Criston Hyett is an applied mathematician at JuliaHub who combines scientific machine learning with component-based models to predict equipment failures before they happen. He has developed predictive maintenance products for sewage pumping stations and water treatment plants, and has led bespoke physics-informed machine learning projects in the automotive and semiconductor industries. He holds a PhD in Applied Mathematics from the University of Arizona, specializing in physics-informed reduced-order models of turbulence and differentiable simulators for optimizing gas and power networks under uncertainty. His background in embedded software keeps him focused on fast, reliable software.
