Summary
Yu Zhu is a postdoctoral researcher with nine years of experience applying computational physics and computer science to soft-matter and biomolecular systems. At Purdue he builds bottom-up coarse-graining workflows and neural-network backmapping tools that bridge all-atom and reduced-resolution models for complex lipid nanoparticles and proteins. His prior work at the University of Memphis produced high-performance C++ and OpenMP simulation engines, sophisticated analysis toolchains (Voronoi, RDFs, order parameters) and large-scale MD studies revealing ordered and helical self-assembly of Janus nanoparticles on membranes. He combines rigorous physics training (PhD in Applied Physics) with an MS in Computer Science, enabling him to translate theoretical insight into practical simulation software and data-driven models. Comfortable across Python, C++, and ML frameworks, he focuses on interpretable coarse-graining and efficient multiscale workflows rather than black-box optimization. Outside research he maintains a technical portfolio on his website and a poetic GitHub bio, signaling a blend of analytical depth and creative perspective.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS, Physics, Bachelor of Science - BS, Physics at Anhui University
Master of Science - MS, Computer Science, 3.7, Master of Science - MS, Computer Science, 3.7 at Georgia Institute of Technology
Doctor's Degree, Applied Physics, 3.7, Doctor's Degree, Applied Physics, 3.7 at The University of Memphis