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ML_Meetings

Page history last edited by Kieran 6 days, 12 hours ago

Machine Learning in Astronomy Meetings

 

Regular fortnightly (i.e. every 2 weeks) informal zoom research meetings take place on https://uws.zoom.us/j/6448448129 and on the WSU Kingswood campus in room Y2.32 (the "Thinktorium"). Times were chosen by a Doodle poll, and will be revisited every 6 months.

 

Schedule of Meetings in 2020: 

  • 04/02/2020 1100 (AEDT) - Nic Ralph (ICNS in the MARCS Institute at WSU): "An Introduction to Neuromorphic Engineering, and how it fits into Astronomy and Machine Learning" . Download Link. Youtube Link.
  • 18/02/2020 2000 (AEDT) - Rafael Mostert (Leiden) - Preliminary Results on Region-CNN's for Radio Source Component Association. Download Link. Youtube link.
  • 03/03/2020 1100 (AEDT) - Jacky Thong (CSIRO's Data61 and University of Melbourne) - Infrared Host Galaxy Identification using GANs without Training Labels. Download Link. Youtube Link. Slides Link.
  • 17/03/2020 2000 (AEDT) - Tim Galvin (CSIRO): "Attempts to Catalogue the Radio Sky using PINK". Download Link. Youtube Link.
  • 31/03/2020 1100 (AEDT) - Wray Buntine (Monash): "Insights into Deep Learning." Download Link. Youtube Link. Slides Link.
  • 14/04/2020 1900 (AEST) - Erica Hopkins (H-ITS) -  Finding a needle in a haystack: The search for strong gravitational lenses Download Link. Youtube Link.
  • 28/04/2020 1000 (AEST) - Cris Sabiu (Yonsei University) - Analysing the cosmic large scale structure with higher order statistics and machine learning. Download Link. Youtube Link.
  • 12/05/2020 1900 (AEST) - James Farr (University College London) - QuasarNET: Quasar Classification in Spectroscopic Surveys. Download Link. Youtube Link.
  • 26/05/2020 1000 (AEST) - Michelle Boyce (Manitoba) - Topological Computational Techniques for source classification and morphology. Download Link. Slides Link. Youtube Link.
  • 09/06/2020 1900 (AEST) - Antonio D'Isanto (H-ITS) - The two worlds of photometric redshift estimation via machine learning: fully automatic vs feature based. Download link. Youtube Link.
  • 23/06/2020 1000 (AEST) - Colin Jacobs (Swinburne) - Probing Neural Networks for science: What is it they are learning? Download Link. Youtube Link.
  • 07/07/2020 1900 (AEST) - Michelle Lochner (African Institute for Mathematical Sciences) - Anomaly Detection with Machine Learning for Astronomical Data. Download Link. Youtube Link.
  • 21/07/2020 1000 (AEST) - No meeting due to EMU International Meeting
  • 04/08/2020 1900 (AEST) - No Meeting
  • 18/08/2020 1000 (AEST) - Shae Brown (University of Iowa) - Classifying Complex Radio Sources and Faraday Spectra with Convolutional Neural Networks. 

 

Meetings alternate between two times (time may not be accurate close to the daylight changeover period -  please check your local time against UTC)

  UTC  Sydney (AEST) Perth (AWST) Paris (CEST)  Denver (MDT) 
America-friendly Tue. 0000
Tue. 1000
Tue. 0800
Tue. 0200
Mon. 1800
Euro-friendly Tue. 0900
Tue. 1900
Tue. 1700
Tue. 1100
Tue. 0300

 

 

How to join the meeting remotely (Note Zoom address has changed):

 

Potential future talks: 

  • Nic Ralph (Western Sydney University) - Neuromorphic unsupervised tracking of fast transient objects using event-based vision sensors
  • Rui Luo (CSIRO) - Injecting “unexpected” signals into real (or simulated) data sets and then discuss ideas (linked to machine learning) on how to identify those signals

 

Format of Talks (suggestions for speakers)

The length is anything from 30 to 50 mins - you choose. In any remaining time (the meeting is scheduled to last for up to 1 hour) we will have an informal discussion about the topics you raise. I suggest you use a powerpoint/keynote/pdf, and expect people to chip in with questions, or even trigger a discussion, as you go along. I.e. the tone is relaxed and informal. Nobody is going to give you a hard time. Think of it as chatting with your peers about your work. Bear in mind that the audience ranges from data scientists who know little about astronomy to astronomers who know little about machine learning, so try to keep the jargon to a minimum, or explain It if you need to use it.

 

Recordings of Meetings in 2019

See this page

 

Recordings of Meetings in 2018 and earlier

See this page

 

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