Name: AI at CERN and SKA
Start: 2018-09-17T10:00:00+01:00
End: 2018-09-18T22:50:00+01:00
Location: Alan Turing Institute, London

AI at CERN and SKA

from Monday 17 September 2018 (10:00) to Tuesday 18 September 2018 (22:50)

Monday 17 September 2018
10:00 Infrastructure for ML
Infrastructure for ML
10:00 - 13:00
Contributions

10:00 opening words - Braam Peter

10:05 Partnering with industry for machine learning at HL-LHC - Maria Girone (CTO CERN OpenLab)

10:35 Emerging Use of DNNs - Stephen Pawlowski (VP of Advanced Computing Solutions, Micron)

11:05 Learning Deep Generative Models of Graphs - Yujia Li (DeepMind)

11:50 Infrastructure for ML panel
13:00 lunch
lunch
13:00 - 14:00
14:00 ML for HEP
ML for HEP
14:00 - 18:00
Contributions

14:00 Deep Learning for Science: Opening the Pandora Box - Shirley Ho

14:30 Two computing challenges for particle physics : the tracking challenge and event simulation with Generative Adversarial Networks - David Rousseau

15:15 Too much of a good thing : how to drink new physics from a 40 Tb/s firehose - Vladimir "Vava" Gligorov

15:45 Deep Learning for the future of the Large Hadron Collider - Maurizio Pierini

16:45 Panel: ML Specific to HEP
18:00 Reception at The Alan Turing Institute
Reception at The Alan Turing Institute
18:00 - 19:00

19:30 Speaker and Organizer Dinner
Speaker and Organizer Dinner
19:30 - 21:30
Tuesday 18 September 2018
10:00 ML for Astro Physics
ML for Astro Physics
10:00 - 13:00
Contributions

10:00 ML and AI challenges in current and future optical and near infra imaging datasets - Richard McMahon

10:30 Optical transient surveys of today and tomorrow : machine learning applications - Stephen Smartt

10:50 Accelerating science by re-purposing machine learning software - Bojan Nikolic

11:10 AstroStatistics and AstroInformatics in the content of the SKA and LSST - Jason McEwen (Astrophysics & Cosmology at Mullard Space Science Laboratory, University College London)

11:30 How to train taggers on data - Jennifer Thompson

11:45 Coffee Break

12:00 Panel: ML Specific to Astro Physics
13:00 lunch
lunch
13:00 - 14:00
14:00 Areas of common interest
Areas of common interest
14:00 - 18:00
Contributions

14:00 Removing Clutter - Killing Dimension - Terry Lyons FLSW FRSE FRS

14:30 Direct optimisation of the discovery significance when training neural networks to search for new physics in particle colliders - Adam Elwood

14:45 Radio Galaxy Classification with Deep Learning - Vesna Lukic (PhD candidate University of Hamburg)

15:00 Learning the solution to SDEs using rough paths theory and machine learning - Hao Ni (Senior Lecturer in Financial Mathematics at UCL and the Turing Fellow at The Alan Turing Institute )

15:30 Networked data-science for research, academic communities and beyond - Andrey Ustyuzhanin

16:00 Building Search Pipelines and Labelled Datasets for Transient Discovery - Griffin Foster (University of Oxford, University of California at Berkeley)

16:15 Time-domain Machine Learning - Opportunities and Challenges for the SKA - Robert Lyon

17:00 Panel: Areas for Possible Collaboration

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