Tuan Minh Le
Tuan Minh Le is a PhD Scholar in the School of Science, Engineering and Technology at RMIT University Vietnam and a Teaching & Research Assistant at the School of Electrical Engineering, International University (VNU-HCM).
His doctoral thesis focuses on advanced machine learning models for predictive maintenance of industrial rotating machinery, utilizing wavelets, CNNs, and Kolmogorov-Arnold Networks (KAN) to engineer next-generation intelligent diagnostic systems.
Current & Past Affiliations
RMIT University
2024 - Present
PhD Scholar at the School of Science, Engineering and Technology. Researching predictive machinery maintenance under a prestigious doctoral scholarship.
International University
2020 - Present
Teaching & Research Assistant, School of Electrical Engineering, VNU-HCM. Developing smart waste monitoring and teaching electronic courses.
Hella Vietnam
2019 - 2020
Embedded Software Engineer & Electronics Lab Specialist. Designed and built functional software modules for automotive body electronics.
Core Research Pillars
Metaheuristic Optimization
Developing hybrid evolutionary algorithms and swarm intelligence models (e.g., Adaptive Particle Swarm Optimization and Grey Wolf Optimization) to perform high-dimensional wrapper-based feature selection and optimize hyperparameter spaces in neural networks.
Biomedical Data Analytics
Applying robust machine learning and deep learning models to predict and classify clinical conditions, including automated epileptic seizure detection, early diabetes prediction, and heart failure onset analysis.
Industrial IoT & Fault Diagnosis
Designing intelligent real-time control architectures and deep-learning-driven predictive maintenance systems for industrial rotating machinery and bearing fault classification under noisy environments.
Academic Journey & Education
Ph.D. in Science, Engineering & Technology
RMIT University Vietnam | 2024 - 2028 (Candidate)
Thesis Topic: Advanced Machine Learning Models for Predictive Maintenance of Rotating Machinery
Focuses on developing intelligent real-time fault diagnostics for industrial rotating systems. Engineered a high-accuracy predictive framework combining Continuous Wavelet Transform (CWT) and deep Convolutional Neural Networks (CNNs), demonstrating superior classification results against Random Forest, MLP, and XGBoost baselines.
Master of Electronics Engineering (M.Eng.)
International University, Vietnam National University, HCMC | 2019 - 2022
Thesis Topic: Improve the machine learning binary classification model using a novel wrapper-based feature selection
Applied advanced machine learning models for early clinical disease diagnosis (e.g., heart failure, diabetes). Designed a novel Grey Wolf Optimization (GWO)-based wrapper feature selection method that successfully reduced input attributes by 50% while significantly outperforming standard SVM, Decision Tree, and KNN classifiers.
Bachelor of Electrical Engineering (B.Eng.)
International University, Vietnam National University, HCMC | 2014 - 2018
Thesis Topic: Design and Implement the Web of Things for Smart Building Application
Designed a wireless Web of Things platform utilizing Contiki OS and 6LoWPAN protocols for smart building automation. Implemented a robust Raspberry Pi CoAP-to-HTTP proxy and real-time monitoring web server.
Featured Projects & Global Exposure
Technical Expertise & Skills
Programming & Modeling
- Python (PyTorch, TensorFlow, Scikit-learn)
- C & C# (.NET Core, Embedded Firmware)
- MATLAB (Signal Processing & Control Systems)
- Ubuntu Linux & Shell Scripting
Hardware & PCB Design
- Altium Designer & Cadence Allegro
- OrCAD PCB Editor & Schematic Capture
- OnShape 3D CAD modeling
- IoT platforms (ESP32, 6LoWPAN, Raspberry Pi)
Algorithms & Systems
- Swarm Heuristics (GWO, PSO, GA)
- Deep CNNs & Signal Wavelet Transforms (CWT)
- Kolmogorov-Arnold Networks (KAN)
- Embedded RTOS & Contiki OS
Honors & Academic Awards
RMIT PhD Scholarship
Awarded in 2024
Prestigious fully-funded doctoral research scholarship.
Excellent Graduate Publications
International University | Class of 2022
Recognized for exceptional peer-reviewed journal papers.
Outstanding Academic Scholarship
Awarded in 2017
Encouragement scholarship for ranking top in electrical engineering.