Research

Main interests

Biostatistics: Applying machine learning methods to studying and characterizing pandemic growth trajectories.

Finance: Innovating upon existing option pricing and implied volatility models with deep learning methodologies.

Political Science: Using traditional and novel statistical approaches to analyze election data.

Publications

Tang, F., Feng, Y., Chiheb, H., and Fan, J. (2021). “The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of U.S. COVID-19 Cases”. Journal of the American Statistical Association. [Link].

Almeida, C., Fan, J., Freire, G., and Tang, F. (Alphabetical order) (2022). “Can a Machine Correct Option Pricing Models?” Journal of Business and Economic Statistics, to appear. [Link].

Working papers and projects

Research discontinuity design: the “Incumbency Curse” in Latin America.

Quantifying charisma of world politicians through text analysis and NLP.

Automating text gathering and applying machine learning methodologies in the field of political science.

Please find my full CV here.