Academic Work
Research & Publications
Doctoral dissertation, Harvard capstone, and research papers spanning quantitative finance, epidemiology, social media analytics, and natural language processing.
Doctoral Dissertation
Use of Bio-Inspired Metaheuristic Methods for Modeling and Prediction of High-Volatility Stock Markets: The Argentinian Case
Universidad Nacional de la Matanza — PhD in Economic Sciences
Doctoral thesis applying evolutionary algorithms, swarm intelligence, and other bio-inspired metaheuristic techniques to model and forecast price behavior in volatile equity markets, with a focus on the Argentine stock market (Merval index). Investigated the effectiveness of genetic algorithms, particle swarm optimization, and ant colony optimization for financial time-series prediction.
Research Papers & Projects
Exploration of Lyme Disease Incidence Rate Modeling and Risk Assessment Mapping
Harvard University — Data Science Capstone
Dr. Gabriel Guillen, Yang Ming (Jason) Lin, Peter Masters
Developed predictive models for Lyme disease incidence using geospatial analysis, epidemiological data, and machine learning to produce county-level risk assessment maps across the Northeastern United States.
Predicting COVID-19 Disease Spread Using Google Mobility Data and Clustering in Census Data
Harvard University — Data Science Research
Dr. Gabriel Guillen et al.
Applied clustering algorithms to Google Mobility Reports combined with U.S. Census demographic data to build predictive models for COVID-19 transmission rates at county and state levels during the pandemic.
Benford's Law Applied to Twitter Data
Harvard University — Data Science Research
Dr. Gabriel Guillen et al.
Investigated whether naturally occurring Twitter engagement metrics (retweets, likes, follower counts) conform to Benford's Law, exploring applications for fraud detection and anomaly identification in social media data.
Predicting U.S. Future Derivatives Market of Wheat Using NLP in President Trump Tweets
Harvard University — Data Science Research
Dr. Gabriel Guillen et al.
Used natural language processing (NLP) and sentiment analysis on the former U.S. President's social media output to build predictive models for wheat futures price movements, demonstrating the market impact of political communication.
How to Predict Spread Movements
Lehigh University — 2019 IAFC Competition
Dr. Gabriel Guillen
Analyzed quantitative models for predicting bid-ask spread movements in equity and derivatives markets, presented at the International Association of Financial Colleges competition.
Research Methods & Tools