Research
Our Research Vision
The SPIN Group develops computational methods that transform raw sensor data into actionable knowledge about material health. Our research sits at the intersection of physics-based sensing, signal processing, and machine learning, enabling intelligent, automated inspection systems that scale across modalities and applications.
Research Themes
Three interconnected pillars that define our approach to computational NDE
Physics-Based Sensing & Signal Processing
We acquire and process data from terahertz, ultrasonic, electromagnetic, and X-ray modalities. Our focus is on extracting physics-informed features, time-of-flight, spectral content, attenuation maps, that capture the signatures of subsurface damage.
Machine Learning for NDE
We develop weakly supervised and self-supervised learning frameworks that reduce reliance on manual annotation. Our pipelines combine classification, localization, and quantification, from Grad-CAM pseudo-labels to physics-informed feature maps.
Cross-Modal Validation & Deployment
We benchmark AI-driven inspection across modalities using X-ray ground truth, and build end-to-end pipelines designed for real-world deployment, from laboratory prototyping to field-ready systems.
Sensing Modalities
Multi-modal data acquisition and cross-validation for robust damage characterization
Terahertz (THz)
Time-domain spectroscopy & imaging
Ultrasonics
Phased-array & guided wave inspection
X-Ray
Radiographic ground truth & validation
Electromagnetic
FDTD simulation & EM characterization
Featured Projects
Selected ongoing and recent research
Multi-Modal Damage Quantification
Physics-informed feature extraction from terahertz, ultrasonic, and electromagnetic sensing data for automated impact damage assessment in composite structures.
■Random Forest r = 0.96–0.99
Weakly Supervised Defect Localization
Training object detectors without manual annotation using Grad-CAM pseudo-labels, enabling scalable inspection across X-ray, THz, and ultrasonic imaging modalities.
■99% classification · 0.67 mAP
AI-Driven Inspection Pipelines
End-to-end systems combining classification, detection, and quantification, with X-ray ground truth validation for cross-modal benchmarking.
■mAP@0.5 = 0.99 · mAP@0.5:0.95 = 0.79
Useful Links
Resources, tools, and references for our research community