In the Informatics for Design, Engineering And Learning (IDEAL) Lab, we study how to make machines that learn how to design and build other machines.
To do this, we use Machine Learning, Artificial Intelligence, and Crowdsourcing to understand how large groups of people design things and how complex engineered systems work, so that we can use the data they produce to make them better.
Some of the fundamental scientific questions we study include: 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 our 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) that accelerate optimization by an order of magnitude or more; software for helping novices 3D print working mechanical devices; and network analyses of online collaborative design networks such as OpenIDEO.
Graduate and Post-Doctoral alumni from our lab have gone on to pursue productive research careers in Academia (MIT, American University), National Labs (NIH, NIST), and Industry (Siemens, Capital One, Amazon).
You can read more about our work by looking at our papers or by reproducing any of our open-source code.