Research Statement

COVID-19 has caused over 6 million deaths worldwide since late 2019 and an estimated $12.5 trillion in economic loss as of 2024. The pandemic exposed deep vulnerabilities in modern networked systems — disrupted supply chains, financial market volatility, misinformation amplified across social networks. The July 2024 CrowdStrike update failure brought airlines, hospitals, and businesses to a standstill worldwide, underscoring how far cascading effects travel inside interconnected systems. Climate change, pandemics, and food security all run on interdependent networks — transportation, finance, supply chains, ecology, social networks — that form the backbone of our societal and economic infrastructure. As the world grows more interconnected, resilient and interpretable network models become essential.

Research interest. I explore the fundamental principles of networked system dynamics by integrating machine learning, network science, and dynamical systems theory. The work spans theoretical innovation and practical application, and centers on efficient, interpretable models that improve decision-making in real-world contexts.