Education:
Ph.D. California Institute of Technology

Research Interests:

• Experimental mechanics: machine learning and data-driven material characterization
• Full-field deformation measurements: digital image correlation, digital volume correlation and particle tracking
• Laser induced inertial cavitation in hydrogels and biological materials
• Dynamic instability in viscoelastic materials

Dr. Yang’s research interests include developing analytical tools and experimental techniques to study viscoelastic materials behavior, dynamic instabilities and material failure under extreme loading conditions. He is currently experimentally characterizing and modeling dynamic, nonlinear behavior of viscoelastic materials, including hydrogels, biological tissues and foam materials under various strain-rate (10^-4 ~ 10^7 s^-1) dynamic loading. Yang’s work also focuses on leveraging state-of-the-art machine learning and data-driven methods to keep improving 2D and 3D full-field deformation measurement (e.g., digital image correlation, digital volume correlation, particle tracking) and other material characterization experimental techniques.

Yang joined the department as an assistant professor in Fall 2022. He received his doctorate from the California Institute of Technology, where he developed fast, accurate, adaptive-mesh augmented Lagrangian digital image/volume correlation (ALDIC/ALDVC) methods to measure 2D/3D full-field deformations quantitatively. After his graduate studies, he was a postdoctoral research associate at the University of Wisconsin-Madison, where his research focused on developing a micro-cavitation-based rheometry method to characterize viscoelastic properties of soft gel-like materials at ultra-high strain rates (> 10^3 s^-1) by utilizing laser-induced cavitation.

Related Websites

• Dr. Jin Yang’s Website
• Center for Mechanics of Solids, Structures and Materials

Recent Awards and Honors

• Academic Development Funds for “Enhancing ASE 324L Aerospace Materials Lab with Digital Image Correlation, Machine Learning Data Analytics, & Large Language-Based Strategy,” 2024-25

• Haythornthwaite Initiation Grant, Applied Mechanics Division (AMD) of the American Society of Mechanical Engineers (ASME), 2022