PhD in Marketing Students

My research interests center primarily around the consumer identity project and the ways in which such identities are constructed through decision-making processes within the post-modern consumer society. My studies aim to examine the subjective experiences of identity formation during symbolic consumption and the factors that contribute to the maintenance or disruption of these identities within the technologocal hyperreality that is manufactured by brands and social influencers.

My research interests mainly lie in targeting and personalization, and digital marketing. Specifically, I am interested in extracting policy-relevant insights from fine-grained level consumer behavior data collected by digital technology, and leverage this information to design effective and efficient targeting policies building on recent advancements in causal machine learning and policy learning.

I am interested in social hierarchies and symbolic consumption. My dissertation focuses on symbolic product trends: I examine what makes a product vintage, why and when fashions make comebacks, and why genderfluid trends are uneven. I use mixed-method approaches, including machine learning, algorithmic frameworks, interviews, surveys, lab and field experiments, and secondary data analysis.

I am a consumer behavior researcher interested in finding ways in which marketers can contribute to bridging people together. Specifically, my research focuses mainly on understanding the psychology behind (1) the social dynamics of consumers and (2) the way consumers exert or avoid autonomy in the marketplace, with the goal of providing novel insights and interventions for firms to maximize consumer welfare. 

My research focuses on gleaning business insights from unstructured data like images and text. Recently, my work explores how to estimate latent treatment effects in various contexts, such as for user-generated content and social issues like discrimination. This research lies at the nexus of several methods including deep generative modeling, representation learning, applied econometrics, and causal inference.

I am an empirical methodologist focusing on statistical and econometric methods. In my prior work, I develop methods for selection correction and causal inference with customer base panel data. Currently, I am working on projects based around representation learning methods for unstructured data and empirical modeling of boundedly rational behavior.

My research focuses on understanding how consumers and firms interact with various emerging technologies such as livestream, non-fungible tokens (NFTs), and the Metaverse. Specifically, I focus on explaining various interesting phenomenon that results from 1) the emergence of these technologies and 2) the use of said technologies by marketplace stakeholder. In doing so, I rely on psychological theories-with a focus on social proof, motivation, and cognitive biases-and use methodologies such as experimental design, empirical modeling, and neuroscience in my research projects.

I’m interested in understanding what makes each of us get out of bed in the  morning. For me, it’s connecting with others, being active and outside and trying  to better understand how we are all navigating a chaotic world just trying  to feel better. How can we guide consumers to make healthier choices for themselves and others? How do consumers think about their health versus their wellness? We are all more complicated than we can verbalize, so what are the underlying  cognitive processes that form our preferences and define our sense of self?

I study the behavioral dimensions of climate change and the policy application of behavioral interventions.

I study the role of unstructured data (e.g., images, texts and videos) in marketing communications and consumption experiences. I am also broadly interested in the topics in creator economy. Specifically, my research projects are built around the tension that content creators face as they commercialize. I use a variety of techniques including representation learning, Bayesian modeling and analytical modeling.

My research interest primarily lies in obtaining information from textual data about consumer behavior and psychology, with a focus on the interplay between emotions and cognitive processes. I work with user-generated content (e.g., online reviews, conversations, thought-listings) to understand how the properties of language can predict consumers’ post-sharing feelings and revisit intentions, cognitive performance (e.g., creativity), and decisions (e.g., when to claim their social security benefit).

My broad research agenda centers on digital marketing and creator economy. Specifically, my research focuses on understanding the production and promotion decisions of content creators (e.g., influencers, gamers, advertisers) on online video platforms, and providing insights on how industry practitioners and platforms can leverage this information for profit and welfare improvement. I explore these topics using a combination of machine learning techniques, structural modeling and analytical modeling methods.