## Seminars

## Applied Math Colloquium - Katherine Morrison

Nov. 8, 2019

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 network. For example, spontaneous sequences of neural activity have been observed in cortex and hippocampus, and...

## Applied Math Colloquium - Stephen Pankavich

Nov. 1, 2019

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 research to space plasmas in planetary magnetospheres and solar winds. The two fundamental models that describe such phenomena...

## Applied Math Colloquium - Maria Kazachenko

Oct. 25, 2019

Maria Kazachenko Department of Astrophysics and Planetary Science (National Solar Observatory), University of Colorado Boulder Data-driven Models of the Solar Corona Magnetic Fields: Present and Future The most violent space weather events, eruptive solar flares and coronal mass ejections, are driven by the release of free magnetic energy stored in...

## Applied Math Colloquium - Manuchehr Aminian

Oct. 18, 2019

Manuchehr Aminian, Department of Mathematics, Colorado State University Using mathematical tools to understand the immune system How soon after exposure can we identify that you're infected with the flu? Can this be done prior to the onset of symptoms? Why do some animals tolerate bacterial infections better than others? It...

## Applied Math Colloquium - Steve Sain

Oct. 11, 2019

Steve Sain Jupiter Intelligence, Boulder CO Data Science @ Jupiter In 2017, the US faced over $300 billion in loses from hurricanes, severe weather, flooding, drought, fire, etc. These national catastrophes are placing an increasing burden on the public and are projected to worsen in the future. Jupiter Intelligence provides...

## Applied Math Colloquium - Andreas Kloeckner

Oct. 4, 2019

Andreas Kloeckner Department of Computer Science, University of Illinois Fast Algorithms for the Evaluation of Layer and Volume Potentials I will present new, asymptotically fast algorithms with high-order error bounds for the evaluation of layer and volume potentials in two and three dimensions. The efficient evaluation of both types of...

## Applied Math Colloquium - Rebecca Morrison

Sept. 27, 2019

Rebecca Morrison Department of Computer Science, University of Colorado Boulder Representing model error in reduced models of interacting systems In many applications of interacting systems, we are only interested in the dynamic behavior of a subset of all possible active species. For example, this is true in combustion models (many...

## Joint APPM/MATH Colloquium - Catherine Sulem

Sept. 24, 2019

Catherine Sulem Department of Mathematics, University of Toronto Bloch theory and spectral gaps for linearized water waves We consider the movement of a free surface of a two-dimensional fluid over a variable bottom. We assume that the bottom has a periodic prole and we study the water wave system linearized...

## Applied Math Colloquium - John Harlim

Sept. 20, 2019

John Harlim Departments of Mathematics and Meteorology, Penn State University Manifold learning based computational methods Recent success of machine learning has drawn tremendous interests in applied mathematics and scientific computations. In this talk, I will discuss recent efforts in using manifold learning algorithms (a branch of machine learning) to do...

## Applied Mathematics Colloquium - Matthew Norman

Sept. 17, 2019

Matthew Norman, Oak Ridge National Laboratory Fluids algorithms from a High Performance Computing Perspective Numerical approximations to Partial Differential Equations have provided diverse benefits to society over the years. Their applications in simulation codes have weathered a number of large changes in computing as well, from the original vector machines...