Project Name: Data at The Museum
The NY Metropolitan Museum classifies roughly 2400 of their 480,000 items as ‘Highlights’. Our team used supervised and unsupervised learning techniques to determine what factors contribute to an item being a highlight, and created a model to predict whether an item in the collection should be identified as such. This model could be used by the Met to determine what pieces to feature in upcoming exhibitions, or by other museums to identify what under-appreciated items to request on loan for their galleries.
I am constantly learning, and apply new information and viewpoints to my global understanding of the world. With experience in many disparate fields—Risk Management, Finance, Hospitality, Construction, Media, Education, Legal Services—I bring surprising insights to unique business situations and questions.
building models, creating classes and automation, and stunning visualizations.
San Antonio, TX
Involvement, insight, and innovation
Machine Learning automation, team environment, new problems