People

Lab members

The CODE2 Lab is led by Zihan Wang and will grow through students and collaborators at Oakland University.

Portrait of Zihan Wang

Office

EC 412
115 Library Drive
Rochester, MI 48309-4479

Selected Awards

  • Rising Star in Mechanical Engineering, MIT Workshop, 2025
  • Design Automation Dissertation Award, ASME Design Engineering Division, 2025
  • 1st Place, ASME Computer & Information in Engineering Division Hackathon Event, 2023
  • BPart Fellowship, ASME Design Engineering Division, 2022
  • General Electric (GE) Fellowship, UConn, 2020

Principal Investigator

Zihan Wang

Assistant Professor
Mechanical Engineering
Oakland University

Dr. Zihan Wang is an Assistant Professor in Mechanical Engineering at Oakland University. Her research develops AI-enabled, experimentally grounded modeling and design methods for advanced manufacturing, with the goal of accelerating the autonomous discovery, analysis, and scalable production of mechanical and material systems. Her work brings together machine learning, design optimization, mechanics, uncertainty quantification, metamaterials, and materials science.

Her research program centers on three connected directions: AI-based autonomous design and discovery for smart manufacturing; generative co-design of manufacturable mechanical and material systems; and uncertainty-aware design for robust and reliable performance. Before joining Oakland University, she was a Postdoctoral Scholar at Northwestern University, where she worked on AI for materials discovery and design with Professor Wei Chen.

Dr. Wang received her Ph.D. in Mechanical Engineering from the University of Connecticut in 2024. Her dissertation focused on the design, performance evaluation, and uncertainty analysis of metamaterial units. She earned a B.S. in Materials Science and Engineering and a B.A. in English Interpretation from Huazhong University of Science and Technology in 2019.

Future Team

Students and collaborators

CODE2 Lab welcomes motivated students interested in computational design, optimization, modeling, simulation, data-driven methods, mechanics, manufacturing, materials science, and design automation.

See opportunities