Visitors

DIRAC Institute hosts visitors from across the world, for short and long term stay. If you are interested visiting the DIRAC, please contact us.

C U R R E N T    V I S I T O R S

Visitor
Jorge Vergara (University of Chile):  July 26th to October 26th.  Jorge is a machine learning expert who works on feature selection, identifying which features provide the most information in a Read More
Jorge VergaraVisitor

Jorge Vergara (University of Chile):  July 26th to October 26th. 

Jorge is a machine learning expert who works on feature selection, identifying which features provide the most information in a data set.

Jorge was born in Temuco, Chile. He obtained his B.S. and P.E. degrees in Electrical Engineering from the University de La Frontera in 2005 and 2007 respectively.

Jorge obtained his Ph.D. in electrical engineering from the University of Chile in 2015 where he worked on feature extraction and selection method based on information theory. Actually Jorge is a postdoctoral research at the University of Chile and the Millennium Institute of Astrophysics, where he works on selection and extraction of feature group based on mutual information for classification of patterns in astronomical images and time series.

Jorge’s scientific interests are interaction and causality in features selection method using information theoretic, quantization of nonlinear time series and hierarchical learning. The Jorge’s current work focuses in detection of candidate asteroid on stamp images in Moving Object Pipeline System on LSST and dimensionality reduction and feature-learning in astronomical spectroscopic data using Variational Autoencoder.

Working at the UW

In Jorge’s stay at the University of Washington, he works with Andrew Connolly’s team on two main topics:

1)  Study of new strategies to optimize the MOPS process in real time to detect asteroid. In this study they work with unsupervised analysis of images (stamp) and hierarchical classification where they create and select the most relevant individual and group features to discriminate between asteroids and non-asteroid.

2)  Feature Extraction from spectroscopic data using Variational Autoencoder. In this study they work on the dimensionality reduction in spectroscopic data using Variational autoencoder to reconstruct spectroscopic data with with different resolution and to map nonlinear similarities on spectra.

 

 

P A S T   V I S I T O R S

Visitor
Richard McMahon (University of Cambridge) : July – August 2017 Richard McMahon is heavily involved in DES and 4MOST and has been working recently on the detection of lensed QSOs. The main focus of Read More
Richard McMahonVisitor

Richard McMahon (University of Cambridge) : July – August 2017

Richard McMahon is heavily involved in DES and 4MOST and has been working recently on the detection of lensed QSOs.

The main focus of my current research is in the study of galaxy formation and evolution in the Epoch of Reionization ; focusing on the discovery and characterisation of high redshift primeval active galaxies andquasars powered by the accretion of matter onto supermassive black holes. My research work includes the discovery of quasars and active galaxies that host supermassive black holes, the determination of the space densities, star formation rates and how and when massive galaxies and quasars form.

This research is centered around the building and use of large scale data intensive techniques using optical and infra-red imaging and spectroscopic sensors on telescopes around the world (primarily in Chile) and in space using Gigapixel cameras and Petsacale multiwavelength datasets. 

 

Visitor
Tamas Budavari (John Hopkins University) : July 24th to 28th 2017. Tamas Budavari is Assistant Professor in the Department of Applied Mathematics & Statistics in the Whiting School of Engineering at the Read More
Tamas BudavariVisitor

Tamas Budavari (John Hopkins University) : July 24th to 28th 2017.

Tamas Budavari is Assistant Professor in the Department of Applied Mathematics & Statistics in the Whiting School of Engineering at the Johns Hopkins University, where he focuses on computational and statistical challenges of big data.

His contributions to astronomy include Bayesian cross-identification of catalogs, statistical inference of galaxy properties and clustering, as well as advanced data solutions for fast searches against the largest surveys and simulations.

 

Visitor
Francisco Förster (University of Chile): July 2017 Francisco Förster is the PI of the HITS program (a time domain survey using the Blanco 4m at CTIO that has been used for Read More
Francisco FörsterVisitor

Francisco Förster (University of Chile): July 2017

Francisco Förster is the PI of the HITS program (a time domain survey using the Blanco 4m at CTIO that has been used for supernova, asteroid, and variable star detection).