2. Paper published: Continuum damage modeling with Neural ODEs in Extreme Mechanics Letters
3. Paper published: Generative hyperelasticity with diffusion in Engineering with Computers
4. Paper published: Data-driven anisotropic viscoelasticity with Neural ODEs in Computer Methods in Applied Mechanics and Engineering
5. Paper published: Benchmarking data-driven models of hyperelasticity in Computational Mechanics
6. Recipient of Purdue's TRACER grant for collaboration with Dr. Ellen Kuhl at Stanford University
7. Presented talks and posters at various academic conferences
Research Experience
1. Postdoctoral Scholar at Stanford University, Department of Bioengineering (August 2024 - present)
2. PhD student at Purdue University (until July 2024)
Education
PhD from Purdue University, specializing in machine learning models of material behaviors such as hyperelasticity, viscoelasticity, and damage, ensuring the underlying physics is always satisfied.
Background
Postdoctoral Scholar in the Department of Bioengineering at Stanford University, focusing on understanding the biomechanics of the human brain using machine learning. Research interests include integrating physics into machine learning models as hard constraints.