Desirability Lab: Design and Innovation Research

This longitudinal study analyses differences in success and failure in high performing innovation teams at IDEO and Fortune 500 companies. It contains in-depth qualitative and quantitative analysis of over 300 projects. This data provides a rare view of success and failure in the real-world complexity of innovation projects, which often involve multi-disciplinary, multi-cultural and multi-organizational collaboration. The ongoing analysis is identifying patterns associated with more (and less) successful outcomes. The work has been funded by the Harvard Initiative for Learning and Teaching and the MIT International Design Center and the University of Cambridge. The original study inspired the development of several of Altringer's project-based design and innovation courses at Harvard. Talks and workshops on this research can be arranged for groups outside of Harvard, which helps fund the continuation of the research. See the 'join' tab or email ba[@] if you or someone you know would like to join our efforts on this project.

Design Community Professional Opportunity Board

We run a closed design job board for current and former students, professionals who want to offer professional opportunities to them, and friends of the lab. Request to join.


Computational Flavor Discovery

Altringer is developing ways to computationally understand how food and drink tastes, how tastes combine, patterns in how people experience the same tastes differently, and how taste links to emotion, region, and culture. She is integrating scientific data on flavor with natural language processing analysis that can identify patterns or ‘genres’ of flavor experiences based on how ingredients and dishes are described, experienced, and replicated by experts, scientists, and everyday reviewers. Getting to the point of automated understanding of what people are intuitively searching for in a food or drink experience, regardless of the terminology they use to search for it, will eventually enable us to intelligently understand flavor goals in context and make it easier for people to discover experiences they are likely to enjoy.