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Samuel Levy

Assistant Professor of Business Administration

Marketing Area

University of Virginia Darden School of Business

100 Darden Boulevard 
Charlottesville, Virginia 22903 USA 

Phone: 412-773-2627 
Email: levys@darden.virginia.edu

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Bio

I am an Assistant Professor of Business Administration in the Marketing Area at the University of Virginia Darden School of Business. My research investigates customer analytics in different contexts such as privacy, data fusion, pro-social behavior. I am also interested in economic models of choice with novel Bayesian statistical and machine learning methods in marketing. I received my PhD from Carnegie Mellon University.

My preferred methodological approaches are Bayesian econometrics, probabilistic machine learning, and Bayesian nonparametrics.

Interested in my research or in collaborating? Feel free to email me at levys@darden.virginia.edu. You can also find me on LinkedIn.
 
For more about my background, check out my CV.

Research

Digital Marketing Twins

with Longxiu Tian 

A novel deep generative framework that extracts latent features from individual customer brand survey responses from competing firms, to optimize individualized marketing policies and flexibly uncover key drivers of brand affinity.

We propose a privacy preserving data fusion methodology - a way to combine multiple datasets -  intended to preserve user anonymity while allowing for a robust and expressive data fusion process.

Relaxing Functional Form in Choice Models Through Gaussian Processes

with Alan Montgomery 

We build a general utility model to detect and "learn" non-linear patterns of consumption from the data.

Understanding the Dynamics of Appeals Scales to Infer Potential to
Donate using Bayesian Nonparametrics

with Joy Lu and Alan Montgomery 

We study the dynamic effect of suggested amounts, i.e. "appeals scales", on donation behavior combining a flexible nonparametric model and field experiments.

Multiview Topic Model For Purchase Prediction

with Dokyun Lee, Daniel McCarthy, and Alan Montgomery 

A Bayesian framework that integrates transaction and clickstream data, providing accurate predictions of purchase timing for industrial clients in a non-contractual business-to-business setting, and reveals diverse customer and product clusters.

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