Current Research
Bio-Inspired Dental Materials
Designing functionally graded interfaces between ceramic and polymer layers to reduce interfacial stresses and enhance the durability of dental restorations, inspired by the natural dento-enamel junction.
Stress Fracture Prediction Using Tibia Geometry
Developing a machine learning model trained on finite element simulations of tibial geometries to predict stress concentrations that lead to fractures, with the goal of enabling personalized injury risk assessments for athletes.
Ant Pad Composite Structure
Inspired by the fibrous, multi-functional architecture of insect cuticle, this project investigates the microstructure of ant footpads to design lightweight, high-adhesion composite materials. The goal is to mimic their directional stiffness and layered morphology to enhance grip and load distribution in synthetic interfaces.
Past Research
Void Detection in Composite Materials
Developed machine learning models using elastic wave data to detect and classify voids and delaminations in composite structures. This approach improves nondestructive testing for structural health monitoring.
Seismic Wave Reconstruction for Structural Health Monitoring
Used deep learning models to estimate incoming seismic wave motions from surface data. This helps improve structural health monitoring by quickly identifying how the ground shakes beneath buildings and infrastructure.