Complex/Dynamical Systems Seminar: Saverio Spagnolie

Feb. 7, 2019

Suspensions of active particles in fluids exhibit incredibly rich behavior, from organization on length scales much longer than the individual particle size to mixing flows and negative viscosities. We will discuss the dynamics of hydrodynamically interacting motile and non-motile stress-generating swimmers or particles as they invade a surrounding viscous fluid,...

Stats, Optimization, and Machine Learning Seminar - Osman Malik and Ann-Casey Hughes

Feb. 5, 2019

Osman Malik - Fast Randomized Matrix and Tensor Interpolative Decomposition Using CountSketch In this talk I will present our recently developed fast randomized algorithm for matrix interpolative decomposition. If time permits, I will also say a few words about how our method can be applied to the tensor interpolative decomposition...

Department Colloquium - Emiliano Dall'anese

Feb. 1, 2019

Feedback-based online algorithms for time-varying network optimization The talk focuses on the synthesis and analysis of online algorithmic solutions to control networked systems based on performance objectives and engineering constraints that may evolve over time. The time-varying optimization formalism is leveraged to model optimal trajectories of the networked systems, as...

Nonlinear Waves Seminar - Michelle Maiden

Jan. 29, 2019

Soliton fission from a large disturbance in the viscous fluid conduit system The resolution of a large, localized initial disturbance is considered in the context of the viscous fluid conduit system--the driven, cylindrical, free interface between two miscible Stokes fluids with high viscosity contrast. Due to buoyancy induced nonlinear self-steepening...

Department Colloquium - Dan Zhang

Jan. 25, 2019

Finite-Horizon Approximate Linear Programs for an Infinite-Horizon Revenue Management Problem Approximate linear programs have been used extensively to approximately solve stochastic dynamic programs that suffer from the well-known curse of dimensionality. Due to canonical results establishing the optimality of stationary value functions and policies for infinite-horizon dynamic programs, the literature...

Nonlinear Waves Seminar - Mark Siemens

Jan. 22, 2019

Quantum Turbulent Structure in Light The infinite superpositions of random plane waves are known to be threaded with vortex line singularities which form complicated tangles and obey strict topological rules. We observe that within these structures a timelike axis appears to emerge with which we can define vortex velocities in...

APPM+CS Postdoc Seminar: Olena Burkovska

Jan. 18, 2019

Title: " Approximation of parametrized kernels arising in nonlocal and fractional Laplace models" Abstract: We consider parametrized linear and obstacle problems driven by a spatially nonlocal integral operator. These problems have a broad impact on current developments in different fields such as, e.g., peridynamics, contact mechanics, and finance. We focus...

Department Colloquium - Adam McCloskey

Jan. 18, 2019

Inference on Winners Many empirical questions can be cast as inference on a parameter selected through optimization. For example, researchers may be interested in the effectiveness of the best policy found in a randomized trial, or the best-performing investment strategy based on historical data. Such settings give rise to a...

Stats, Optimization, and Machine Learning Seminar - Lio Horesh

Jan. 15, 2019

"Don't go with the flow -- – A new tensor algebra for Neural Networks" Multi-dimensional information often involves multi-dimensional correlations that may remain latent by virtue of traditional matrix-based learning algorithms. In this study, we propose a tensor neural network framework that offers an exciting new paradigm for supervised machine...

APPM+CS Postdoc Seminar - Jeffrey Hokanson

Dec. 14, 2018

In many applications throughout science and engineering, model reduction plays an important role replacing expensive large-scale linear dynamical systems by inexpensive reduced order models that capture key features of the original, full order model. One approach to model reduction is to find reduced order models that are locally optimal approximations...

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