Seminars

Yury Makarychev Toyota Technological Institute at Chicago (TTIC) Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering Consider an instance of Euclidean k-means or k-medians clustering. We show that the cost of the optimal solution is preserved up to a factor of (1+ε) under a projection onto a random O(log(k/ε)/ε^2)-dimensional...

Erin Ellefsen Department of Applied Mathematics University of Colorado Boulder Efficiently finding steady states of nonlocal territorial models in Ecology There are certain populations of animals that tend to move in social groups. We investigate territory development of these social groups by studying a system of non-local continuum equations. However,...

Rebecca Morrison Department of Computer Science, University of Colorado Boulder Representing model error in reduced models of interacting systems In many applications of interacting systems, we are only interested in the dynamic behavior of a subset of all possible active species. For example, this is true in combustion models (many...

Catherine Sulem Department of Mathematics, University of Toronto Bloch theory and spectral gaps for linearized water waves We consider the movement of a free surface of a two-dimensional fluid over a variable bottom. We assume that the bottom has a periodic prole and we study the water wave system linearized...

Zhishen Huang Department of Applied Mathematics, University of Colorado Boulder Finding local minimizers in nonconvex and non-smooth optimization We consider the problem of finding local minimizers in nonconvex and non-smooth optimization. The objective function we consider is in the form of the sum of a nonconvex function and a l1-penalty...

John Harlim Departments of Mathematics and Meteorology, Penn State University Manifold learning based computational methods Recent success of machine learning has drawn tremendous interests in applied mathematics and scientific computations. In this talk, I will discuss recent efforts in using manifold learning algorithms (a branch of machine learning) to do...

Brett Werner Department of Mathematics, University of Colorado Boulder On Machine Learning Machine learning (ML) is the process of making computers learn without specifically programming them to do so. ML has a variety of applications including predicting revenue, detecting credit card fraud, and self-driving cars. We will begin by discussing...

Osita Onyejekwe Department of Mathematics, University of Colorado Boulder Feature Detection in Observed Climate Factors The detection of inflection points is an important task in science and engineering. This task is onerous with subjective results. Signals are often corrupted with noise and signal denoising is often required before feature extraction,...

Ashutosh Trivedi Department of Computer Science, University of Colorado Boulder Reinforcement Learning and Formal Requirements Reinforcement learning is an approach to controller synthesis where agents rely on reward signals to choose actions in order to satisfy the requirements implicit in reward signals. Oftentimes non-experts have to come up with the...

Jacqui Wentz Department of Applied Mathematics, University of Colorado Boulder Singular value decomposition of the reaction-diffusion stoichiometry matrix Flux balance analysis (FBA) is a mathematical technique used to study biochemical networks. In contrast to other methods, FBA requires limited information about kinetic parameters and metabolite concentrations. Previously, we developed a...