Activities
Reading Group
The reading group consists of monthly two-hour online meetings. Each meeting will consist of two halves: a discussion of a recent piece of contributing literature followed by the opportunity for one of our members to present ongoing research or brainstorm new ideas. There will be 10-minute breaks between each part.
Use this form to request the Zoom link to our next meeting.
Meeting structure
- First half: during the first 55 minutes of each session, we cover a paper. There will be a lead discussant who sets the discussion in motion, and then the group jumps in.
- In the last final minutes, we wrap up and look for a volunteer to lead the next session (a new discussant may be recommended by PhD supervisors).
- A 10-minute break.
- Second half: the last 55 minutes are for a research presentation or brainstorming new ideas.
- In the final minutes, we look for a volunteer to present their research/discuss ideas in the next session (again, may be recommended by PhD supervisors).
Reading list
| Date and time | Article | Discussant |
|---|---|---|
| 18/10/24 13:00-14:00 |
Olafsdottir, H. K., Rootzén, H., & Bolin, D. (2021). Extreme rainfall events in the Northeastern United States become more frequent with rising temperatures, but their intensity distribution remains stable. Journal of Climate, 34(22), 8863-8877.
. |
Daniela Castro-Camilo |
Research presentation/brainstorming
Friday 18th October 14:00-15:00
Title: A deep learning approach to modelling joint environmental extremes
By: Jordan Richards
Summary
The geometric representation for multivariate extremes, where data is split into radial and angular components and the radial component is modelled conditionally on the angle, provides an exciting new approach to modelling environmental data. Through a consideration of scaled sample clouds and limit sets, it provides a flexible, semi-parametric model for extremes that connects multiple classical extremal dependence measures; these include the coefficients of tail dependence and asymptotic independence, and parameters of the conditional extremes framework. Although the geometric approach is becoming an increasingly popular modelling tool for environmental data, its inference is limited to a low dimensional setting (d ≤ 3).
We propose here the first deep representation for geometric extremes. By leveraging the predictive power and computational scalability of neural networks, we construct asymptotically-justified yet flexible semi-parametric models for extremal dependence of high-dimensional data. We showcase the efficacy of our deep approach by modelling the complex extremal dependence between metocean variables sampled from the North Sea.
Joint work with Callum JR Murphy-Barltrop and Reetam Majumder
Workshops
Two half-day hands-on workshops will be held each year, shaped by the reading group discussions. More detailed information will appear here in due course.