Guangshuai Han

Guangshuai(Jerry) Han

Guangshuai Han is a fourth-year PhD candidate in Civil Engineering at Purdue University. His research focuses on utilizing AI algorithms to design materials with specialized properties, such as thermoelectric and dielectric materials. Additionally, he works on sensor design and AI-assisted signal processing for applications including structural health monitoring, wearable electronics, and pharmaceutical quality control. Guangshuai has collaborated with numerous funding sources, including the National Science Foundation, Indiana Department of Transportation, Pfizer, Eli Lilly, and Genentech.

Most of my work is currently under review, so the majority of my GitHub Pages are temporarily unavailable. They will be accessible as soon as conditions permit.

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01 Artificial Intelligence-Assisted Material Design

This project explores the integration of advanced AI techniques with computational materials science to develop comprehensive material gene encoding, feature extraction, physically inspired neural networks, and explainable AI models. The goal is to build a robust database and design suitable AI model based on specific requirements.

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02 Molecular Scale Piezo-Materials Design

This research focuses on leveraging intermolecular forces to design and control the dipole moment alignment in piezoelectric materials, with the goal of promoting and enhancing their piezoelectric performance.

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03 Wearable Flexible Device Fabrication

This project focuses on the topological design and fabrication of multifunctional wearable devices for health monitoring, utilizing COMSOL for structural and geometric optimization and cutting-edge additive manufacturing techniques.

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04 AI and Piezoelectric Sensor for Concrete Strength Monitoring
I’m working on the development of piezoelectric sensors for monitoring concrete strength, creating a comprehensive sensing database through extensive experiments and field tests. This work offers new insights into concrete strength sensing and has been applied in practical engineering.

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05 AI-Driven Moisture Content Monitoring in Pharmaceuticals

A deep learning-guided approach to electrochemical impedance spectroscopy (EIS) for accurate, calibration-free monitoring of moisture content in pharmaceutical materials. This method enhances real-time quality control by eliminating the need for extensive calibration.

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06 Rheology Monitoring for Concrete 3D Printing

Electromechanical impedance (EMI) and piezoelectric sensors to monitor the rheological properties of concrete, enhancing precision and control in 3D printing and additive manufacturing processes.

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Teaching Experience

I co-developed a Mini Graduate Program (Materials Engineering for Intelligent Infrastructure) with my advisor, Na Lu, comprising six courses that focus on the application of nanotechnology in civil engineering and non-destructive testing methods. These courses have been enrolled by over 10,000 students from around the world.

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