
Professor Burnap’s research focuses on how companies can improve product design and market innovation using machine learning and large-scale consumer data.
His research interests include: (1) quantifying consumer preferences and product needs, (2) integrating marketing decisions with engineering feasibility, and (3) the implications of targeted marketing and consumer data privacy.
Prior to joining Yale, he worked as a research scientist at General Motors and as a postdoctoral fellow at MIT Sloan School of Management. He holds a Ph.D. Design Science and M.S. Mechanical Engineering from University of Michigan, and B.S. Engineering Physics from UIUC.
Expertise
Education
- PhD, University of Michigan, 2016
- MS, University of Michigan, 2013
- BS, University of Illinois, Urbana-Champaign, 2011
Selected Articles
Working Papers
Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach
Discovering "Product Gaps" using Big Data and Machine Learning