DIRAC Institute hosts visitors from across the world, for short and long term stay. If you are interested visiting the DIRAC Institute, please contact us.
C U R R E N T V I S I T O R S
October 22-23, 2018.
Katy is a Ph.D. candidate in the Department of Statistics at Harvard University. She does research as a member of both the CHASC: Astrostatistics Group and the Sports Analytics Lab at Harvard. Her Tuesday 12pm talk will be on Defining Regions that Contain Complex Astronomical Structures.
U P C O M I N G V I S I T O R S
November 29-30, 2018.
I’m interested in galaxies: how did they form and end up looking the way they do today? There are lots of different ways to attack this problem: my interest is in observational studies of both nearby galaxies and their star clusters and also distant, high-redshift galaxies. I’ve used quite a few different telescopes and instruments, with a lot of emphasis on infrared observations with the Spitzer Space Telescope.
April 12, 2019.
David A van Dyk is currently a Chair Professor in the Statistics Section of the Department of Mathematics at Imperial College London. He received his Ph.D. from the Department of Statistics at the University of Chicago and his B.S. in Statistics and Probability from Michigan State University. Professor van Dyk was elected Fellow in the American Statistical Association in 2006. His research focuses on Statistical Computation, Statistical Methods in Astronomy, Causal Inference, and Statistical Analysis with Missing Data.
P A S T V I S I T O R S
June 27-29, 2018.
I work on Galactic Archaeology, which is the study of the structure and evolution of the Milky Way. I did my PhD at New Mexico State University working with Jon Holtzman and Jo Bovy with the APOGEE survey, which is an SDSS3 and SDSS4 spectroscopic survey of several hundred thousand red giants across the Milky Way. I spent two years working in Nice at the Observatoire de la Cote d’Azur with the Gaia-ESO and AMBRE projects, which have spectroscopic observations of tens of thousands of stars throughout the Galaxy.
Currently, I am an ASTRO 3D fellow working at the University of Sydney with the GALAH Survey. GALAH is a large-scale spectroscopic survey of 500,000 stars throughout the Milky Way, and aims to measure chemical abundances for 30 different elements to map and chemically tag the stellar populations of the Galaxy. In my talk, “Chemical Cartography of the Milky Way”, I will discuss the chemodynamic properties of different stellar populations throughout the disk and how they vary with location.
I attached a figure of some preliminary results from the GALAH survey, showing how the chemical structure of the disk varies from the inner Galaxy to the edge of the disk.
February 20, 2018.
Jean-Charles Cuillandre is an astronomer at the Canada-France-Hawaii Telescope (from Observatoire de Paris).
His talk is scheduled for Tuesday, February 20th. Title: The challenge of the distributed Euclid survey
February 1-2, 2018.
I am currently an associate professor in the Astronomy Group in the Physics and Astronomy Department at Vanderbilt University. My research interests lie in the areas of large-scale structure and galaxy formation, as well as ultra-high energy cosmic rays. I completed my Ph.D. degree in Astronomy at the Ohio State University, and my A.B. degree inAstrophysical Sciences at Princeton University. Before that, I lived in Athens, Greece where I attended Athens College.
February 14-16, 2018.
Eddie is a Hubble Fellow working at Lawrence Berkeley National Laboratory, trying to understand the structure of the Galaxy, and especially its dust, using observations of stars. His most recent work uses the DECam instrument on the Blanco 4-m telescope to image the southern Galactic plane, to understand its stars, gas, and dust. He also uses APOGEE spectroscopy to understand how dust properties vary, and the PS1 survey to infer the three-dimensional structure of the dust in the Milky Way.
February 12-15, 2018.
Naoki Yoshida is a Professor of Astrophysics in the Department of Physics at the University of Tokyo, as well as a Senior Research Scientist at the Kavli Institute for the Physics and Mathematics of the Universe.
My general interest in statistical inference and dynamics has also led me to projects on binary stars, supermassive black holes, and calibration of the distance ladder.
December 11th, 2017.Concurrently, AURA-LSST is excited to roll out a training video platform called Lynda.com. We were pleased to find out that University of Washington faculty and staff has access to Lynda.com. Chris will partner with the proper channels to ensure all LSST Project Members know how to access this resource so we can share videos within teams and subsystems.
December 11th – 12th, 2017.
When: December 11, 2017 @1:30-2:30pm
Where: WRF Data Science Studio, The Seminar Room
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).
July 24th -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.
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.
July 26th – October 26th, 2017.
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.