Speech title: Machine Learning Models for Depression Detection and Classification

Abstract: 

Depression is a leading mental health challenge worldwide, and adolescents are especially at risk due to ongoing changes in brain development and social environment. Using Electroencephalography (EEG), which captures brain activity through non-invasive scalp electrodes, offers a practical and precise way to monitor neural patterns and has shown potential for identifying signs of depression. This talk presents an automated EEG-based framework targeting adolescents aged 18–19 for early depression classification. Discussion of ML-based classification models via publicly available EEG dataset filtered by key features and signals were preprocessed to extract four distinct feature sets. Multiple classifiers such as Naive Bayes, Logistic Regression, K-Nearest Neighbours, Random Forest, Multilayer Perceptron, and XGBoost—were evaluated across all feature types. The proposed framework offers a reproducible, optimizer-agnostic pipeline for adolescent mental health screening using EEG and machine learning.

Aslina Baharum, IEEE Senior Member

Sunway University, Malaysia

BIO:

Ts. Dr Aslina Baharum is an Associate Professor and UX Researcher at the School of Engineering and Technology, Sunway University. Previously, she was a Senior Lecturer at Universiti Teknologi MARA (UiTM), and Universiti Malaysia Sabah (UMS). She also has industry experiences where she worked as an IT Officer for the Forest Research Institute of Malaysia (FRIM). She had experienced more than 20 years in the IT field.

She received a PhD in Visual Informatics (UKM), a Master Science degree in IT (UiTM) and graduated Bachelor of Science (Hons.) in E-Commerce from UMS. She is a member of the Young Scientists Network - Academy of Science Malaysia, Senior Member IEEE, and certified Professional Technologist from MBOT, and served as MBOT/MQA auditor.  

She won several medals in research and innovation showcases and was awarded several publication awards, teaching awards, Excellence Service award, and UMS Researchers Awards. She has co-authored and editor books, published several books of chapters (>20), technical papers in conferences and peer-reviewed and indexed journals (>60) papers. She also served as editor for several journals, scholarly contributed as a committee, editorial team and reviewers, and given several invited/ plenary talks at conferences.  

Her research interests include UX/UI, HCI/Interaction Design, Product & Service Design, Software Engineering & Mobile Development, Information Visualization & Analytics, Multimedia, ICT, IS and Entre/Technopreneurship. Her workshops and talks covered Entrepreneurship, Video/Image Editing, E-Commerce/Digital Marketing, AR/VR/MR/XR in STEM, Design Thinking and etc.

She is also a Certified Professional Entrepreneurial Educator, Executive Entrepreneurial Leaders and HRDF Professional Trainer. 

Speech title: Humanizing AI: Redefining User Experience in the Age of Intelligent Systems

Abstract: 

As artificial intelligence (AI) becomes increasingly integrated into everyday life, designing user experiences that are both intuitive and adaptive has emerged as a critical challenge. The field of AI-User Experience (AI-UX) seeks to bridge the gap between human needs and intelligent technologies, ensuring AI systems enhance, rather than hinder, our digital interactions. However, current AI implementations often struggle with transparency, user control, and personalization, creating barriers to user trust and adoption. This keynote addresses these challenges by exploring the intersection of AI and UX design, aiming to redefine user experiences in the age of intelligent systems. The objective is to provide a framework for developing human-centered AI solutions that prioritize user engagement and ethical considerations. Utilizing a combination of design thinking approaches, user behavior analysis, and AI-driven interaction modeling, the study identifies key principles for creating adaptive interfaces and predictive systems that are both useful and usable. The findings reveal strategies for improving user satisfaction by incorporating explainability, personalization, and proactive feedback mechanisms into AI-driven interfaces. The hope is that this research will guide practitioners in designing AI-powered products that are not only functional but also empathetic, thus fostering a more seamless and human-centered integration of AI into digital ecosystems.

Yi Lou, Harbin Institute of Technology (Weihai), China

Bio:

Main research areas: full-duplex underwater acoustic communication and underwater small target detection. Published two English monographs (first author, Springer) and one Chinese monograph. Served as a reviewer for journals such as IEEE JSAC and the IEEE Trans. series. As the first author or corresponding author, published over 20 SCI papers in international academic journals, including one hot paper and one highly cited paper. Received the WUWNet'21 Best Poster Award and served as the chair of the DMCIS 2025 conference. Acted as an editorial board member for journals such as "Digital Ocean and Underwater Offense and Defense," and led more than 10 research projects, including one project funded by the National Natural Science Foundation and 13 provincial and ministerial level projects.

Speech title: A Comprehensive Analysis of Quaternion-Based DOA Estimation for Acoustic Vector Sensor Arrays: Algorithms, Performance, and Theory

Abstract:

Acoustic Vector Sensor (AVS) arrays are pivotal for underwater Direction of Arrival (DOA) estimation. Quaternion algebra provides a potent but complex framework for processing the four-channel AVS data. This presentation delivers a comprehensive analysis of quaternion-based DOA algorithms, systematically exploring various modeling strategies, from simple channel selection to advanced unitary transformations.

Through comparative analysis, we demonstrate that the quaternion framework significantly enhances DOA estimation, offering superior spatial resolution and robustness, particularly with coherent sources, low Signal-to-Noise Ratios (SNR), and limited snapshots. Furthermore, we establish a rigorous theoretical foundation by deriving a closed-form expression for the Mean Squared Error (MSE) of these algorithms. This contribution not only enables accurate performance prediction but also validates the practical advantages of applying quaternion theory to underwater acoustic signal processing.

Weichao Yang, Northwestern Polytechnical University, China

Bio: 

Graduated with a Ph.D. in Signal and Information Processing from Harbin Engineering University in July 2012. In August of the same year, he joined the Xi’an Branch of the Fifth Academy of Aerospace Science and Technology Corporation. As a core technical expert, he participated in the demonstration and development of electronic reconnaissance payloads for multiple satellites and developed several communication reconnaissance systems for a series of satellites. He has presided over seven national defense projects at the provincial or ministerial level and participated in three others. He has published more than twenty academic papers, with 16 indexed by SCI/EI, and applied for or been granted seven patents.

Speech title: Technologies of Space-based Electromagnetic Spectrum Sensing

Abstract:

This report provides a comprehensive overview of space-based electromagnetic spectrum sensing, covering its concept, key technologies, research progress, and future trends. It begins by defining space-based electromagnetic spectrum sensing and its significance in utilizing and monitoring the electromagnetic spectrum from space. Next, it examines the core technologies enabling space-based electromagnetic spectrum sensing, including advanced sensors and satellite integration. The report also compares the current state of research domestically and internationally, highlighting global advancements and strategies. Finally, it explores emerging trends, offering insights into the future development and applications of this cutting-edge field.