Professor Burnap’s work focuses on how companies can improve product management and product design using tools from machine learning and big data.
His research interests include: (1) quantifying consumer needs and converting them to product features, (2) understanding market structure and new product opportunities using big data, and (3) improving product decisions by accounting for engineering and design feasibility.
Prior to joining Yale, he worked in product research at General Motors, and in consulting for a variety of startups and enterprise firms. He completed his postdoc at MIT Sloan, Ph.D. Design Science and M.S. Mechanical Engineering at Michigan, and B.S. Engineering Physics at UIUC.
- PhD, University of Michigan, 2016
- MS, University of Michigan, 2013
- BS, University of Illinois, Urbana-Champaign, 2011
- Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach
A. Burnap, J. R. Hauser, and A. Timoshenko
- Balancing Design Freedom and Brand Recognition in the Evolution of Automotive Brand Styling
A. Burnap, J. Hartley, Y. Pan, R. Gonzalez, and P. Y. Papalambros
Design Science Journal
Volume 2, Issue 9
- Improving Design Preference Prediction Accuracy Using Feature Learning
A. Burnap, Y. Pan, Y. Liu, Y. Ren, H. Lee, R. Gonzalez, and P. Y. Papalambros
Journal of Mechanical Design
Volume 138, Issue 7