About

Alex Burnap is an Assistant Professor of Marketing at Yale School of Management. His work focuses on how companies can improve product management and product design through the application of machine learning and big data tools. He specializes in developing generative AI for targeting personalized products, with research interests including quantifying consumer needs and translating them into product features, understanding market structure and identifying new product opportunities using big data, and enhancing product decisions by considering engineering and design feasibility. Prior to his tenure at Yale, he gained experience working in product research at General Motors and in consulting for startups and enterprise firms. He completed his postdoctoral work at MIT Sloan, earning a Ph.D. in Design Science, along with an M.S. in Mechanical Engineering from the University of Michigan and a B.S. in Engineering Physics from the University of Illinois, Urbana-Champaign.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Data Mining
• Business
• Data science
• Engineering
• World Wide Web
• Chemistry
• Marketing

Selected publications

• Generative Interpretable Visual Design: Using Disentanglement for Visual Conjoint Analysis

  Journal of Marketing Research · 2024-08-09 · 14 citations

  article

  This article develops a method to automatically discover and quantify human-interpretable visual characteristics directly from product image data. The method is generative and can create new visual designs spanning the space of visual characteristics. It builds on disentanglement methods in deep learning using variational autoencoders, which aim to discover underlying statistically independent and interpretable visual characteristics of an object. The impossibility theorem in the deep learning literature indicates that supervision with ground truth characteristics would be required to obtain unique disentangled representations. However, these are typically unknown in real-world applications, and are in fact exactly the characteristics that need to be discovered. Extant machine learning methods are unsuitable since they require ground truth labels for each visual characteristic. In contrast, this method postulates the use of readily available product characteristics (such as brand and price) as proxy supervisory signals to enable disentanglement. This method discovers and quantifies human-interpretable and statistically independent characteristics without any specific domain knowledge on the product category. It is applied to a dataset of watches to automatically discover interpretable visual product characteristics, obtain consumer preferences over visual designs, and generate new ideal point designs targeted to specific consumer segments.

  PublisherDOI

• CROWDSOURCING: A PRIMER AND ITS IMPLICATIONS FOR SYSTEMS ENGINEERING

  SAE technical papers on CD-ROM/SAE technical paper series · 2024 · 17 citations

  • Computer Science
  • Computer Science
  • Data science

  <title>ABSTRACT</title> <p>Crowdsourcing is an overarching term that denotes a number of ways to use the web as means to enlist a large number of individuals to perform a particular task. The tasks can range from simply providing an opinion, to contributing material, to solving a problem. Because the term crowdsourcing is used to denote a variety of activities in many different contexts, strong opinions have formed in many minds. This paper is an attempt to inform the reader of the complexity that underlies the simple term “crowdsourcing.” We then describe the connection between the DARPA Adaptive Vehicle Make program with the potential limitations of crowdsourcing complex tasks using examples from industry. Using these examples, we present a research motivation detailing areas to be improved within current crowdsourcing frameworks. Finally, an agent-based simulation using machine learning techniques is defined, preliminary results are presented, and future research directions are described.</p>

  PublisherDOI

• Product Aesthetic Design: A Machine Learning Augmentation

  Marketing Science · 2023 · 82 citations

  1st authorCorresponding
  • Computer Science
  • Machine Learning
  • Artificial Intelligence

  Aesthetics are critically important to market acceptance. In the automotive industry, an improved aesthetic design can boost sales by 30% or more. Firms invest heavily in designing and testing aesthetics. A single automotive “theme clinic” can cost more than $100,000, and hundreds are conducted annually. We propose a model to augment the commonly used aesthetic design process by predicting aesthetic scores and automatically generating innovative and appealing product designs. The model combines a probabilistic variational autoencoder (VAE) with adversarial components from generative adversarial networks (GAN) and a supervised learning component. We train and evaluate the model with data from an automotive partner—images of 203 SUVs evaluated by targeted consumers and 180,000 high-quality unrated images. Our model predicts well the appeal of new aesthetic designs—43.5% improvement relative to a uniform baseline and substantial improvement over conventional machine learning models and pretrained deep neural networks. New automotive designs are generated in a controllable manner for use by design teams. We empirically verify that automatically generated designs are (1) appealing to consumers and (2) resemble designs that were introduced to the market five years after our data were collected. We provide an additional proof-of-concept application using open-source images of dining room chairs. History: Puneet Manchanda served as the senior editor. Funding: A. Burnap received support from General Motors to partially fund a postdoctoral research position for the research conducted in this work. He certifies that none of the research or its results were censored or obfuscated in its publication. J. Hauser and A. Timoshenko certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Supplemental Material: The data files are available at https://doi.org/10.1287/mksc.2022.1429 .

  PublisherDOI

• Automatically Discovering Visual Product Characteristics

  SSRN Electronic Journal · 2022-01-01 · 3 citations

  articleOpen access
  PublisherDOI

• Product Aesthetic Design: A Machine Learning Augmentation

  SSRN Electronic Journal · 2022-01-01 · 3 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Soul and machine (learning)

  Marketing Letters · 2020 · 34 citations

  • Computer Science
  • Computer Science
  • Machine Learning
  PublisherOA PDFDOI

• Product Aesthetic Design: A Machine Learning Augmentation

  RePEc: Research Papers in Economics · 2019-07-17 · 22 citations

  preprintOpen access1st authorCorresponding

  Aesthetics are critically important to market acceptance. In the automotive industry, an improved aesthetic design can boost sales by 30% or more. Firms invest heavily in designing and testing aesthetics. A single automotive "theme clinic" can cost over $100,000, and hundreds are conducted annually. We propose a model to augment the commonly-used aesthetic design process by predicting aesthetic scores and automatically generating innovative and appealing product designs. The model combines a probabilistic variational autoencoder (VAE) with adversarial components from generative adversarial networks (GAN) and a supervised learning component. We train and evaluate the model with data from an automotive partner-images of 203 SUVs evaluated by targeted consumers and 180,000 high-quality unrated images. Our model predicts well the appeal of new aesthetic designs-43.5% improvement relative to a uniform baseline and substantial improvement over conventional machine learning models and pretrained deep neural networks. New automotive designs are generated in a controllable manner for use by design teams. We empirically verify that automatically generated designs are (1) appealing to consumers and (2) resemble designs which were introduced to the market five years after our data were collected. We provide an additional proof-of-concept application using opensource images of dining room chairs.

  OA PDFDOI

• Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach

  SSRN Electronic Journal · 2019-01-01 · 12 citations

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Soul and Machine (Learning)

  SSRN Electronic Journal · 2019-01-01 · 1 citations

  articleOpen access
  PublisherDOI

• Influence of automobile seat form and comfort rating on willingness-to-pay

  International Journal of Vehicle Design · 2018-01-01 · 5 citations

  article

  Customers nowadays consider the driver's seat, specifically its comfort and aesthetic form, during the automobile purchase decision. As a result, much research has been recently conducted into seat comfort and the influence of the visual appearance of the seat on the perception of comfort. However, the cost of the seat remains an important contributor to overall vehicle cost, and the visual appearance of a seat may influence a customer's willingness to pay. We conducted an experiment measuring this tradeoff using hierarchical Bayesian conjoint analysis, a marketing method that elicits customer preferences and willingness-to-pay at the individual customer level. Utility models are statistically inferred for three brand segmentations using a dataset obtained through an online interactive web application. Results indicate that in a heterogeneous market, willingness-to-pay is affected by seat form and comfort rating, with particularly significant tradeoffs for the luxury automotive brand segment.

  PublisherDOI

Frequent coauthors

• Panos Y. Papalambros

  Design Science (United States)

  15 shared

• John R. Hauser

  Massachusetts Institute of Technology

  10 shared

• Artem Timoshenko

  8 shared

• Richard Gonzalez

  7 shared

• Matthew P. Reed

  University of Michigan–Ann Arbor

  6 shared

• K. Han Kim

  6 shared

• Richard J. Gerth

  5 shared

• Yi Ren

  Institute of Software

  4 shared