AI-enabled computational co-design, metamaterial, and manufacturing research visual

Computational and Data-Enabled Design · Oakland University

CODE2 Lab

We develop computational methods that enable materials, structures, manufacturing processes, products, and systems to be designed together for high-performing, manufacturable, and reliable engineering solutions.

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Lab Vision

Computational and Data-Enabled DEsign (CODE2 Lab) reflects the use of computational methods in engineering design and the lab's emphasis on integrated, concurrent design across multiple domains. Rather than treating materials, structures, manufacturing processes, and systems as separate problems, the lab develops modeling, simulation, optimization, data-driven methods, and design automation approaches that enable them to be designed together.

By accounting for these interactions early in the design process, CODE2 Lab aims to create engineering solutions that are high-performing, manufacturable, reliable, and application-ready.

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Lab Identity

CODE2 Lab

Computational and Data-Enabled Design

Portrait of Zihan Wang

Principal Investigator

Zihan Wang

Assistant Professor
Mechanical Engineering
Oakland University

Latest

News

The CODE2 Lab website is live. We are recruiting fully funded Ph.D. students for 2026/2027 admission. View openings

The ARCO-BO paper on collaborative Bayesian optimization for heterogeneous multi-agent design appears in the Journal of Mechanical Design. View publications

Zihan Wang received the ASME Design Automation Dissertation Award and was selected for the MIT Rising Stars in Mechanical Engineering workshop. View PI profile

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