Name: Machine learning for phenomenology
Start: 2018-04-03T08:00:00+01:00
End: 2018-04-06T18:00:00+01:00
Location: IPPP Durham

Machine learning for phenomenology

from Tuesday, 3 April 2018 (08:00) to Friday, 6 April 2018 (18:00)

Monday, 2 April 2018
Tuesday, 3 April 2018

13:00 Sandwich buffet at the IPPP
Sandwich buffet at the IPPP
13:00 - 14:00
14:00
14:00 - 17:00
Contributions

14:00 Introduction - Ben Nachman (LBNL)

15:30 How to figure out what information the machine is learning from - Bryan Ostdiek (University of Oregon)

16:15 Generative models introduction - Michela Paganini (Yale)
18:00 Dinner in Collingwood
Dinner in Collingwood
18:00 - 19:00
Wednesday, 4 April 2018
09:30
09:30 - 12:30
Contributions

09:30 Discriminating gluon fusion and VBF in Higgs production with machine learning - Nicolas Greiner (University of Zurich)

10:00 PDFs from NN to ML - Stefano Forte (Milan University)

10:30 Machine learnng: new ideas for PDF determination - Stefano Carrazza

11:30 Generative models - Michela Paganini (Yale)
13:00 Lunch at Collingwood
Lunch at Collingwood
13:00 - 14:00
14:00
14:00 - 17:00
Contributions

14:00 Identification of Hadronically Decaying W Bosons and Top Quarks Using Machine Learning in ATLAS - Ece Akilli

14:45 Mauro Verzetti

16:00 Use of Machine Learning Techniques for improved Monte Carlo Integration - Josh Bendavid (CERN)
18:00 Dinner in Collingwood
Dinner in Collingwood
18:00 - 19:00
Thursday, 5 April 2018
09:30
09:30 - 12:55
Contributions

09:30 Zihao Jiang

10:15 Kristian Gregeren
13:00 Lunch at Collingwood
Lunch at Collingwood
13:00 - 14:00
14:00
14:00 - 17:30
Contributions

14:45 Chase Shimmin

16:00 Clustering techniques for parameter space scanning in BSM - Dorigo Tommaso

16:45 Weak supervision - Bryan Ostdiek (University of Oregon)
18:30 Dinner
Dinner
18:30 - 20:30
Friday, 6 April 2018

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