Seminars

Stats, Optimization, and Machine Learning Seminar - Bo Waggoner

Sept. 3, 2019

Bo Waggoner Department of Computer Science, University of Colorado Boulder Toward a Characterization of Loss Functions for Distribution Learning A common machine-learning task is to learn a probability distribution over a very large domain. Examples include natural language processing and generative adversarial networks. But how should the learned distribution be...

Math Bio Seminar - Harry Dudley

Sept. 3, 2019

Harry Dudley Department of Applied Mathematics, University of Colorado Boulder Model Selection & Bioelectrochemical Systems Microbial electrolysis cells (MECs) are devices that produce hydrogen from renewable organic matter, such as wastewater. These devices require less energy input than water electrolysis and have greater efficiency than fermentative hydrogen production. We present...

Applied Math Colloquium - Adrianna Gillman

Aug. 30, 2019

Adrianna Gillman Department of Applied Mathematics, University of Colorado Boulder An efficient and high order accurate direct solution technique for variable coefficient elliptic partial differential equations For many applications in science and engineering, the ability to efficiently and accurately approximate solutions to elliptic PDEs dictates what physical phenomena can be...

Department Colloquium - Marcel Nutz

April 26, 2019

Convergence to the Mean Field Game Limit: A Case Study Mean field games are used as approximations to n-player games with large n. Indeed, n-player Nash equilibria are known to converge to their mean field counterpart when the latter is unique. In this talk we study a specific stochastic game...

Nonlinear Waves Seminar - Igor Rumanov

April 23, 2019

Whitham theory and dispersive shock waves (DSWs) for the radial nonlinear Schroedinger (rNLS) equation. Dispersive shock waves of the defocusing rNLS equation in two spatial dimensions are studied. This equation arises naturally in Bose-Einstein condensates, water waves and nonlinear optics. A unified nonlinear WKB approach, equally applicable to integrable and...

Stats, Optimization, and Machine Learning Seminar - Sidney D'Mello

April 23, 2019

Project Tesserae: Longitudinal Multimodal Modeling of Individuals in Naturalistic Contexts I will describe our team’s efforts on a two-year Intelligence Advanced Research Projects Activity (IARPA) program called MOSAIC - Multimodal Objective Sensing to Assess Individuals with Context. The program’s ambitious aims are to “advance multimodal sensing to measure personnel and...

Department Colloquium - Yu Du and Fred Glover

April 19, 2019

QUBO Models in Optimization, Machine Learning, and Quantum Computing The Quadratic Unconstrained Binary Optimization (QUBO) model has gained prominence in recent years with the discovery that it unifies a rich variety of combinatorial optimization problems. By its association with the Ising problem in physics, the QUBO model has emerged as...

Stats, Optimization, and Machine Learning Seminar - John Pearson

April 16, 2019

Modeling Real Behavior in Two-Person Differential Games In the behavioral sciences, games and game theory have long been the tools of choice for studying strategic behavior. However, the most commonly studied games involve only small numbers of discrete choices and well-defined rounds, while real-world strategic behaviors are continuous and extended...

Department Colloquium - Jianfeng Zhang

April 12, 2019

Set Values for Nonzero Sum Games With Multiple Equilibriums Nonzero sum games typically have multiple Nash equilibriums (or no equilibriums), and unlike zero sum games, they may have different values at different equilibriums. While most works in the literature focus on the existence of individual equilibriums, we propose instead to...

Nonlinear Waves Seminar - Mingzhong Wu

April 9, 2019

Experimental Observation of Spin-Wave Fractals A fractal is a shape made of parts each of which is similar to the whole in some way. One can group fractals into two main categories, (i) exact fractals in which the same feature replicates itself on successively smaller scales and (ii) statistical fractals...

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