PHYSTAT Seminars

NEW: PHYSTAT INFORMAL REVIEWS:

In this new format a Tandem consisting of a physicist and a statistician will review a statistical method introduced by one of the parties or a general critical analysis topic from the Physicist's and Statistician's perspectives. The virtual events comprise: two 20+10 min. complementary presentations followed by ~30 minutes of general discussion.

  • 4th Dec 2024 Matt Kenzie (Cambridge) and Anthony Davison (EPFL) "Discrete Profiling"
  • 30th Oct 2024 Glen Cowan (RHUL), Enzo Canonero (RHUL), and Richard Lockhart (SFU) "Errors on Errors"
  • 8th May 2024 Tom Junk (FNAL), Alex Read (Uni Oslo) and Mike Evans (Toronto) "CLs criterion for limit setting"
  • 28th Feb 2024 Roger Barlow (Uni Huddersfield) and Alessandra Brazzale (Uni Padova) "Asymmetric Uncertainties"
  • 24th Jan 2024 Bob Cousins (UCLA) and Larry Wasserman (CMU) "Hybrid Frequentist-Bayesian Approaches"

PHYSTAT SEMINAR SERIES:

Click on the dates for the links to the individual indico seminar pages

  • 10th Apr 2024 Hans Dembinski (Dortmund) "Template Fits"
  • 13th Mar 2024 Charles Geyer (UMN) "An Introduction to the Nonparametric Bootstrap"
  • 10th Jan 2024 Alan Heavens (Imperial) "Extreme Lossless Data Compression for Likelihood-Free Inference"
  • 25th Oct 2023 Lydia Brenner (Nikhef) "Comparison of Unfolding methods"
  • 27th Sept 2023 Harrison Prosper (Florida State) "Likelihood-free inference"
  • 6th Sept 2023 Galin Jones (Minnesota) "Practical Markov Chain Monte Carlo"
  • 10th May 2023 Mike Williams (MIT) "Robust and provably monotonic networks for Particle Physics and beyond"
  • 8th Mar 2023 Ben Nachman (LBNL) “Unbinned and High-dimensional Unfolding with Machine Learning“
  • 8th Feb 2023 Gilles Louppe (Liege) "Reliable simulation based-inference with balanced neural ratio estimation"
  • 25th Jan 2023 Jesse Thaler (MIT), " Learning Uncertainties the Frequentist Way "
  • 16th Nov 2022 Michael Kagan (SLAC) "On relating Uncertainties in Machine Learning and HEP" joint PHYSTAT - CERN DS Seminar
  • 9th Nov 2022 Lolian Shtembari (MPI Munich) "Spacing statistics: what they are and how to use them"
  • 26th Oct 2022 Daniel Whiteson (Irvine), "Using Machine Learning to Get Serious about Systematics"
  • 12th Oct 2022 Philipp Windischhofer (Chicago), "Optimal Transport in HEP: theory and applications"
  • 20th July 2022 Mikael Kuusela (CMU), "Gaussian Processes for Particle Physicists"
  • 15th June 2022 Tommaso Dorigo (Padova), "Sticking to the roots of machine learning: old-school approaches to physics analysis"
  • 27th April 2022 Mikael Kuusela (CMU) "Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests". This is part of the PHYSTAT-Anomalies Workshop on 24th & 25th May 2022, https://indico.cern.ch/event/1148820/
  • 13th April 2022 Matthew Kenzie (Warwick) "COWs: Custom Orthogonal Weight Functions"
  • 23rd Mar 2022 Special Phystat event dedicated to the memory of Sir David Cox, including a PHYSTAT seminar: Sara Algeri (Uni Minnesota): "On computationally efficient methods for testing multivariate distributions with unknown parameters"
  • 19th Jan 2022 Christian Robert (Paris Dauphine PSL & Warwick) "The many nuances of Bayesian testing"
  • 1st Dec 2021 Amanda Cooper-Sarkar (Oxford): "Statistical Issues in proton PDF fits"

Past PHYSTAT seminars joint with CERN EP-IT Data Science:

  • 17th Nov 2021 David Blei (Columbia): "Scaling and Generalising Approximate Bayesian Inference"
  • 27th Oct 2021 Ann Lee (CMU): "Likelihood-free Frequentist Inference"
  • 7th July 2021 Xiao-Li Meng (Harvard) "From COVID-19 testing to election prediction: how small are our big data? "
  • 12th May 2021 Richard Samworth (Cambridge) "USP: an independence test that improves on Pearson's chi-squared and the G-test "
  • 14th Apr 2021 Chad Shafer (CMU): "An Overview of Nonparametric Regression"
  • 10th Mar 2021 Nick Wardle (Imperial College): "Can we really reinterpret data from the LHC?"
  • 20th Jan 2021 Aaditya Ramdas (CMU): "Some new concepts in methodological statistics, that could be of utility in the sciences"
  • 9th Dec 2020 Roberto Trotta (Imperial College) : "Supervised learning with biased training data and applications to Supernovae type 1a Cosmology
  • 11th Nov 2020 Wolfgang Rolke (UPR): "Testing Goodness of Fit"
  • 28th Oct 2020 Larry Wasserman (CMU): "Optimal Transport"
  • 14th Oct 2020 Kyle Cranmer (NYU) : "Likelihood publishing, RECAST, and simulation based inference"
  • 15th July 2020 Chad Shafer (CMU): "An Overview of Approximate Bayesian Computation"
  • 3rd June 2020 Harrison Prosper (FSU): "Statistics in Astronomy: A View Through the Looking Glass"
  • 1th April 2020 Mikael Kuusela (CMU): "Uncertainty Quantification in ill-posed inverse problems: Case Studies in the Physical Sciences "
  • 5th Feb 2020 at CERN Yoav Benjamini (Tel Aviv) "Addressing the effect of selection on inference with the False Discovery Rate"
  • 15th Jan 2020 at CERN Allen Caldwell (MPIM): "Accelerating Bayesian Computation: Parallelizing Markov Chain MC"
  • 20th Nov 2019 at CERN Christian Mueller (LMU): "A general introduction to continuous optimization"
  • 9th Oct 2019 at CERN Anthony Davison (EPFL): "Statistical discovery and the modelling of rare events"
  • 15th Apr 2019 at CERN Naftali Tishby (ELSC): "On the Statistical Mechanics and Information theory of Deep Learning", jointly with LPCC IML Workshop & CERN DS seminar