Seminars

Emily King, Department of Mathematics, Colorado State University Interpretable, Explainable, and Adversarial AI: Data Science Buzzwords and You (Mathematicians) Many state-of-the-art methods in machine learning are black boxes which do not allow humans to understand how decisions are made. In a number of applications, like medicine and atmospheric science, researchers...

Madeleine Udell, Institute for Computational and Mathematical Engineering, Stanford University Low rank approximation for faster optimization Low rank structure is pervasive in real-world datasets. This talk shows how to accelerate the solution of fundamental computational problems, including eigenvalue decomposition, linear system solves, composite convex optimization, and stochastic optimization (including deep...

Bradley Warner, Director of Data Science Program, US Air Force Academy Data Science Education: A Journey of Creation and Implementation In the increasingly data-driven landscape of decision-making, the demand for skilled data scientists is on the rise. Therefore, the development and execution of data science education programs have become essential...

Maria Kazachenko, Laboratory for Atmospheric and Space Phenomena (LASP), University of Colorado Boulder Solar Magnetic Fields Before and During Eruptions: Perspectives with the Largest Telescope to observe the Sun (DKIST) Space weather is largely caused by the activity of our Sun. Invisible yet powerful magnetic fields, created within the Sun,...

Abigail Crocker, Department of Mathematics & Statistics, The University of Vermont Statistics and Social Justice Transforming the criminal-legal system is of broad interest. There is growing recognition of the inequities in our current system as well as its negative impacts on justice-involved populations and communities, including harms from mass incarceration...

Henrik Kalisch, Department of Mathematics, University of Bergen, Norway Coastal Modeling in Norway: When Shallow Water Runs Deep For coastal communities around the world, catastrophic storm waves and storm surge events pose the biggest threat to life and property. For these major events, forecasting is relatively reliable, protective structures are...

Manas Rachh, Center for Computational Mathematics, Flatiron Institute Static Currents in Type-I superconductors In this talk, we describe the classical magneto-static approach to the theory of type-I superconductors. The magnetic field and the current in type-I superconductors are related by the London equations and tend to decay exponentially inside the...

Nadir Jeevanjee, Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration (NOAA) The surprising fluid dynamics of atmospheric thermals Atmospheric convection is both ubiquitous and fundamental to the climate system, as the primary means of communicating heat from the surface to the atmosphere. The fundamental building block of atmospheric convection...

Enrico Camporeale, Space Weather Prediction Center, National Oceanic and Atmospheric Administration Using Physics-Informed Neural Networks for solving inverse problems: a space physics case study We use the framework of Physics-Informed Neural Network (PINN) to solve the inverse problem associated with the Fokker-Planck equation for radiation belts' electron transport, using 4...

Peter Thomas; Department of Mathematics, Applied Mathematics, and Statistics; Case Western Reserve University A Universal Description of Stochastic Oscillators Many systems in physics, chemistry and biology exhibit oscillations with a pronounced random component. Such stochastic oscillations can emerge via different mechanisms, for example linear dynamics of a stable focus with...