Past SCAIM Seminars

Tue, 2017-10-17 13:30 - 14:30
Bamdad Hosseini, Department of Mathematics, Simon Fraser University
Statistical and probabilistic methods are promising approaches to solving inverse problems – the process of recovering unknown parameters from indirect measurements. Of these, the Bayesian methods provide a principled approach to incorporating our existing beliefs about the parameters (the prior model) and randomness in the data. These approaches are at the forefront of extensive current investigation. Overwhelmingly, Gaussian prior models are used in Bayesian inverse problems since they provide mathematically simple and computationally efficient formulations of important inverse problems.
Tue, 2017-10-03 12:30 - 13:30
Eldad Haber, Department of Mathematics and Earth and Ocean Science, UBC
In this talk we will explore deep neural networks from a dynamical systems point of view. We will show that the learning problem can be cast as a path planning problem with PDE constraint. This opens the door to conventional Computational techniques that can speed up the learning process and avoid some of the local minima.
Tue, 2017-09-19 12:30 - 13:30
Uri Ascher, Department of Computer Science, The University of British Columbia
Visual computing is a wide area that includes computer graphics and image processing, where the ``eyeball-norm'' rules. I will briefly discuss two case studies involving numerical methods and analysis applied to this area. The first involves motion simulation and calibration of soft objects such as cloth, plants and skin. The governing elastodynamics PDE system, discretized in space already at the variational level using co-rotated FEM, leads to a large, expensive to assemble, dynamical system in time, where the damped motion may mask highly oscillatory stiffness.
Tue, 2017-08-29 12:30 - 13:30
Michael Overton, Courant Institute of Mathematical Sciences, New York University
In many applications one wishes to minimize an objective function that is not convex and is not differentiable at its minimizers. We discuss two algorithms for minimization of nonsmooth, nonconvex functions. Gradient Sampling is a simple method that, although computationally intensive, has a nice convergence theory. The method is robust and the convergence theory has recently been extended to constrained problems. BFGS is a well known method, developed for smooth problems, but which is remarkably effective for nonsmooth problems too.
Tue, 2017-06-13 12:30 - 14:00
Joshua Scurll, Department of Mathematics, The University of British Columbia
With super-resolution microscopy techniques such as Direct Stochastic Optical Reconstruction Microscopy (dSTORM), it is possible to image fluorescently labeled proteins on a cell membrane with high precision. Often, the extent to which such proteins cluster is biologically meaningful; for example, in B-cells, clustering of the B-cell receptor (BCR) is associated with increased intracellular signaling and B-cell activation, and spontaneous BCR clustering can cause chronic active BCR signaling that results in an aggressive B-cell malignancy.
Tue, 2017-05-30 12:30 - 14:00
Ailyn Stötzner, Faculty of Mathematics, TU Chemnitz
Elastoplastic deformations play a tremendous role in industrial forming. Many of these processes happen at non-isothermal conditions.Therefore, the optimization of such problems is of interest not only mathematically but also for applications. In this talk we will present the analysis of the existence of a global solution of an optimal control problem governed by a thermovisco(elasto)plastic model. We will point out the difficulties arising from the nonlinear coupling of the heat equation with the mechanical part of the model.
Tue, 2017-04-25 12:30 - 14:00
Paul Tupper, Mathematics, SFU
One important construction in the theory of metric spaces is the tight span. The tight span of a metric space can be thought of as a generalization of the idea of a convex hull in linear spaces and is the basis for much work in the study and visualization of finite metric spaces. Motivated by problems in phylogenetics, we have developed a generalization of the concept of metric spaces, which we call diversities. In a diversity, every subset of points in the space corresponds to a number, not just pairs, and there is a more general version of the triangle inequality.
Tue, 2017-04-04 12:30 - 14:00
Rongrong Wang, UBC Mathematics
In this talk, we examine two methods for frequency extrapolation. Frequency extrapolation is the problem of utilizing data processing techniques to obtain the entire spectrum of an objective signal while only a middle band is sampled. This problem is well-posed for signals with special structures, such as those with a few non-zeros. The study is motivated by seismic inversion. Due to physical constraints, data obtained from a seismic survey is severely limiting in both the low and high frequency extent for the purposes of inversion.
Tue, 2017-03-28 12:30 - 14:00
Marie Graff, Department of Earth, Ocean and Atmospheric Sciences, The University of British Columbia
A nonlinear optimization method is proposed for the solution of inverse scattering problems in the frequency domain, when the scattered field is governed by the Helmholtz equation. The time-harmonic inverse medium problem is formulated as a PDE-constrained optimization problem and solved by an inexact truncated Newton-type iteration. Instead of a grid-based discrete representation, the unknown wave speed is projected to a particular finite-dimensional basis of eigenfunctions, which is iteratively adapted during the optimization.
Tue, 2017-03-21 12:30 - 14:00
Curt Da Silva, Seismic Laboratory for Imaging and Modelling, The University of British Columbia
Many useful and interesting optimization problems can be cast in a convex composite form min_x h(c(x)), where h is a non-smooth but convex function and c is a smooth nonlinear or linear mapping. The non-smoothness of the outer function prevents traditional methods such as the Gauss-Newton method from converging quickly, which is problematic for large scale problems. In this talk, we will explore level set methods, aka the SPGL1 'trick', for solving this class of problem when we can easily project on to the level sets of h(z).
Templates provided by UBC Department of Physics & Astronomy

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