Summary
Behzad Tabari is an AI trainer and computational mechanics specialist based in Munich, combining three years of hands-on experience in physics-informed surrogate simulation, numerical methods, computational geometry, and machine learning. He bridges domain expertise in parametric CAD and manufacturing workflows with practical ML: his master's work produced a two-stage GNN pipeline for machining cost estimation and a 13k-sample synthetic CAD/CAM dataset to overcome data scarcity. At micro1 he evaluates and refines AI outputs for CAD, while prior roles at Tebis and TUM translated research-grade tools into production-ready Python bindings and graph-based representations for assembly planning and surrogate models. Comfortable moving between C++ bindings, Python toolchains, and graph neural architectures, he favors pragmatic, stochastic approaches to problem solving and enjoys probing beyond textbook answers.
3 years of coding experience
2 years of employment as a software developer
Master's degree, Computational mechanics, Master's degree, Computational mechanics at Technical University of Munich
Bachelor of Science - BS, Mechanical Engineering, Bachelor of Science - BS, Mechanical Engineering at University of Tehran
Persian, English, German