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BIRS talks Canada

8–12 Jun 2026
US/Mountain timezone

Contribution List

22 out of 22 displayed
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  1. 11. Introduction
    Kyle Cormier (CERN), Lydia Brenner (Nikhef National institute for subatomic physics (NL)), Olaf Behnke (Deutsches Elektronen-Synchrotron (DE)), Philipp Windischhofer (University of Chicago (US))
    08/06/2026, 09:00
  2. 1. Tools and methods for interpretable high-dimensional analysis
    Mathieu Markovitch (Centre National de la Recherche Scientifique (FR))
    08/06/2026, 09:05
  3. 4. Interpreting high-dimensional excesses and anomalies
    Jannicke Pearkes (University of Colorado Boulder (US)), Dr Purvasha Chakravarti (University College London)
    08/06/2026, 10:30
  4. 3. Data reduction
    David van Dyk, Konstantin Leyde (Flatiron Institute / CCA)
    08/06/2026, 13:30
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  5. 2. Validation and goodness-of-fit in high dimensions
    Cristina Alexe (Scuola Normale Superiore & INFN Pisa (IT)), Prof. Wolfgang Rolke (University of Puerto Rico - Mayaguez, Department of Mathematical Sciences)
    08/06/2026, 15:30
  6. 5. modelling in small sub-regions
    Mikael Kuusela (Carnegie Mellon University (US)), Oliver Rieger (Nikhef National institute for subatomic physics (NL))
    09/06/2026, 09:00
  7. 6. validating high-dimensional simulations
    Chris Pollard (University of Warwick (GB)), Michelle Ntampaka
    09/06/2026, 10:30
  8. 7. High-dimensional distributions and Machine Learning
    Lukas Alexander Heinrich (Technische Universitat Munchen (DE))
    09/06/2026, 13:00
  9. 8. Modelling of relationships between variables in high-dimensional data
    Leigh Preimesberger, Wouter Verkerke (Nikhef National institute for subatomic physics (NL))
    09/06/2026, 14:00
  10. 9. Unfolding of high-dimensional observables
    Erik Bensen, Kyle Cormier (CERN)
    09/06/2026, 15:30
  11. 10. Round-table discussion: biases and dependencies
    André David (CERN)
    09/06/2026, 16:30
  12. 12. Scalable inference
    Aliya Nigamova (Paul Scherrer Institute (CH))
    10/06/2026, 09:00
  13. 13. Methods for reducing computational complexity
    Alessandra Rosalba Brazzale, Jonathon Mark Langford (Imperial College (GB))
    10/06/2026, 10:30
  14. 16. Information transfer from machine learning to scientists
    Martina Fusi (University of Southampton), Simon Mak
    10/06/2026, 13:00
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  15. 17. interpretation and diagnostics using machine learning
    James Carzon (Carnegie Mellon University), Jay Ajitbhai Sandesara (University of Wisconsin (US))
    10/06/2026, 14:00
  16. 18. Complexity scaling of machine learning methods
    Maarten Van Veghel (Nikhef National institute for subatomic physics (NL)), Tom Junk (Fermi National Accelerator Lab. (US))
    10/06/2026, 15:30
  17. 19. Round-table discussion: future of machine learning
    Jack Harrison (Institut de Física d’Altes Energies (IFAE)), Peter Risse, Dr Peter Risse (Southern Methodist University), Peter Risse (Southern Methodist University)
    10/06/2026, 16:30
  18. 14. Leveraging machine learning and its limits
    Anthony Ozerov, Claire David (AIMS South Africa)
    11/06/2026, 09:00
  19. 15. Uncertainties and Machine Learing
    Davide Valsecchi (ETH Zurich (CH))
    11/06/2026, 10:30
  20. 20. Highlight talk from a physicist
    Philipp Windischhofer (University of Chicago (US))
    12/06/2026, 09:00
  21. 21. Highlight talk from a statistician
    Alessandra Rosalba Brazzale, David van Dyk
    12/06/2026, 09:30
  22. 22. Closing
    Kyle Cormier (CERN), Lydia Brenner (Nikhef National institute for subatomic physics (NL)), Olaf Behnke (Deutsches Elektronen-Synchrotron (DE)), Philipp Windischhofer (University of Chicago (US))
    12/06/2026, 11:00