Scientific Machine Learning
Operator learning, physics-informed learning, and data-driven models for dynamical systems.
RESEARCH ENGINEER
Huy Hoang Le
Research Engineer at Powermore Ltd., working at the intersection of scientific machine learning, prognostics and health management, uncertainty quantification, and power-system applications.
RESEARCH
Research focus
Prognostics & Health Management
Remaining useful life prediction, health indicators, fault diagnosis, and maintenance analytics.
Uncertainty Quantification
Conformal prediction, calibrated Bayesian methods, and reliable predictive intervals under shift.
Power Systems
Digital twins, transient modeling, inverter dynamics, and intelligent control of distribution networks.
SELECTED WORK
Selected publications
Generalization Bounds and Sample Complexity for Remaining Useful Life Prediction from Complete Degradation Trajectories
Information-Theoretic Limits of Health Indicator Construction for Remaining Useful Life Prediction
Transformer Fault Diagnosis Using an Efficient Simulation-Driven Variational Quantum Classifier with Domain-Aware Feature Encoding
Indicator-aware design of run-to-failure degradation tests for data-driven prognostics
CONTACT
Let’s connect
For research collaboration, academic opportunities, or technical discussion, please use the links below.