Guangshuai Han

Guangshuai(Jerry) Han

Guangshuai Han leverages artificial intelligence to drive transformative research across materials and device design. More broadly, he is committed to developing AI-for-science approaches that accelerate innovation across disciplines.

During his PhD, one of his major achievements was pioneering an AI-assisted piezoelectric sensing system that transformed highway infrastructure monitoring. This technology was adopted as a new AASHTO standard, recognized by Time Magazine as one of the Best Inventions of 2023, and successfully transferred into practice as the core technology of a startup company.

He is currently a Postdoctoral Associate at Johns Hopkins University, where his research focuses on applying AI to tackle the extreme complexity of disordered materials systems. Through these approaches, he aims to open new directions for high-entropy disordered materials design and discovery.

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01 Artificial Intelligence-Assisted Material Design

My research spans multiple directions in AI-driven materials discovery, from chemical composition design and machine-learning force fields to interpretable graph neural networks. The central focus is on high-entropy materials, where I develop physically inspired algorithms to navigate billions of possibilities and identify optimized designs.

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02 Next-Generation Signal Processing with AI

This direction focuses on using AI to efficiently extract information from sensors and to design foundation models for advanced signal processing. The piezoelectric sensing system developed through this work has already been implemented across more than 15 U.S. states and incorporated into DOT standards. In parallel, AI-driven signal analysis is being extended to pharmaceutical materials sensing, enabling accurate and robust monitoring solutions.

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03 From Materials to Wearable Electronics

This direction bridges fundamental materials design with applications in wearable electronics. Starting from atomic-scale modeling to create high-performance materials, the work extends to developing novel sensing mechanisms and tailored signal processing algorithms. Together, these efforts enable next-generation wearable devices that combine material innovation with intelligent data interpretation.

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04 Other Collaborations

Beyond his core research areas, he is engaged in diverse collaborations that span 3D-printed sensor design, advanced concrete materials for 3D printing, computer vision for material inspection, AI-guided polymer design, and solid-state materials research. These efforts reflect a broad commitment to integrating artificial intelligence with emerging technologies across multiple domains.

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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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