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New ISR-affiliated Assistant Professor Mark Fuge (Mechanical Engineering) uses machine learning, artificial intelligence, and crowdsourcing to understand how large groups of people design things and how complex engineered systems work, with the end goal of using the data they produce to make them better.

In his IDEAL Lab, Fuge's research group studies fundamental scientific questions like:

• What are efficient and useful ways to computationally and mathematically represent designs?
• How do we combine physics-driven and data-driven models to design better products?
• What makes design collaboration between large groups of people work well or poorly?
• How can we use tools from applied mathematics (such as graph theory, category theory, and statistics) and computer science (such as complexity theory, submodular optimization, and artificial intelligence) to better understand how humans design?

Some past practical applications of Fuge’s research include:

• A fully automated system for inferring what makes designs creative given human feedback;
• The world’s first polynomial time algorithm for diverse bi-partite b-matching;
• Algorithms for exploring and optimizing high-dimensional design spaces (e.g., aircraft);
• Software for helping novices 3D print working mechanical devices; and
• Network analyses of online collaborative design networks such as OpenIDEO.

Fuge recently mentored “The Oyster Boys,” an undergraduate team that won the Mechanical Engineering Department’s Design Day competition in May. These students developed a robotic underwater microphone that can detect oyster poaching dredging in the Chesapeake Bay.



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