Simon Fraser University

Bayesian inference provides a natural framework for quantifying uncertainty in model parameters and predictions, and for combining heterogeneous sources of information. But the computational demands of the Bayesian framework constitute a major...

Geometric numerical integration aims to develop numerical schemes that preserve geometric properties of differential equations. Such properties can include symmetries, conservation laws, a symplectic structure or blow-up properties. In this talk I...

Developmental Systems Theory (Gottlieb, 2007; Johnston & Edwards, 2002; Oyama, 2000) offers a compelling approach to development. Stressing bidirectional flow of information between genes, biological structures like the brain, and behavior, it offers...

Stochastic differential equations of motion (e.g., the Langevin equation) provide a popular framework for simulating molecular systems. Any computational algorithm must discretize these equations, yet the resulting finite time step integration...

Differential equations (DEs) are widely used to model complex dynamical systems in many areas including neuroscience, biology, medicine, genetics, ecology, engineering, physics, and finance. While parameters in DEs usually have scientific...

Aggregation is an ubiquitous natural phenomenon that pervades both the animal world and many inanimate physical systems. In the animal kingdom, group formation is observed across all levels from bacterial colonies and insect swarms to complex...