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Program

Plenary Sessions

  • Peter Glynn (Stanford University)
  • Sid Resnick (Cornell University)
  • Johanna G. Nešlehová (McGill University)
  • TBA

Invited Sessions

  1. Asymptotic Theory (Organizer: Ana Ferreira). Invited speakers Holger Drees (Hamburg University), Ligia Rodrigues (Évora University), Juan Juan Cai (Vrije Universiteit Amsterdam).
  2. Bayesian Statistics (Organizer, Leo Belzile). Reetam Majumder (University of Arkansas), Simone Padoan (Bocconi University), Miguel de Carvalho (University of Edinburgh).
  3. Causality (Organizer, Valerie Chavez-Demoulin). Mario Krali (École Polytechnique Fédérale de Lausanne), Kirstin Strokorb (Cardiff University), Linda Mhalla (École Polytechnique Fédérale de Lausanne).
  4. Computational and Learning Methods for Extremes (Organizer, Jose Blanchet). Chang-Han Rhee (Northwestern University), Wenhao Yang (Stanford University), Vasilis Syrgkanis (Stanford University).
  5. Dimension Reduction (Organizer, Anja Janssen). Anne Sabourin (Université Paris Cité), Dan Cooley (Colorado State University), Marco Avella-Medina (Columbia University).
  6. Extrapolation in Regression (Organizer, Miguel de Carvalho). Brian Reich (North Carolina State University), Jordan Richards (University of Edinburgh), Vianey Palacios Ramirez (Newcastle University).
  7. Extremes and Risk Management in Finance and Insurance (Organizer, Zhengjun Zhang). John Einmahl (Tilburg University), Marie Kratz (ESSEC Business School), TBA.
  8. Extremes for Environmental Sciences (Organizer, Philippe Naveau). Glwadys Toulemonde (Universite de Montpellier), Jonathan Koh (ETH, Switzerland), TBA.
  9. Extremes in Time Series and Machine Learning Algorithms (Organizer, Rafal Kulik). Mai Ghannam (University of Toronto), Piotr Kokoszka (Colorado State University), Jose Blanchet (Stanford University).
  10. Extreme Quantile Regression (Organizer, Surya Tokdar. Speakers TBA.
  11. Extreme Value Analysis in Sports (Organizer, John Einmahl).Yi He (University of Amsterdam), Jess Spearing (Shell), Malick Kebe (Howard University).
  12. Forecasting Extremes and Rare Events (Organizer, Clement Dombry). Johan Segers (KU Leuwen), Philippe Naveau (Institut Pierre-Simon Laplace), Ruwan Wickramarachchi (University of South Carolina, to be confirmed).
  13. Geometric Methods (Organizer, Jenny Wadsworth). Natalia Nolde (University of British Columbia), Ioannis Papastathopoulos (University of Edinburgh), and Callum Murphy-Barltrop (TU Dresden).
  14. Graphical Models (Organizer, Stanislav Volgushev). Michael Lalancette (Université du Québec), Nicola Gnecco (University College London), Anna Kiriliouk (University of Namur), TBA.
  15. Machine Learning (Organizer: Anne Sabourin). Raphael Huser (KAUST), Mahsa Taheri (Hamburg University), Gloria Buritica (AgroParisTech).
  16. Networks and Heavy Tails (Organizer: Tiandong Wang). Mariana Olvera-Cravioto (UNC Chapel Hill), Shuyang Bai (University of Georgia), Daniel Cirkovic (Texas A&M).
  17. Sparsity and High-dimensions (Organizer: Phyllis Wan). Johannes Heiny (Stockholm University), Chen Zhou (Erasmus University), Marco Oesting (University of Stuttgart).
  18. Spatial Extremes (Organizers: Emily Hector and Brian Reich). Likun Zhang (University of Missouri), Ben Shaby (Colorado State University), Lydia Kakampakou (Lancaster University)

Short Course

A short course will be offered on Sunday, June 22, primarily intended for graduate students and new researchers in extreme value analysis. It will be divided into two sessions, as follows:

  1. Statistical learning and extreme value analysis (Speaker: Anne Sabourin (Université Paris Cité). In recent years, there has been a surge of theoretical and methodological advancements aimed at bridging the gap between Extreme Value Analysis (EVA) and fields such as machine learning, statistical learning, and artificial intelligence. These developments offer new perspectives both theoretically and methodologically. The goal of this tutorial is twofold: (a) to explain the thought process and main ideas underlying statistical learning frameworks in EVA in a rather non-technical way, and (b) to provide a deeper understanding of the proof techniques used to derive non-asymptotic guarantees. The presentation will be structured around three main themes: i. Supervised Learning with Extreme Covariates: Classification and regression tasks, Empirical risk minimization, Risk decomposition; ii. Unsupervised Learning: Dimensionality reduction, Detecting sparsity patterns, Anomaly detection; iii. Proof Techniques: Analysis of the k-largest order statistics and exceedances above the (1 – k/n)-quantile, Conditioning trick, Concentration inequalities for rare events. This tutorial is designed for researchers and practitioners interested in integrating EVA into modern statistical learning and machine learning frameworks, as well as incorporating modern statistical learning tools into EVA.
  2. Statistical Modeling of Extremes (speaker: Anthony Davison, EPFL). Over the past two decades Stuart Coles’ book has provided an excellent and widely-used introduction to the statistics of extremes for researchers, students and practitioners from a wide variety of disciplines.  The area has developed rapidly since its publication in 2001, however, and, joint with Stuart, Anthony Davison and Miguel de Carvalho have tried to update the book to reflect some modern developments, without reducing its accessibility and clarity.   This workshop will give an overview of the main changes, including something of an historical overview of the development of basic statistical modelling of extremes over the past quarter-century, including in multivariate and spatial extremes, with the updated contents illustrated through hands-on computing experience.