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); software for helping novices 3D print working mechanical devices; and network analyses of online collaborative design networks such as OpenIDEO.

You can read more about our work by looking at our papers or by reproducing any of our open-source code.

Our article entitled “Unpacking Subjective Creativity Ratings: Using Embeddings to Explain and Measure Idea Novelty” was accepted to this year’s IDETC. Check out the publications page for more information.

Posted 25 Apr 2018 by

Our article entitled “Synthesizing Designs with Inter-part Dependencies Using Hierarchical Generative Adversarial Networks” was accepted to this year’s IDETC. We will be presenting this paper at the 44th Design Automation Conference. Check out the publications page for more information.

Posted 24 Apr 2018 by

Citrine Informatics featured our new Pre-Print “Applying machine learning techniques to predict the properties of energetic materials” in the “New Literature Worth Reading” section of its Data-Driven Material Science and Chemistry Newsletter!

Posted 22 Jan 2018 by Mark

Our article entitled “Active expansion sampling for learning feasible domains in an unbounded input space” was published in Structural and Multidisciplinary Optimization (SAMO). Check out the publications page for more information.

Posted 19 Jan 2018 by
Posted 31 Dec 2018 by Mark
Posted 01 Sep 2015 by Mark
Posted 30 Aug 2014 by Mark