Research

Research Interests: 

Applied Microeconometrics; Behavioral Economics; Industrial Organization; Consumer Behavior and Marketing;

Fashion Marketing; Digital Marketing; Social Media Marketing; Luxury Brand Marketing; Electronic Word of Mouth (eWOM)

Research Topics: 

• The Economics of Social Media

• The Economics of the Fashion Industry

• The Impact of Brands on Consumer Behavior

• The Impact of Online Social Networks on Economic Behavior

• Gender and Leadership in Social Media Marketing (TikTok & Fashion Brands)

• Social Media Influencers and the Price–Quality Belief in Luxury Consumption

Virginia Tech, Blacksburg, VA

Research Assistant in Operations Research, Grado Department of Industrial and Systems Engineering, June 2025 - Present 

Scheduling Institutionalization Six Months Ahead: A Parsimonious Markov Decision Process (Advisor: Prof. Huaiyang Zhong & Prof. Ali Hajjar)

In the project “Scheduling Institutionalization Six Months Ahead: A Parsimonious Markov Decision Process,” conducted under the supervision of Prof. Huaiyang Zhong and Prof. Ali Hajjar, I apply Markov Decision Processes (MDPs) to optimize the timing of institutionalization for dementia patients, seeking to balance clinical progression with caregiver burden. To establish a rigorous foundation, I reviewed over one hundred studies on dynamic decision-making in chronic disease and dementia care, extracting state definitions, transition matrices, sojourn times, and cost–reward structures into a structured evidence framework that informed model design. Building on this groundwork, I synthesized two core MDP papers to identify transferable modeling assumptions and policy iteration mechanisms applicable to healthcare contexts. Our current work integrates both clinical indicators (MMSE, CDR, NPI, FAQ) and caregiver burden measures (ZBI, CBI) into a six-month look-ahead model that determines whether continued home care or institutionalization maximizes cumulative reward. I also developed a reproducible Python simulation framework to calibrate transition probabilities, validate policy robustness, and conduct sensitivity analyses across stochastic parameter spaces. This project advances a parsimonious and ethically informed decision model that bridges quantitative optimization with the human dimensions of dementia care.

Virginia Tech, Blacksburg, VA 

Research Assistant in Economics, Department of Economics, May 2023 - August 2023

The Impact of Online Social Networks on Economic Behavior (Advisor: Prof. Xu Lin) 

Under the supervision of Prof. Xu Lin, I investigated how digital interaction shapes individual and aggregate economic behaviors. The project explored both the structural formation of online social networks and the diffusion of peer influence across regions. I helped construct large-scale datasets combining social media activity with economic indicators, designed variable definitions, and built reproducible R and Stata pipelines for OLS and logit analysis. In addressing endogeneity and omitted-variable bias, I contributed to the development of a simultaneous structural framework that models how network formation and behavioral contagion coevolve. This research deepened my interest in how digital connectivity modifies classical assumptions of independent decision-making in economics.

Virginia Tech, Blacksburg, VA

Undergraduate Research Assistant in Economics, Department of Economics, January 2022 - May 2023

The Impact of Brands on Consumer Behavior (Advisor: Prof. Xu Lin)

As an undergraduate and later graduate research assistant under Prof. Xu Lin in the Department of Economics at Virginia Tech, I participated in a multi-semester project examining how fashion brands and social media engagement shape consumer decision-making. My work focused on two themes—brand signaling and electronic word-of-mouth (eWOM)—within the broader framework of The Economics of the Fashion Industry, Social Media, and The Impact of Brands on Consumer Behavior. I reviewed approximately one hundred academic papers on social media and consumption, producing structured summaries and reflections that guided our analytical design. Using panel and survey data, I constructed engagement variables such as view, like, and comment frequencies, cleaned data pipelines in Stata and R, and applied OLS and logit models to assess how these online interactions influence purchase intention and brand perception. This experience strengthened my empirical research skills while deepening my understanding of how narrative cues, authenticity signals, and social proof interact with economic rationality to shape consumer behavior in digital markets.