PHYSTAT - Statistics meets ML - VERaIPHY

→ Europe/Amsterdam
Colloquium room (Nikhef)

Colloquium room

Nikhef

Gaia Grosso (IAIFI, MIT), Ramon Winterhalder (Università degli Studi di Milano), Louis Lyons (Imperial College (GB)), Olaf Behnke (Deutsches Elektronen-Synchrotron (DE)), Nicholas Wardle (Imperial College (GB)), Lydia Brenner (Nikhef National institute for subatomic physics (NL)), Sara Algeri (University of Minnesota)
Description

Second PHYSTAT Topical Meeting on “Statistics meets ML” in Particle Physics & Astrophysics – VERaIPHY

In recent years, ML has become more and more integrated into many stages of our analyses.  In Particle Physics, it includes data collection and processing (triggering, tracking, etc), classification of different particle types, unfolding, parameter determination, anomaly detection, and even end-to-end processing. In astronomy, its use is increasingly widespread in areas such as object classification, distance determination, and regression problems. It is used theoretically to enhance simulations and to emulate theoretical predictions that are expensive to compute. It is also increasingly used in simulation-based inference, both for finding informative summary statistics and for variational methods of inference.

The first PHYSTAT meeting on “Statistics meets Machine Learning” brought together experts from particle physics, astrophysics, statistics, and ML to discuss the statistical foundations and limitations of these approaches. While ML methods often outperform traditional techniques in terms of speed and apparent precision, a central open question remains whether they are also statistically reliable and scientifically trustworthy, in particular with respect to uncertainty quantification, generalization, robustness, and interpretability.

Since that first meeting, a coordinated community effort has emerged to systematically address these questions: the VERaIPHY initiative (“Validation & Evaluation for Robust AI in PHYsics”). Within VERaIPHY, ten focused working groups have been developing a series of in-depth review and research articles on the statistical validation of modern ML methods in the physical sciences.

The present meeting serves as a capstone event for this article series. Rather than a broad, open-call workshop, it is structured as a topical assessment and synthesis, in which the authors of the VERaIPHY working groups will present and discuss their results. The program therefore consists of invited contributions from these groups, followed by extended discussion sessions with the wider PHYSTAT community.

The scientific scope covers, among others:

  • Statistical properties of training and generalization

  • Uncertainty quantification and calibration

  • Model robustness and failure modes

  • Inference and parameter estimation

  • Generative models and their statistical validation

  • Hypothesis testing and discovery

  • Model selection, interpretability, and explainability

  • Information-theoretic aspects and data representations

  • Symbolic and physics-informed ML

Although the list of speakers is fixed, participation is open, and ample time will be reserved for questions and discussion with all attendees.

The meeting will be hybrid i.e. participation will be either in person or remote. In both cases, registration is necessary, as the Zoom link to the meeting will be sent only to registered participants a few days before the meeting.
 
In-person registration costs €50 to cover coffee and lunch.  The limit for in-person participants is 80. 

PHYSTAT

The PHYSTAT series of Workshops started in 2000. They were the first meetings devoted solely to the statistical issues that occur in analyses in Particle Physics and neighbouring fields. The homepage of PHYSTAT, with a list of all Workshops, Seminars, and Informal Reviews, is at https://phystat.github.io/Website/ .

Registration
In-person registration (€50)
Remote participation registration
Participants
  • Monday 16 February
    • 09:00 → 09:30
      Registration (with coffee) 30m
    • 09:30 → 10:00
      Welcome and Introduction 30m
      Speakers: Louis Lyons (Imperial College (GB)), Lydia Brenner (Nikhef National institute for subatomic physics (NL))
    • 10:00 → 10:30
      Statistical Properties of Training & Generalization 30m
      Speakers: Itay Lavie (Harvard University), Prof. Jonatan Kahn (University of Toronto), Noam Levi (Tel Aviv University)
    • 10:30 → 10:45
      Q&A 15m
    • 10:45 → 11:15
      Coffee Break 30m
    • 11:15 → 11:45
      Uncertainty Quantification 30m
      Speakers: Manuel Haußmann (Universität Heidelberg), Maria Ubiali (University of Cambridge (GB)), Ramon Winterhalder (Università degli Studi di Milano)
    • 11:45 → 12:00
      Q&A 15m
    • 12:00 → 13:15
      Lunch Break 1h 15m
    • 13:15 → 13:45
      Robustness 30m
      Speakers: Alexander Held (University of Wisconsin Madison (US)), Carolina Cuesta Lazaro, Dr Juan M. Cruz Martinez (Universidad de Sevilla (ES)), Michael Kagan (SLAC National Accelerator Laboratory (US))
    • 13:45 → 14:00
      Q&A 15m
    • 14:00 → 14:30
      Inference & Parameter Estimation 30m
      Speaker: Theo Heimel (UCLouvain)
    • 14:30 → 14:45
      Q&A 15m
    • 14:45 → 15:15
      Coffee Break 30m
    • 15:15 → 15:45
      Generative Models & Statistical Validation 30m
      Speaker: Sascha Diefenbacher (Lawrence Berkeley National Lab. (US))
    • 15:45 → 16:00
      Q&A 15m
    • 16:00 → 16:30
      Anomaly Detection & Statistical Discovery 30m
      Speakers: Dr Marco Letizia, Mikael Kuusela (Carnegie Mellon University (US)), Oz Amram (Fermi National Accelerator Lab. (US))
    • 16:30 → 16:45
      Q&A 15m
    • 16:45 → 17:15
      Coffee Break 30m
    • 17:15 → 18:15
      Roundtable 1h
  • Tuesday 17 February
    • 09:00 → 09:30
      Information Theory & Data Representation 30m
      Speakers: Anna Scaife (University of Manchester), Daniel Muthukrishna (MIT), Samuel Byrne Klein (Universite de Geneve (CH))
    • 09:30 → 09:45
      Q&A 15m
    • 09:45 → 10:15
      Interpretability & Explainability 30m
      Speakers: Jesse Thaler (MIT), Luisa Lucie-Smith, Rikab Gambhir (Rutgers State Univ. of New Jersey (US)), Rikab Gambhir, Rikab Gambhir (MIT)
    • 10:15 → 10:30
      Q&A 15m
    • 10:30 → 11:00
      Coffee Break 30m
    • 11:00 → 11:30
      Model Selection & Inductive Bias 30m
      Speakers: Alexander Bogatskiy (Flatiron Institute, Simons Foundation), Jan Tuzlic Offermann (Brown University (US)), Prof. Soledad Villar (Johns Hopkins University)
    • 11:30 → 11:45
      Q&A 15m
    • 11:45 → 12:15
      Future Directions & Open Challenges 30m
      Speakers: Gaia Grosso (IAIFI, MIT), Lukas Alexander Heinrich (Technische Universitat Munchen (DE)), Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))
    • 12:15 → 12:30
      Q&A 15m
    • 12:30 → 13:30
      Roundtable 1h
    • 13:30 → 13:45
      Concluding thoughts 15m
      Speakers: Gaia Grosso (IAIFI, MIT), Louis Lyons (Imperial College (GB)), Lydia Brenner (Nikhef National institute for subatomic physics (NL)), Ramon Winterhalder (Università degli Studi di Milano), Tilman Plehn
    • 13:45 → 15:10
      Lunch Break 1h 25m