Research

Research focus

My interests center on using machine learning and computational modeling to understand global manufacturing and industrial systems. I am especially interested in predictive methods, including multi-agent models and reinforcement learning, and in the ways firms, production networks, and markets respond to technological, economic, and geopolitical change.

Supply-Chain Risk & Financial Markets

Independent Research | 2026 - Present

This project examines whether elevated supply-chain risk is associated with negative abnormal returns. I developed a Python pipeline to collect and analyze SEC 10-Ks, XBRL, and equity-market data across 450 public companies in nine sectors.

The work uses Word2Vec to create supply-chain risk and resolution dictionaries from 20 years of SEC filings and earnings calls. I apply FinBERT to quantify information in corporate disclosures and investigate relationships with market returns. The event-study and regression analyses compare cumulative abnormal returns (CAR) to expected returns using a four-factor benchmark and firm-specific market-response patterns.

For related implementation details, see Projects.