Last Updated: 20180820

AAS 233: Special Session

Machine Learning in Astronomical Data Analysis

Monday, 7 January 2019

2:00pm - 3:30pm PST
Washington State Convention & Trade Center, Seattle WA

hea-www.harvard.edu/AstroStat/aas233/special.html
| Description | Schedule | Posters | Contacts | changelog |

Description

Machine Learning is quickly becoming a popular method to analyze astronomical data. There is a great deal of interest among the astronomical community in the powerful techniques that are now being developed, with every session, workshop, or seminar relating to the subject having overflow audiences.

We are therefore organizing a ML-oriented special session at AAS 233. The goal of this session is to focus attention on new ML applications specific for astronomical data. Under the principle that it is better to learn with concrete examples, we seek to provide a forum for reporting on new applications and enhancements in existing methodologies. Modern telescopes collect a large amount of data, freely accessible via archives, to all scientists. With big datasets, come big opportunities. The SDSS, Kepler, and K2 datasets, the recently released Gaia DR2, the forthcoming LSST in the optical, ALMA, MWA, and SKA in the radio, SDO in the EUV, are perfect illustrations of the power of data to unlock new science. This session is designed to help us prepare to take advantage of these opportunities, by making astronomers aware of both the promise of ML and to understand its limitations.

Beyond astronomy, ML has many applications in science and a wide range of other fields. The skills developed by astronomers as they investigate and implement ML techniques will also serve them in cross-disciplinary endeavours, and will be an excellent way for Astro grad students to enhance their skill sets for non-astronomy career paths.

Our session will consist of a talk by Mario Juric (DIRAC Institute), focused on transient filtering and on knowledge discovery to unearth interesting science, for example, in data to be collected by LSST, followed by a talk by James Davenport (DIRAC Institute) focused on the practical aspects of learning and applying ML concepts to Kepler and Gaia data. This will be followed by up to four contributed talks (10 minutes each) which we will select from the submitted abstracts.


Schedule

Chair: V. Kashyap (CfA)


TIME: Mario Juric (DIRAC Institute, Univ. of Washington)

Transient Filtering and Knowledge Discovery

Abstract: TBD

Notes:-
 

TIME: James Davenport (Univ. of Washington)

Practical ML

Abstract: TBD

Notes:-
 

Poster Session NUMBER TBD

Poster presentations accepted for the session will be listed here.

Contacts

Rosanne Di Stefano (rdistefano @ cfa . harvard . edu)
Vinay Kashyap (vkashyap @ cfa . harvard . edu)
Aneta Siemiginowska (asiemiginowska @ cfa . harvard . edu)

changelog

2018-jul-05: started page.
2018-aug-06: Date and time set.
2018-aug-20: Time corrected.
2018-aug-23: changed URL, in anticipation of there also being a splinter session



CfA / CHASC / AAS233

Machine Learning in Astronomical Data Analysis
7 Jan 2019
2pm-3pm PST
VENUE: TBD

Description
Schedule
Mario Juric
James Davenport
Posters
Contacts
changelog




CfA / CHASC