Professor Seng is an Adjunct Professor at the University of the Sunshine Coast, specialising in AI, data science, machine learning and intelligent systems for smart technologies. A recognised international leader in AI and IoT with over 20 years of research experience.
Professor Seng has authored four books and over 250 articles (80+ as principal author), including recently Multimodal Analytics for Next-Generation Big Data Technologies & Applications (Springer, principal author) and Artificial Intelligence.
Professor Seng has published extensively in IEEE/IET Q1 journals (impact factors 3.015–11.75) with h-index 39 and over 7000 citations (Google Scholar) and holds a B.Eng. (First Class Honours) and Ph.D. in Engineering from the University of Tasmania, and has held positions at Monash, Griffith, Tasmania, Nottingham and Xi’an Jiaotong-Liverpool as Professor, plus adjunct professor roles at QUT and UNSW.
Recognised by Stanford University (2022–2025) as among the world’s top 2% most-cited scientists for single-year impact, and recipient of the 2022 International Discipline Leading Talent Project (Outstanding).
Research interests include AI, data science, big data, multimodal processing, embedded systems, and IoT, focusing on intelligent data-driven technologies for real-world systems such as smart environments and digital infrastructure.
Over the past five years, Professor Seng has received industry-funded AI/computer vision/automation projects in agriculture exceeded A$1.5 million, boosting productivity. Career total: 25+ project grants (12 as lead), A$5 million in funding (2002–present). Supervised 18+ PhD, 10 Masters, and 25 Honours students to completion. Professor Seng is a Fellow of IET and Senior Member of IEEE.
Research areas
- Artificial intelligence
- Internet of things
- Electrical engineering
- Collaborative AI Dysarthric Speech Recognition System with Data Augmentation Using Generative Adversarial Neural Network
IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 33, pp. 2097–2111, 2025. - Graph Split Federated Learning for Distributed Large-Scale AIoT in Smart Cities
IEEE Open Journal of the Computer Society, vol. 6, pp. 1027–1040, 2025. - CycleGAN*: Collaborative AI Learning With Improved Adversarial Neural Networks for Multimodalities Data
IEEE Transactions on Artificial Intelligence, vol. 5, no. 11, pp. 5616–5629, 2024. - Optimizing Energy Consumption and Provisioning for Wireless Charging and Data Collection in Large-Scale WRSNs With Mobile Elements
IEEE Internet of Things Journal, vol. 10, no. 20, pp. 17585–17602, 2023. - An Automated Identification Approach for Partial Discharge Detection Using Density-Based Clustering Without User Inputs
IEEE Transactions on Artificial Intelligence, vol. 5, no. 1, pp. 310–320, 2024. - Embedded Intelligence: State-of-the-Art and Research Challenges
IEEE Access, vol. 10, pp. 59236–59258, 2022. - Embedded Intelligence: Platform Technologies, Device Analytics, and Smart City Applications
IEEE Internet of Things Journal, vol. 8, no. 17, pp. 13165–13182, 2021. - Data Convexity and Parameter Independent Clustering for Biomedical Datasets
IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 2, pp. 765–772, 2021.
Specialist areas of knowledge include artificial intelligence, data science, machine learning, intelligent engineering systems and applications in smart technologies and environments.