William Gilpin

The Gilpin group researches at the intersection of fluid dynamics, statistical learning, and systems biology, developing new theory and algorithms for analysis and control of chaotic systems—like turbulent bursts or neural spikes—and collaborate to apply these tools broadly. Projects include novel machine learning algorithms for time series, drawing upon the classical physics of complex systems like strange attractors, cellular automata, and random graphs, and applying these principles to real-world complex systems such as biological measurements and fluid mixing. Research spans nonlinear dynamics and chaos, data-driven modeling and statistical learning, mathematical biology, complex fluid flows and pattern formation.