Dr. Linh Nguyen
Associate Professor, Mechatronics Engineering
Campus
Biography
Linh Nguyen received a PhD degree in robotics from the world leading Robotics Institute at the University of Technology Sydney (UTS), Australia in 2015. He then held research fellow positions at Nanyang Technological University, Singapore, and UTS, Australia, until early 2020. He is currently an associate professor at Federation University, Australia.
Linh’s research interests include informative path planning, robotics, artificial intelligence, mobile sensor networks, and machine perception. He has secured millions of dollars in competitive research funding, including highly prestigious grants funded by the Australian and State Governments. He is also the author of more than 100 peer-reviewed publications, mostly in top-ranked journals and flagship conferences within interdisciplinary areas of engineering and computer science.
Linh was a recipient of the Best Paper Awards from the IEEE Innovations in Intelligent Systems and Application conferences in 2021 and 2023, respectively.
Fields of research
- Autonomous agents and multiagent systems
- Control engineering, mechatronics and robotics
- Active sensing
- Publications
Efficient e-waste inventory management through distributed systems: a systematic review of frameworks and strategies
- Journals
- DOI reference: 10.1007/s10163-025-02404-3
A maximum power point tracking control for wind energy conversion systems using regularized data-enabled predictive control
- Journals
- DOI reference: 10.1016/j.engappai.2026.114005
A field-acquired RGB-Depth image dataset for computer vision-based baby broccoli detection and size estimation under varying illumination conditions
- Journals
- DOI reference: 10.1016/j.dib.2026.112621
A neural network surrogate for modelling granular flow dynamics in industrial applications with dynamic boundary conditions
- Journals
- DOI reference: 10.1016/j.powtec.2026.122258
Hierarchical Mixed-Effects and Stacked Machine Learning Ensembles with Data Augmentation for Leakage-Safe E-Waste Forecasting
- Journals
- DOI reference: 10.32604/cmc.2026.074444
LEAP-O: Learning to Predict Dynamic Obstacles for Safe Trajectory Planning
Deep Learning-Enhanced Agoraphilic Navigation for Uneven Terrain and Dynamic Obstacles
- Journals
- DOI reference: 10.1109/TMECH.2026.3722637
