DECEMBER 9-13, 2019, RINGBERG CASTLE

Machine Learning Tools for Research in Astronomy



Harnessing the tools of Machine Learning methods for analysis and discovery in observations and simulations

Scientific Organizing Committee

Annalisa Pillepich (MPIA)
Gianfranco Bertone (U. of Amsterdam)
David W Hogg (NYU, Flatiron Inst.)
Kai Polsterer (HITS)
Michelle Ntampaka (Harvard)
Glenn van de Ven (U. of Vienna)

Local Organizing Committee

Elad Zinger
(+49|0) 6221 528-221


Email the LOC

ml2019@mpia.de

CONFERENCE PHOTO

Participants

Camille Avestruz (U. of Michigan)
Dalya Baron (TAU)
Vincent Boucher (B12 Consulting)
Massimo Brescia (INAF/Napoli)
Tobias Buck (AIP)
Tom Charnock (IAP)
Gabriella Contardo (CCA)
Erin Cram (Northeastern U.)
Joachim Denzler (Jena U.)
Lukas Eisert (MPIA)
Jesus Falcon Barroso (IAC)
Doug Finkbeiner (Harvard)
Morgan Fouesneau (MPIA)
Neige Frankel (MPIA)
Timothy Gebhard (MPI Intelligente Systeme)
Nikos Gianniotis (HITS, Heidelberg)
Andy Goulding (Princeton)
Gregory Green (MPIA)
David W. Hogg (NYU; CCA)
Marc Huertas-Company (Paris Obs./U. Paris Diderot.)
Prashin Jethwa (U. of Vienna)
Nikolay Kacharov (MPIA)
Tomasz Kacprzak (ETH Zurich)
Wolfgang Kerzendorf (NYU/Michigan State)
Mikhail Kovalev (MPIA)
Victor Ksoll (U. of Heidelberg)
Francois Lanusse (UC Berkely)
Luisa Lucie-Smith (UCL)
Peter Melchior (Princeton)
Brice Menard (Johns Hopkins U.)
Allison Merritt (MPIA)
Brian Nord (Fermilab)
Michelle Ntampaka (Harvard)
Stella Offner (U. of Texas)
Mario Pasquato (INAF Padova)
Josh Peek (Space Telescope Institute)
Laurence Perreault Levasseur (U. of Montreal)
Annalisa Pillepich (MPIA)
Kai Polsterer (HITS, Heidelberg)
Stephen Portillo (U. of Washington)
Dov Poznanski (TAU)
Nesar Ramachandra (Argonne Labs)
Nima Sedaghat (ESO)
Glenn van de Ven (U. of Vienna)
Soledad Villar (NYU; CDS)
Ashley Villar (Harvard)
Yuan-Sen Ting (IAS; ANU)
Elad Zinger (MPIA)

SCIENTIFIC RATIONALE

Machine learning methods can reveal the relations between observational data and the properties of the observed objects. They simultaneously have the potential to complement and inform the build up of physical models, provide data-driven predictions for practical applications, and automatize the analysis and comparisons of large data sets (real or simulated).

A number of machine learning tools have already been proven successful in a variety of astrophysical contexts, e.g. from the measurement of galaxy morphologies to automatic object detection and identification (e.g. quasars in the early Universe or RR Lyrae stars around the Milky Way), streamlined estimation of galaxies’ redshifts, and image analysis techniques for de-noising and deconvolution. In the upcoming years efficient machine learning and image analysis algorithms will become of the essences to fully exploit the available data and make substantial progress in our understanding of astrophysical phenomena.

We are therefore planning a hands-on and interactive workshop to foster big-data and machine-learning proficiency in the astronomy community, with particular focus on applications, among others, pertaining extra-galactic and galactic astronomy, galaxy formation, large scale structure, cosmology and cosmological surveys. Tools will range from classical regression and classification methods to state-of-the-art deep learning algorithms as generative adversarial networks and variational autoencoders. We plan to invite to participate also a few representatives of big and small private companies, in order to favor the percolation of their know-how expertise to the astronomy community.



The meeting will focus on the following subjects:

  • Obtaining an overview of the current landscape of ML applications in astronomy
  • Facilitating an interface between data science-oriented astronomers, astrophysicists and cosmologists with every-day practitioners and researchers in machine learning techniques.
  • Enhancing the sophistication and rigor of the analyses and discovery potential of astronomical data, both from observations and computer simulations.
  • Introducing input and inspirations from applications of ML in other fields
  • PROGRAM & RESOURCES

    Workshop Venue

    Schloss Ringberg

    The workshop will be held in the Schloss Ringberg, overlooking the Tegernsee Lake, in Southern Bavaria. Lodging and meals will be provided at the castle as well. For more information on the castle see here.

    Meals and Lodging

    All participants will be lodged at the Castle for the duration of the program.

    Breakfast, Lunch, and Dinner will all be provided at the Castle as well. The first meal will be a light dinner on Sunday evening. The last meal served will be Lunch on Friday, just before departure. Vegetarian options will be possible as well.

    Schloss_Ringberg
    Schloss_Ringberg

    Code of Conduct

    The SOC and LOC are committed to creating an environment that is safe, professional and of mutual trust where diversity and inclusion are valued, and where everyone is entitled to be treated with courtesy and respect. As conference organisers we are committed to making this workshop, and all associated activities productive and enjoyable for everyone. We will not tolerate harassment of participants in any form.

    Please follow these guidelines:

  • Behave professionally. Harassment and sexist, racist, or exclusionary comments or jokes are not appropriate. Harassment includes sustained disruption of talks or other events, inappropriate physical contact, sexual attention or innuendo, deliberate intimidation, stalking, and photography or recording of an individual without consent. It also includes offensive comments related to individual characteristics, for example: age, gender, sexual orientation, disability, physical appearance, race, nationality or religion.
  • All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual or sexist language and imagery is not appropriate.
  • Be respectful and do not insult or put down other attendees or facilitators of the event. Critique ideas not people.
  • Should a participant witness events of bullying, harassment or aggression, we recommend that they approach the affected person to show support and check how they are. The witness may also wish to suggest that the person report the inappropriate behaviour. However, it is up to the affected person alone whether or not they wish to report it.
  • If participants wish to share photos of a speaker on social media, we strongly recommend that they first get the speaker’s permission. Participants may also share the contents of talks/slides via social media unless speakers have asked that specific details/slides not be shared.
  • The designated contact points for any issue relating to this Code of Conduct, from the SOC and LOC are Annalisa Pillepich and Elad Zinger. Participants can report any violation of these guidelines to these designates in confidence. If asked to stop inappropriate behaviour participants are expected to comply immediately and, in serious cases, may be asked to leave the event. The organizers will not tolerate retaliation against anyone reporting violations of this code of conduct.

    Acknowledgement: This code of conduct is based on the ESO Code of Conduct for Workshops & Conferences.