PHYSTAT-Systematics 2021, 1-3 Nov + 10 Nov

Contribution List

1. Welcome to workshop

01/11/2021, 14:00

Session 1a

2. Setting the Scene

Louis Lyons (Imperial College (GB))

01/11/2021, 14:05

Session 1a

3. Statistician's overview of Systematics

David van Dyk (University of California, Irvine), David van Dyk

01/11/2021, 14:30

Session 1a

4. Systematics of direct Dark Matter signals experiments and models

Knut Morå (Columbia University), Knut Morå (Stockholm University)

01/11/2021, 15:10

Session 1a

22. Response 1

Alessandra Brazzale (Padova)

01/11/2021, 15:50

Session 1a

5. Systematics at LHC for event selection, discovery and limits

Lukas Alexander Heinrich (CERN)

01/11/2021, 16:20

Session 1b

6. Precision measurements

Alexander Glazov (Deutsches Elektronen-Synchrotron (DE))

01/11/2021, 17:00

Session 1b

7. Response 2

Tom Junk (Department of Physics), Tom Junk (Fermi National Accelerator Lab. (US))

01/11/2021, 17:40

Session 1b

23. A call to action: Honoring PHYSTAT's 20 year old agreement

Kyle Cranmer (NYU), Kyle Stuart Cranmer (New York University (US))

01/11/2021, 18:00

Session 1b

8. Systematics in a selection of neutrino oscillation experiments

Christophe Bronner (Kamioka, ICRR, University of Tokyo)

02/11/2021, 14:00

Session 2a

9. Flavour physics

Thomas Blake (University of Warwick)

02/11/2021, 14:40

Session 2a

10. Response 3

Chad Schafer (CMU)

02/11/2021, 15:20

Session 2a

11. Theory uncertainties

Frank Tackmann, Frank Tackmann (Deutsches Elektronen-Synchrotron (DE))

02/11/2021, 16:00

Session 2b

12. Data mismodelling

Adinda De Wit (Universitaet Zuerich (CH))

02/11/2021, 16:40

Session 2b

14. Response 4

Larry Wasserman (CMU)

02/11/2021, 17:20

Session 2b

13. How the Bootstrap eliminates nuisance parameters for confidence intervals

Brad Efron (Stanford)

02/11/2021, 17:40

Session 2b

15. Systematic uncertainties in unfolding particle spectra

Mikael Kuusela (Carnegie Mellon University (US))

03/11/2021, 14:00

Session 3a

16. Three approaches to Systematics with Machine Learning

Kyle Stuart Cranmer (New York University (US))

03/11/2021, 14:40

Session 3a

17. Response 5

Anthony Davison (EPFL)

03/11/2021, 15:20

Session 3a

24. Transfer Learning for data-driven background modelling: Optimal Transport v Classifier Extrapolation

Tudor Manole (CMU)

03/11/2021, 16:00

Session 3b

25. Systematic effects versus nuisance parameters

Richard Lockhart (SFU)

03/11/2021, 16:25

Session 3b

26. Universal Inference

Larry Wasserman (Carnegie Mellon University)

03/11/2021, 16:50

Session 3b

27. Bayesian reflection on Systematics

James Berger (Duke University)

03/11/2021, 17:15

Session 3b

28. Partially Bayes : An inter-perspective approach for handling nuisance parameters

Xiao-Li Meng (Harvard)

03/11/2021, 17:40

Session 3b

Recorded talk, available for viewing afterwards
Send comments/questions for Prof. Meng to l.lyons@physics.ox.ac.uk

19. Final remarks ** N.B. Summary Talks on Nov 10th **

Olaf Behnke (Deutsches Elektronen-Synchrotron (DE))

03/11/2021, 17:42

Session 3b

20. Physicist's summary

Nicholas Wardle (Imperial College (GB))

10/11/2021, 15:00

Summary presentations

21. Statistician's summary

Sara Algeri (University of Minnesota)

10/11/2021, 15:45

Summary presentations

18. Short talks by Statisticians

Session 3b