As a Research Engineer funded by the Amazon-Max Planck Science Hub, he specializes in leveraging deep learning alongside finite element analysis to model the interactions between clothing and the human body. His work focuses on using artificial neural networks to simulate how different materials, sizes, and body shapes affect clothing pressure and deformation.

Driven by a profound fascination with the replication of human intelligence for more efficient problem-solving, his research primarily revolves around applying machine learning to science and engineering. His focus areas include developing surrogate models and reducing computational costs in the simulation of complex systems.