## Seminars

## Statistics, Optimization, and Machine Learning Seminar - Pratyush Tiwary (Virtual)

April 28, 2020

Pratyush Tiwary; Department of Chemistry & Biochemistry and Institute for Physical Science and Technology; University of Maryland From atoms to emergent dynamics (with help from statistical physics and artificial intelligence) ABSTRACT: The ability to rapidly learn from high-dimensional data to make reliable predictions about the future of a given system...

## Applied Math Colloquium - Lou Pecora

Feb. 28, 2020

Lou Pecora, Naval Research Laboratory Cluster Synchronization of Chaotic Systems in Complex Networks The concept of synchronized systems has been around for centuries with one of the earliest studies being on the synchronization of clocks by Christiaan Huygens in the mid 1700’s. By the mid 1900’s it was well-known how...

## Nonlinear Waves Seminar - Vera Hur

Feb. 25, 2020

Vera Hur, Department of Mathematics, University of Illinois Stokes waves in a constant vorticity flow: theory and numerics Stokes in the 1800s made many contributions about periodic waves at the surface of water, under the influence of gravity, propagating in permanent form a long distance at a practically constant velocity...

## Stats, Optimization, and Machine Learning Seminar - Anindya De

Feb. 25, 2020

Anindya De, Department of Computer and Information Science, University of Pennsylvania Testing noisy linear functions for sparsity Consider the following basic problem in sparse linear regression -- an algorithm gets labeled samples of the form (x, + \eps) where w is an unknown n-dimensional vector, x is drawn from a...

## Applied Math Colloquium - Owen Miller

Feb. 21, 2020

Owen Miller, Department of Applied Physics, Yale University Upper bounds to electromagnetic response via convexity, causality, and duality Nanophotonics, the study of light interacting with materials patterned at the scale of the wavelength, is developing at a rapid pace, with ever more materials, form factors, and structural degrees of freedom...

## Applied Math Colloquium - Laura Miller

Feb. 14, 2020

Laura Miller, Department of Biology, University of North Carolina - Chapel Hill Flow through the hairy appendages of small animals: The leaky rake to solid plate transition. Numerous small organisms that swim, fly, smell, or feed in flows at the mesoscale, where inertial and viscous forces are balanced, use branched,...

## Applied Math Colloquium - Bengt Fornberg

Feb. 7, 2020

Bengt Fornberg, Department of Applied Mathematics, University of Colorado Boulder Euler-Maclaurin without analytic derivatives We consider here the Euler-Maclaurin (EM) formulas in the context of approximating infinite sums. If the function to be summed can be integrated analytically, these formulas provide highly accurate asymptotic expansions for the difference between the...

## Nonlinear Waves Seminar - Thibault Congy

Feb. 4, 2020

Thibault Congy; Department of Mathematics, Physics, and Electrical Engineering; University of Northumbria; Newcastle, UK Bidirectional soliton gas The soliton structure plays a fundamental role in many physical systems due to its fundamental feature: its shape remains unchanged after the collision with another soliton in the case of integrable dynamics. Such...

## Applied Math Colloquium - Katie Oliveras

Jan. 31, 2020

Katie Oliveras, Department of Mathematics, Seattle University Measuring Water Waves: Using Pressure to Reconstruct Wave Profiles Euler's equations describe water-waves on the surface of an ideal fluid. In this talk, I will discuss an inverse problem related to measuring water-waves using pressure sensors placed inside the fluid. Using a non-local...

## Stats, Optimization, and Machine Learning Seminar - Stephen Becker

Jan. 28, 2020

Stephen Becker, Department of Applied Mathematics, University of Colorado Boulder Stochastic Subspace Descent: Stochastic gradient-free optimization, with applications to PDE-constrained optimization We describe and analyze a family of algorithms that generalize block-coordinate descent, where we assume one can take directional derivatives (for low-precision optimization, this can be approximated with finite...