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Nathan Urban, Department of Neurobiology, University of Pittsburgh Title and Abstract Pending https://calendar.colorado.edu/event/applied_math_colloquium_-_nathan_urban_3165

Katherine Morrison, School of Mathematical Sciences, University of Northern Colorado Predicting neural network dynamics from graph structure Neural networks often exhibit complex patterns of activity that are shaped by the intrinsic structure of the

Engineers, like everyone, are influenced by media messages every day. Don’t miss this opportunity to interact with TED speaker, renowned media scholar and CU Â鶹ӰԺ alumnus, Chris Bell, PhD and consider how media impacts your personal and

nPatrick Sprenger, Department of Applied Mathematics, Â鶹ӰԺ Generalized Riemann problems in dispersive hydrodynamics Nonlinear, dispersive wave phenomena are observed in a variety of physical contexts in nature and the

Mohsen Imadi; Department of Computer Science and Engineering; University of California, San Diego Towards Learning with Brain Efficiency Modern computing systems are plagued with significant issues in efficiently performing learning tasks. In this

Stephen Pankavich, Department of Applied Mathematics and Statistics, Colorado School of Mines Kinetic Models of Collisionless Plasmas Collisionless plasmas arise in a variety of settings, ranging from magnetically confined plasmas for thermonuclear

Justin ColeDepartment of Applied Mathematics, Â鶹ӰԺSoliton Dynamics in the Korteweg-de Vries Equation with Nonzero Boundary ConditionsInspired by recent experiments, the Korteweg-de Vries equation with nonzero Dirichlet

Maria KazachenkoDepartment of Astrophysics and Planetary Science (National Solar Observatory), Â鶹ӰԺData-driven Models of the Solar Corona Magnetic Fields: Present and FutureThe most violent space weather events, eruptive

S. Chakravarty, Department of Mathematics, University of Colorado Colorado Springs A class of rational solutions of the KPI equation I will revisit a class of KP "lump" solutions which were studied by Ablowitz & Villarroel as rational potentials

Alec Dunton, Department of Applied Mathematics, Â鶹ӰԺ Learning a kernel matrix for nonlinear dimensionality reduction (Weinberger et. al. 2004) We investigate how to learn a kernel matrix for high dimensional data that lies