Applied Math Colloquium

<p><strong><span>Fred Daum, Principal Fellow, Raytheon Technologies</span> </strong><br />
<span>Particle Flow for Filtering and Bayesian Deep Learning</span></p>

<p><strong>Bayesian Spatially Varying Multi-Regularization Image Deblurring</strong><br />
Jessica Pillow, Program in Applied Mathematics, University of Arizona</p>

<p><strong>Filtering methods for Inverse Problems</strong><br />
Michael Herty, Department of Mathematics, TWTH Aachen University, Germany</p>

<p><strong>Output-Weighted Active Sampling for Bayesian Uncertainty Quantification and Prediction of Rare Events</strong><br />
Themistoklis Sapsis, Department of Mechanical Engineering, MIT</p>

<p><strong>Physics Guided Deep Learning for Spatiotemporal Dynamics</strong><br />
Rose Yu, Department of Computer Science and Engineering, UC San Diego</p>

<p><strong>Physical Discovery by Machine Learning: from Symmetries and Chemical Reactions to Generative and Causal Models</strong><br />
Sergei Kalinin, Oak Ridge National Laboratory, Corporate Fellow, Group Leader for Data Nano Analytics Group (DNA), The Center for Nanophase Materials Sciences</p>

<p><strong>Plasma Instabilities in the Young Solar Wind: Thermodynamics far from Equilibrium&nbsp;</strong>&nbsp;&nbsp;&nbsp;&nbsp;<br />
Kristopher Klein, Lunar and Planetary Laboratory, Department of Planetary Sciences, University of Arizona</p>

<p><strong>Likelihood ratio methods for sensitivity analysis and linear response estimation&nbsp;of non-equilibrium stationary states</strong><br />
Petr Plechac, Dept. of Mathematical Sciences, University of Delaware,</p>

<p><strong>Brownian Dynamics Simulations for Flexible Fibers</strong><br />
Brennan Sprinkle, Department of Mathematics, NYU</p>

<p><strong>Combining network analysis and persistent homology for classifying behavior of time series</strong><br />
Elizabeth Munch,&nbsp;Dept.&nbsp;of Computational Mathematics, Science, and Engineering, Dept. of Mathematics, Michigan State University</p>