About Me

I am a Ph.D. candidate in Engineering Mechanics at The University of Texas at Austin, advised by Dr. Jin Yang. I combine solid mechanics, full-field experiments, and finite element simulation to characterize material behavior and validate predictive models, using machine learning where it improves resolution, inference, or speed.

I work across the full mechanics loop, from loading a specimen to measuring its response, identifying its properties, and testing the model against what the experiment actually shows.

I am also a research software developer. I build and maintain open-source Python packages for full-field deformation measurement, from classical augmented-Lagrangian DIC and DVC to deep-learning solvers, released on PyPI with desktop GUIs, scriptable APIs, and documentation.

I am graduating in 2027 and exploring research engineering and scientific software roles in solid mechanics, material characterization, simulation, and model validation.

What I Do

• Measure: DIC, stereo-DIC, and DVC, high-speed imaging, instrumented testing
• Model: solid and continuum mechanics, finite element analysis, thermo-mechanical systems
• Machine learning: deep-learning DIC and DVC solvers, ML-aided spatial adaptation for complex geometries, FE-based surrogate models, synthetic training data and synthetic-to-experimental transfer
• Identify and validate: inverse identification, simulation–test correlation
• Research software: Python and PyTorch, Numba-accelerated solvers, PySide6 desktop GUIs, automated tests and CI, PyPI and Zenodo releases

Open-Source Software

• pyALDIC: augmented-Lagrangian DIC in Python with a desktop GUI and adaptive quadtree meshes (pip install al-dic). Recommended in two UT Austin undergraduate courses.
• pyALDVC: the volumetric (DVC) counterpart for micro-CT, confocal, and other 3D image data (pip install al-dvc).
• pyALDIC-3D: stereo and multi-camera DIC for 3D shape, displacement, and surface strain (pip install al-dic-3d).
• RAFTcorr: a deep-learning DIC framework with a training-data pipeline, pre-trained weights, and a GUI.
• RAFT-DVC: resolution-aware, learning-based DVC.

In the wider DIC community, I am a co-author of the iDICs Good Practices Guide for DIC (2nd edition) and lead author of the DVC Challenge 2.0 benchmark dataset. More tools are on GitHub and the Projects page.