MATH + X Symposium on Data Science and Inverse Problems in Geophysics

Wednesday, January 24

8:15 AM Maarten de Hoop – Welcome
Reginald DesRoches – Opening Remarks
8:30 AM – Nathan Kutz – Data-driven discovery of governing physical laws and their parametric dependencies in engineering, physics and biology
9:20 AM – Michel Campillo – From “ambient noise” to seismic imaging and monitoring
10:35 AM – Andrès Vasy – Global analysis via microlocal tools: Fredholm problems in non-elliptic settings
11:25 AM – Andrew Stuart – Large graph limits of learning algorithms
1:15 PM – Matti Lassas – Manifold learning and an inverse problem for a wave equation
2:05 PM – Frederik Simons – On the inversion of noisy, incomplete, scattered, and vector-valued satellite-data for planetary magnetic-field models
3:20 PM – Elchanan Mossel – Hierarchal generative models and deep learning
4:10 PM – Andrea Bertozzi – Geometric graph-based methods for high dimensional data

Thursday, January 25

8:30 AM – Gregory Beroza – FAST: a data-mining approach for earthquake detection
9:20 AM – Ankur Moitra – Robustness meets algorithms
10:35 AM – Yiran Wang – Inverse problems for nonlinear acoustic and elastic wave equations
11:25 AM – Carola-Bibiane Schönlieb – Model-based learning in imaging
1:15 PM – Joan Bruna – On the optimization landscape of neural networks
2:05 PM – Lauri Oksanen – Correlation based passive imaging with a white noise source
3:20 PM – Victor Pankratius – Computer-aided discovery: can a machine win a Nobel Prize?
4:10 PM – Robert Nowak – Outranked: exploiting nonlinear algebraic structure in matrix recovery problems

Friday, January 26

8:30 AM – Stèphane Mallat – Unsupervised learning of stochastic models with deep scattering networks
9:20 AM – Peter Hintz – Reconstruction of Lorentzian manifolds from boundary light observation sets
10:35 AM – Paul Johnson – Probing fault frictional state with machine learning
11:25 AM – Ivan Dokmanić – Regularization by multiscale statistics
1:15 PM – Elizabeth Rampe – Interpreting data collected by the NASA Mars Science Laboratory Rover
1:40 PM – Michael McCann – Convolutional neural networks for inverse problems in imaging
2:30 PM – Joonas Ilmavirta – Communication between theory and practice via deep learning and “deep teaching”
3:20 PM – Alexandros Dimakis – Generative models and compressed sensing
4:10 PM – Rich Baraniuk – (Geo) Physics 101 for data scientists