This work reflects the scientific side of my practice: modeling difficult systems, analyzing dynamic behavior, and building a foundation of rigor that still shapes how I approach machine learning and AI today.
Studies how synchronization breaks and reforms in a nonlinear circuit model, highlighting transition patterns that help explain unstable collective behavior.
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Examines how economic dynamics shift between stable and unstable regimes, using nonlinear systems tools to make complex macro behavior more interpretable.
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Explores how linked economic systems influence each other over time, showing how coupling can reshape the stability and rhythm of business cycles.
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Uses control theory and network dynamics to study synchronization across layered systems, with an emphasis on how coordinated behavior can be induced or stabilized.
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Investigates how amplification changes the behavior of multilayer networks, offering insight into when interactions across layers intensify or destabilize the system.
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