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Emily King, Department of Mathematics, Colorado State UniversityInterpretable, 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

Madeleine Udell, Institute for Computational and Mathematical Engineering, Stanford UniversityLow rank approximation for faster optimizationLow rank structure is pervasive in real-world datasets. This talk shows how to accelerate the solution

Bradley Warner, Director of Data Science Program, US Air Force AcademyData Science Education: A Journey of Creation and Implementation    In the increasingly data-driven landscape of decision-making, the demand for skilled data

Maria Kazachenko, Laboratory for Atmospheric and Space Phenomena (LASP), Â鶹ӰԺSolar Magnetic Fields Before and During Eruptions: Perspectives with the Largest Telescope to observe the Sun (DKIST)Space weather is largely

Abigail Crocker, Department of Mathematics & Statistics, The University of VermontStatistics and Social JusticeTransforming the criminal-legal system is of broad interest. There is growing recognition of the inequities in our current system as

Henrik Kalisch, Department of Mathematics, University of Bergen, NorwayCoastal Modeling in Norway: When Shallow Water Runs DeepFor coastal communities around the world, catastrophic storm waves and storm surge events pose the biggest threat to life

Manas Rachh, Center for Computational Mathematics, Flatiron InstituteStatic Currents in Type-I superconductorsIn this talk, we describe the classical magneto-static approach to the theory of type-I superconductors. The magnetic field and the current

Nadir Jeevanjee, Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration (NOAA)The surprising fluid dynamics of atmospheric thermalsAtmospheric convection is both ubiquitous and fundamental to the climate system, as

Enrico Camporeale, Space Weather Prediction Center, National Oceanic and Atmospheric AdministrationUsing Physics-Informed Neural Networks for solving inverse problems: a space physics case studyWe use the framework of Physics-Informed Neural Network

Peter Thomas; Department of Mathematics, Applied Mathematics, and Statistics; Case Western Reserve UniversityA Universal Description of Stochastic OscillatorsMany systems in physics, chemistry and biology exhibit oscillations with a pronounced