Seminars

Department Colloquium - Joshua A. Grochow

March 16, 2019

Computational Complexity, Dynamical Systems, and Non-Convex Optimization For a given computational problem, computational complexity asks the question of the resources needed - such as time, space, energy - by any algorithm which solves the problem. Despite algorithms being a form of discrete dynamical system (in both time & space), the...

APPM+CS Postdoc Seminar: Sophie Giffard

March 15, 2019

Title: Deep learning for hurricane forecasting Abstract: The forecast of hurricane trajectories is crucial for the protection of people and property, but machine learning techniques have been scarce for this so far. I will present a method that we developed recently, a fusion of neural networks, that is able to...

Department Colloquium - Fan Yang

March 15, 2019

Using Survival Information in Truncation by Death Problems Without the Monotonicity Assumption In some randomized clinical trials, patients may die before the measurements of their outcomes. Even though randomization generates comparable treatment and control groups, the remaining survivors often differ significantly in background variables that are prognostic to the outcomes...

Nonlinear Waves Seminar - Igor Rumanov

March 13, 2019

Whitham modulation theory - developments and open problems I discuss the development of Whitham modulation theory as a nonlinear WKB method successfully used to describe the behavior of nonlinear dispersive waves. Recent advances include the appearance of the Whitham theory for (2+1)-dimensional evolution equations of Kadomtsev-Petviashvili (KP) type. The systems...

Stats, Optimization, and Machine Learning Seminar - Anshumali Shrivastava

March 12, 2019

Hashing Algorithms for Extreme Scale Machine Learning In this talk, I will discuss some of my recent and surprising findings on the use of hashing algorithms for large-scale estimations. Locality Sensitive Hashing (LSH) is a hugely popular algorithm for sub-linear near neighbor search. However, it turns out that fundamentally LSH...

Department Colloquium - Nancy Rodriguez and Daniel Appelö

March 7, 2019

Nancy Rodriguez, Department of Applied Mathematics, University of Colorado Boulder "Mathematical Biology and Sociology: a Buff's Perspective" Daniel Appelö , Department of Applied Mathematics, University of Colorado Boulder “Computational Mathematics at CU Boulder”

Stats, Optimization, and Machine Learning Seminar - Shuang Li

March 5, 2019

Optimization for High-dimensional Analysis and Estimation High-dimensional signal analysis and estimation appear in many signal processing applications, including modal analysis, and parameter estimation in the spectrally sparse signals. The underlying low-dimensional structure in these high-dimensional signals inspires us to develop optimization-based techniques and theoretical guarantees for the above fundamental problems...

Department Colloquium - Jan S Hesthaven

March 1, 2019

ontrolling oscillations in high-order accurate methods through neural networks While discontinuous Galerkin methods have proven themselves to be powerful computational methods, capable of accurately solving a variety of PDE's, the combination of high-order accuracy and discontinuous solutions remain a significant challenge. Traditional methods such as TVB limiting or artificial viscosity...

Complex/Dynamical Systems Seminar - Shmuel Fishman

Feb. 28, 2019

Statistical Description of Hamiltonian Mixed Phase space systems and many Body Localization Typical physical systems follow deterministic behavior. This behavior can be sensitive to initial conditions, such that it is very difficult to predict their behavior in the longtime limit. The resulting motion is chaotic and looks stochastic or random...

Nonlinear Waves Seminar - Mahmoud I. Hussein

Feb. 26, 2019

Generalized dispersion relation predicts harmonic generation in strongly nonlinear systems In recent work, we have derived an exact nonlinear dispersion relation for elastic wave propagation in a thin rod (linearly nondispersive) and a thick rod (linearly dispersive). The derivation is generally applicable to any type of nonlinearity, geometric (related to...

Pages