TITLE

Insight and Applications of Big Data: A Case Study with Family Logistics

 

ABSTRACT

This talk describes how Big Data allows for a cycle of innovation between the Computer and Social Sciences. I use commercial mobile phone data to explain how family logistics can break down. I show how we can use the same data to create unsupervised models of family coordination, sensing when parents pick up and drop off their children at over 90% accuracy, and predicting which parent will make the next pick-up for an event with over 85% accuracy. I show how Big Data combine with predictive models in real time to create a new way to visualize coordination.

 

BIO

Dr. Scott Davidoff is a Postdoctoral Fellow and Lecturer in Human-Computer Interaction at Carnegie Mellon. In his fourteen years across research and advanced development, Dr. Davidoff has developed domestic robots with Microsoft Research, and as principal of Scott Davidoff Design, developed mobile and context-aware systems for companies like AOL and SBC Ameritech. His work to create new design methods now forms part of the HCI and Design curricula at CMU.

 

Dr. Davidoff has a PhD in Human-Computer Interaction, an MS in Computer Science (Research) and an M.HCI in Human-Computer Interaction (Practice), all from Carnegie Mellon.

 

For more information:

http://scottdavidoff.com/research

http://scottdavidoff.com/consulting