Zihan Wang is a Postdoctoral Scholar at Northwestern University specializing in data-driven design and materials informatics, with a PhD in Mechanical Engineering from the University of Connecticut. He combines deep learning (PyTorch, TensorFlow) with advanced simulation tools (SolidWorks, UG NX, Abaqus, LS-DYNA, Pro-CAST) to optimize designs under uncertainty and for manufacturability. His work blends uncertainty quantification and propagation with practical engineering workflows, demonstrated during a GE Research fellowship tackling probabilistic methods for industry problems. With high academic distinction and bilingual training in engineering and translation, he bridges technical depth and clear cross-disciplinary communication. Based in the Greater Chicago Area, he brings three years of focused research experience and prior collaborations at Deepseek-AI and UIUC NLP that reflect a broad interest across AI and engineering. Notably, he pairs rigorous simulation expertise with modern ML to move materials design from theory toward manufacturable solutions.
4 years of coding experience
Doctor of Philosophy - PhD, Mechanical Engineering, 4.066/4.0, Doctor of Philosophy - PhD, Mechanical Engineering, 4.066/4.0 at University of Connecticut
Bachelor of Engineering - BE, Material Processing and Control Engineering, 3.89/4.0, Bachelor of Engineering - BE, Material Processing and Control Engineering, 3.89/4.0 at Huazhong University of Science and Technology
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