Page 2 — 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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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.

Research Sponsors:

National Science Foundation (NSF)
Federal Highway Administration (FHWA)
Indiana Department of Transportation (INDOT)
Pfizer
Eli Lilly
Genentech

Github Page

Impedance Data Processing: Imped_spec
Piezo sensor field application: EMI-Net

Publication:

G. Han, Y. Su, R. He, C. Huang, Z. Kong, G. Lin, Y. Feng, N. Lu, “Are We Measuring Concrete Strength Correctly? AI Solutions for Real-Time Structural Monitoring” Nature Communications. (Major Revision)
G. Han, Y.F. Su, C. Huang, N. Lu, Y. Feng, “Field-Validated deep learning model for Piezoelectric-Based In-Situ concrete strength sensing”, Mechanical Systems and Signal Processing, 232, 112768, 2025.

G. Han, B. Maranzano, C. Welch, N. Lu, Y. Feng. “Deep-Learning-Guided Electrochemical Impedance Spectroscopy for Calibration-Free Pharmaceutical Moisture Content Monitoring” ACS Sensors, 9, pp. 4186-4195, 2024.
Y. Su, G. Han, T. Nantung and N. Lu. “Field implementation the piezoelectric sensing technique for in-situ concrete evaluation” ACI Materials Journal, 118, 1, 2021.

G. Han, YF. Su, T. Nantung, and N. Lu. “Mechanism for Using Piezoelectric Sensor to Monitor Strength Gain Process of Cementitious Materials with the Temperature Effect” Journal of Intelligent Material Systems and Structures, 32, pp. 1128-1139, 2021.

G. Han, YF. Su, S. Ma, T. Nantung, and N. Lu. “In situ rheological properties monitoring of cementitious materials through the piezoelectric-based electromechanical impedance (EMI) approach” Engineered Science, 16, pp. 259- 268, 2021.
Y. Su, G. Han, Z. Kong, T, Nantung, and N. Lu, “Embeddable piezoelectric sensors for strength gain monitoring: the influence of coating materials” Engineered Science, 11, pp. 66-75, 2020.
Y. Su, G. Han, T. Nantung, and N. Lu. “Novel methodology on direct extraction of the strength information from cementitious materials using piezo-sensor based electromechanical impedance (EMI) method” Construction and Building Materials, 259, 119848, 2020.
Y. Su, G. Han, AG. Amran, T. Nantung, and N. Lu, “Instantaneous Sensing of the Early Age Properties of Cementitious Materials using PZT-based Electromechanical Impedance (EMI) Technique”, Construction and Building Materials, 225, pp. 340-347, 2019。

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