# Seminars

Seminars appear in decreasing order in relation to date. To find an activity of your interest just go down on the list. Normally seminars are given in english. If not, they will be marked as Spanish Only.

## “DASH: Deep Learning for the Automated Spectral Classification of Supernovae”

Event Date: Jan 27, 2017 in Other Areas, Seminars

ABSTRACT:   We have reached a new era of ‘big data’ in astronomy with surveys now recording an unprecedented number of spectra. In particular, new telescopes such as LSST will soon incease the spectral catalogue by a few orders of magnitude. Moreover, the Australian sector of the Dark Energy Survey (DES) is currently in the process of spectroscopically measuring several thousands of supernovae. To meet this new demand, novel approaches that are able to automate and speed up the classification process of these spectra is...

## Yaglom limits can depend on the initial state

Event Date: Jan 16, 2017 in Seminars, Stochastic Modeling

Abstract:   To quote the economist John Maynard Keynes: “The long run is a misleading guide to current affairs. In the long run we are all dead.” It makes more sense to study the state of an evanescent system given it has not yet expired. For a substochastic Markov chain with kernel K on a state space S with killing this amounts to the study of of the Yaglom limit; that is the limiting probability the state at time n is y given the chain has not been absorbed; i.e. lim_{n\to\infty}K^n(x,y)/K^n(x,S).   We given an example...

## Provably efficient high dimensional feature extraction

Event Date: Dec 28, 2016 in Discrete Mathematics, Seminars

Abstract: The goal of inference is to extract information from data. A basic building block in high dimensional inference is feature extraction, that is, to compute functionals of given data that represent it in a way that highlights some underlying structure. For example, Principal Component Analysis is an algorithm that finds a basis to represent data that highlights the property of data being close to a low-dimensional subspace. A fundamental challenge in high dimensional inference is the design of algorithms that are provably efficient...

## Provably efficient high dimensional feature extraction

Event Date: Dec 28, 2016 in Optimization and Equilibrium, Seminars

Abstract: The goal of inference is to extract information from data. A basic building block in high dimensional inference is feature extraction, that is, to compute functionals of given data that represent it in a way that highlights some underlying structure. For example, Principal Component Analysis is an algorithm that finds a basis to represent data that highlights the property of data being close to a low-dimensional subspace. A fundamental challenge in high dimensional inference is the design of algorithms that are provably efficient...

## The group of reversible Turing machines and the torsion problem for $\Aut(A^{\mathbb{Z})$ and related topological fullgroups.

Event Date: Dec 19, 2016 in Dynamical Systems, Seminars

Abstract:   We introduce the group $RTM(G,n,k)$ composed of abstract Turing machines which use the group $G$ as a tape, use an alphabet of $n$ symbols, $k$ states and act as a bijection on the set of configurations. These objects can be represented both as cellular automata and in terms of continous functions and cocycles. The study of this group structure yields interesting results concerning computability properties of some well studied groups such as $\Aut(A^{\mathbb{Z})$ and the topological full group of the two dimensional full shift....

## Iterative regularization via a dual diagonal descent method

Event Date: Dec 14, 2016 in Optimization and Equilibrium, Seminars

Abstract: In the context of linear inverse problems, we propose and study general iterative regularization method allowing to consider classes of regularizers and data-fit terms. The algorithm we propose is based on a primal-dual diagonal descent method, designed to solve hierarchical optimization problems. Our analysis establishes convergence as well as stability results, in presence of error in the data. In this noisy case, the number of iterations is shown to act as a regularization parameter, which makes our algorithm an iterative...

## Seguimiento docente e implementación de recursos educativos de Comunidad inGenio en el aula

Event Date: Dec 13, 2016 in Education, Seminars

Resumen: La exposición abordará el proyecto “Seguimiento Docente”, el cual consiste en una serie de actividades y trabajo de capacitación con diversos profesores de matemática de enseñanza media en la R.M. y Biobío. Este proyecto es desarrollado por Comunidad InGenio, programa de divulgación y educación del Instituto Sistemas Complejos de Ingeniería (ISCI). Con más de 8 años de experiencia, Comunidad InGenio cuenta con una variedad de recursos educativos basados en la investigación científica que el ISCI desarrolla. De esta manera, y a través...

## Propagation of critical behavior for unitary invariant plus GUE random matrices

Event Date: Nov 24, 2016 in Núcleo Modelos Estocásticos de Sistemas Complejos y Desordenados, Seminars

Abstract: It is a well known and celebrated fact that the eigenvalues of random Hermitian matrices from a unitary invariant ensemble form a determinantal point process with correlation kernel given in terms of a system of orthogonal polynomials on the real line. It is a much more recent result that the eigenvalues of the sum of such a random matrix with a matrix from the Gaussian unitary ensemble (GUE) also forms a determinantal point process, with the kernel given in terms of the Weierstrass transform of the original kernel. I’ll talk about...

## Does convexity arise in optimization naturally?

Event Date: Nov 16, 2016 in Optimization and Equilibrium, Seminars

Abstract Convexity is one of the conditions that any researcher may desire to have when dealing with problems in Optimization. Thus, the lack of standard convexity provides an interesting challenge in mathematics. In this talk we show various instances from mathematical programming, differential inclusions to calculus of variations, where convexity is present in one way or in another. Among the issues to be described lie: strong duality, KKT optimality conditions; joint-range and the S-lemma for a pair of (not necessarily homogeneous)...

## Relación de las mujeres con las matemáticas: La necesidad de un modelo multinivel y múltiples métodos de investigación.

Event Date: Nov 15, 2016 in Education, Seminars

Resumen: La relación ‘complicada’ de las mujeres con las matemáticas ha sido un área de profunda investigación ya por mas de 40 años. En esta presentación recorreremos los principales modelos teóricos que han sido utilizados para comprender esta situación y revisaremos una propuesta integrativa que pone al sujeto (y sus identidades – particularmente su ‘identidad matemática’) en el centro de la indagación. Usando este modelo exploraremos algunas preguntas de mi tesis doctoral: ¿Qué tan grande son las diferencias de rendimiento? ¿Existe una...