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Microstructure and Crystallographic Orientation Effect on the Mechanical Behavior of Materials


A key part of materials science is to build the structure-property linkage. For example, mechanical behavior of structural materials depends strongly on the microstructure and orientation of crystalline domains. A deeper understanding of this particular linkage requires an thorough investigation into the generation and motion of dislocations, dislocation-dislocation interaction and dislocation-grain boundary interaction. Characterization of the distribution and types of dislocations over a large area has been made possible with electron backscatter diffraction

Sustainable Design of Metallic Alloys Through Microstructure Engineering


As the demand for structural alloys is expected to grow around 200% in many industrial sectors until 2050, reduction in greenhouse gas emission associated with metal extraction and manufacturing becomes critically important. For example, method such as scrap metal recycling in the secondary industry would make direct impact to the sustainability of structural metals but the challenge nowadays is to sort metals with increasingly complex alloying elements. Fortunately, properties of metals, particular mechanical properties, can be adjusted through microstructure tuning without changing the alloying composition

High-Throughput Characterization with Machine Learning


Following some of the most significant discoveries in physics made in the 20th century, modern breakthroughs in physical science have been closely correlated with the exponential growth in the computational power and advanced analytical algorithms. To tackle some of the urgent problems that our humanity faces such as global warming, scientists more actively seek for material solutions to lead a more sustainable future, for example, structural alloys that last longer in service and battery materials with higher energy storage capacity. Part of the so-called ‘closed-loop’ challenge in designing new materials is to integrate high-throughput characterization into the materials’ design process. With ever more sophisticated machine learning algorithms…

Computer Vision Approach to Experimental Mechanics


In experimental mechanics, understanding how materials deform and where they are most susceptible to failure is crucial for promoting safety and potentially guiding material design to alleviate catastrophic loss. Characterization methods at different length scales that can map strain in situ and probe residual deformation post-mortem are therefore very important to provide researchers with location-specific measurable quantities to study failure mechanisms.

Materials Characterization Using Physics Based Forward Model


Most characterization methods are ill-posed inverse problems which could benefit from some form of regularization. Physics based forward model serves as a strong form of regularization which allows us to accurately model the source (electron, x-ray, photons, neutrons, etc), source-sample interaction, detector behavior, and even take into account potential sources of measurement noise. It is then possible to iteratively infer material properties through direct comparison of the modeling results with experimental data by solving the inverse problem



TA: MATH 20C Vector Calculus

Undergraduate Course, UC San Diego, 2015

Department of Applied Physics and Mathematics (MATH 20C: Vector Calculus)
Teaching Assistant, Sept. 27th 2015-Mar. 22nd 2016

TA: CHEM 143B Organic Chemistry Lab

Undergraduate Course, UC San Diego, 2016

Department of Chemistry & Biochemistry (CHEM 143B: Organic Chemistry Lab)
Teaching Assistant, June. 28th 2016-Aug. 2nd 2016

TA: MAE 131A Solid Mechanics

Undergraduate Course, UC San Diego, 2016

Department of Mechanical and Aerospace Engineering (MAE 131A: Solid Mechanics)
Teaching Assistant, Aug. 28th 2016-Sept 6th 2016

TA: MATH 20D Differential Equations

Undergraduate Course, UC San Diego, 2018

Department of Applied Physics and Mathematics (MATH 20D: Differential Equations) Teaching Assistant, Sept. 24th 2018-Dec. 18th 2018