1.0Physics & Astronomyhttps://uwm.edu/physicsElle Cochranehttps://uwm.edu/physics/author/cochratcuwm-edu/Physics Colloquium - Andrew Fergusonrich600338<blockquote class="wp-embedded-content" data-secret="l3AXva3JpF"><a href="https://uwm.edu/physics/event/physics-colloquium-andrew-ferguson/">Physics Colloquium – Andrew Ferguson</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://uwm.edu/physics/event/physics-colloquium-andrew-ferguson/embed/#?secret=l3AXva3JpF" width="600" height="338" title="“Physics Colloquium – Andrew Ferguson” — Physics & Astronomy" data-secret="l3AXva3JpF" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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Andrew Ferguson, University of Chicago Reconstructing All-Atom Protein Folding from Low-Dimensional Experimental Time Series Data-driven modeling and machine learning present powerful tools that are opening up new paradigms and opportunities in the understanding, discovery, and design of soft and biological materials. In the first part of this talk, I will describe an approach integrating ideas from dynamical systems theory and nonlinear manifold learning to reconstruct multidimensional protein folding funnels from the time evolution of single experimentally measurable observables.