The DIRAC Institute in the Department of Astronomy at the University of Washington is seeking applicants with a strong research record in the development of statistical techniques or algorithms for analyzing large astrophysical data sets for two postdoctoral positions.
AstroML: The first position is to help in the development of the second edition of astroML (http://astroml.org) a popular Python-based machine learning package for astrophysics. New components we are incorporating within astroML include methodologies from deep learning and hierarchical bayesian statistics. Special emphasis will be placed on building a broader community and making astroML a sustainable open-source project. The successful candidate will lead these activities, including the application of the new codes to dataset available to UW researchers.
Time Series Data: The second position is to develop new approaches for analyzing astronomical time series data using modern computational frameworks. The goal of this framework will be to enable science with the ZTF and LSST data sets. Promising applicants should possess an interest in time domain science and experience or interest in the use of databases and large scale compute platforms such as Spark, Dask, or similar. Good Python skills, and experience with machine learning libraries, image processing of astronomical images, or astronomical databases are desirable.
The DIRAC Institute is a newly formed center for data intensive astrophysics at the University of Washington. The Institute consists of six faculty and senior fellows, and over 20 postdoctoral researchers and research scientists. It has active research programs in Cosmology, Solar System science, Milky-Way structure, the Variable and Transient universe, andAstronomical Software.
The University of Washington is a partner in the Zwicky Transient Facility (ZTF) project, a new time-domain survey which will begin operations in early 2018. The UW is a founding partner of the LSST project, and leads the construction of its time domain and solar system processing pipelines. Other research activities at UW/DIRAC include topics in extragalactic science, as well as the understanding the structure, formation, and evolution of the Milky Way using large surveys (SDSS, WISE, PanSTARRS PS1, and others).
A Ph.D. degree in astronomy, physics, computer science, or a related subject is required. The initial appointment is for two years, renewable up to three years, and offers competitive salary and benefits. The appointments are available immediately and are expected to start no later than September 2018.
Applicants should submit a curriculum vitae, description of research interests (with links to Github if relevant) and arrange for three letters of reference to be submitted to Nikolina Horvat at firstname.lastname@example.org with subject line “DIRAC postdoc application (your name)”. Applications will be accepted until the positions are filled, to assure full consideration, please send your application by Dec 31st 2017
For detailed information about the benefits available through the University of Washington, including dental, medical and disability insurance, retirement, and childcare centers, see the University of Washington benefits page: https://www.washington.edu/admin/hr/benefits/.
The DIRAC Institute is a community of people with diverse interests and areas of expertise, engaged in the understanding of our universe through the analysis of large and complex data sets. We are an open, ethical, highly engaged and collaborative community based on trust, transparency and mutual respect. We believe in providing a welcoming and inclusive environment, in the importance of quality of life, in embracing diversity, in making a difference and having fun.
When: October, 14, 2019 @ 12:30pm Where: PAB, 6th Floor, eScience Studio, Seminar Rm.
The upcoming generation of wide-field optical surveys which includes LSST will aim to shed some much needed light on the physical nature of dark energy and dark matter by mapping the Universe in great detail and on an unprecedented scale. However, with the increase in data quality also comes a significant increase in data complexity, bringing new and outstanding challenges at all levels of the scientific analysis. In this talk, I will illustrate how deep generative models, combined with physical modeling, can be used to address some of these challenges at the image processing level, specifically by providing data-driven priors of galaxy morphology.
I will first describe how to build such generative models from corrupted and heterogeneous data, i.e. when the training set contains varying observing conditions (in terms of noise, seeing, or even instruments). Once trained, sampling from these models produces realistic galaxy light profiles, which can then be used in survey emulation, for the purpose of validating and/or calibrating data reduction pipelines.
Even more interestingly, these models can be seen as priors on galaxy morphologies and used as such as part of standard Bayesian inference techniques to solve astronomical inverse problems ranging from deconvolution to deblending galaxy images. I will present how combining these deep morphology priors with a physical forward model of observed blended scenes allows us to address the deblending problem in a physically motivated and interpretable way.
I am currently a postdoctoral follow at the Berkeley Center for Cosmological Physics (BCCP) and with the Foundation of Data Analysis (FODA) institute at UC Berkeley, where I conduct my research at the intersection between cosmology and machine learning.
Previously, I was a postdoctoral researcher in the McWilliams Center for Cosmology at Carnegie Mellon University, where I was working with Prof. Rachel Mandelbaum on weak gravitational lensing measurements and systematics, and also interacted with both Statistics and Machine Learning departments here at CMU.
I did my PhD in the CosmoStat laboratory of CEA Saclay near Paris, France, under the supervision of Jean-Luc Starck. My PhD work focused on the application of sparse regularization techniques to solve ill-posed inverse problems in a cosmological context.
Before that, I received a Master’s degree in fundamental physics from Paris-Sud University as well as a Master’s degree in fundamental and applied mathematics from Paul Verlaine University in Metz (France). I am also a Supélec engineer, from one of France’s top grandes écoles, where I specialized in Robotics and Interactive Systems.
You can find my academic CV here.
DIRAC Seminars are appropriate for students and researchers at all levels who are interested in astronomy and/or data science, and everyone is welcome to attend. They are not typically streamed or recorded.
Where: PAB, 6th Floor, eScience Studio, Seminar Rm. Time: 12:30pm | Seminar dates are listed below
Talk titles, abstracts, opportunities to meet with speakers, and information about lunch will be publicized on the UW Astronomy “astro-all” email list and the DIRAC Slack the week before each talk. Most speakers will visit DIRAC for a day or two and be available for meetings following their talk. For more information, you can contact DIRAC Visitor Committee Chair Stephen Portillo at email@example.com.
NOV 12 | Ashley Villar | Harvard | machine learning and transient light curves
DEC 19 | Adam Smercina | U Michigan | galaxy formation and detecting faint diffuse structure
JAN 13 | Michael Medford | UC Berkeley | IMBH and Planet Nine searches with ZTF
FEB 10 | Stephanie Juneau | NOAO | galaxy and SMBH evolution using large surveys
MAR 9 | Angus Wright | U Bonn | redshift calibration and galaxy clustering estimation
APR 13 | Bruce Conway | U Illinois Urbana-Champaign | astrodynamics and heuristic optimation
MAY 11 | Abigail Stevens | Michigan State U / high energy observations and time series
JUN 8 | Federica Bianco | U Delaware | LSST science collaborations chair and transients and variable stars science committee chair
When: September 24, 2019 @ 12:00pm
Where: PAB, 3rd floor, B305
Classification in Astronomy using ML.
Dr. Sharma is a postdoc at the Inter University Centre for Astronomy and Astrophysics in Pune, India. His Tuesday noon talk will be on “Classification in Astronomy using ML.” In his research, he applies machine learning and deep learning to the classification of stellar spectra and LMXB X-ray spectra.
Abstract: The huge data volume generated from the ongoing and upcoming astronomical survey programs has pushed the astronomers to shift from the traditional methods to more sophisticated and scalable approaches for data reduction and analysis without compromising with the precision and accuracy of the results. To address these concerns, Machine Learning (ML) algorithms have been considered in Astronomy, like other domains, and have been employed successfully for a wide range of astronomical problems in the last couple of decades. Classifying astronomical objects is one such field and in this talk, we will consider:
(1) stellar spectral classification using machine learning (ML) and deep learning (DL) techniques like ANN, RF, and CNN based on their spectra in the optical region of EM spectrum. We show that using CNNs, we are able to lower the RMS error up to 1.23 spectral sub-classes and apply the final model on stellar spectra from the SDSS.
(2) classification of X-ray spectra of Low Mass X-ray Binary (LMXB) systems which consist of a main-sequence star and a compact object that could be a Black Hole (BH) or a Neutron Star (NS). Using random forest (RF) algorithm, we are able to identify the type of compact object in the LMXB system (BH or NS) by feeding the X-ray energy spectrum to the model with an average accuracy of 87+/-13%.
Modern sky surveys are producing astronomical catalogs with billions of stars and galaxies. What is often important for science is cross-correlating these catalogs and finding the matching objects in several catalogs so that new insights can be gained from all observations at once. This operation, commonly known as ‘cross-matching’, can be extremely computationaly expensive because of the large number of comparisons that need to be performed.
DIRAC team has designed and implemented a system called AXS, or Astronomical Extensions for Spark, that comprises a new cross-matching approach that significantly outperforms other such systems and is capable of cross-matching multi-billion catalogs in tens of seconds on commodity hardware. AXS also contains other functionalities useful to astronomers and is based on Apache Spark, an industry-standard, open-sourced, distributed data processing system.”
During the last week of June, Asteroid Day celebrated their Fifth Anniversary in 2019, with events in 192 countries, and once again broadcasted their six-hour Asteroid Day LIVE TV show from Luxembourg, hosting innovators, astronauts, planetary scientists, celebrities and asteroid experts.
Scientists from the DiRAC Institute and LSST, Dr. Lynne Jones and Prof. Mario Jurić, were among the panel guests. The program featured two short films about team’s work and current research. “2017 The LSST – Making Census of the Solar System” and “2019 New Era of Cosmic Discovery“.