Jordan Richards, PhD

I am a lecturer in statistics in the School of Mathematics at the University of Edinburgh. I am mainly interested in the intersection of extreme value theory, spatial statistics, and deep-learning, with a particular focus on applications to natural hazards and extreme climate risk.

I attained my PhD in statistics and operational research in 2021, through the STOR-i centre for doctoral training, under the supervision of Jon Tawn and Jenny Wadsworth, both of Lancaster University, and Simon Brown of the Hadley Centre for Climate Science and Services at the UK Met Office. Between 2021 and 2024, I was a postdoc at the King Abdullah University of Science and Technology (KAUST), in the extreme statistics (XSTAT) research group led by Raphaël Huser. My postdoctoral research focused on the development of sparse models for spatio-temporal extremes.

1. 13/01/25: Happy to announce that Xuanjie Shao has been selected to receive one of the three honorable mentions of this year’s ASA ENVR 2025 Student Paper Competition, for our joint paper entitled “Deep Compositional Spatial Models for Nonstationary Extremal Dependence”.
2. 31/10/24: Calls open for a special issue in Extremes, on bridging Artificial Intelligence (AI) and Extreme Value Theory (EVT). Submissions by 30th March 2025.

1. Mackay, E., Murphy-Barltrop, C. J. R., Richards, J., and Jonathan, P. (2025).
   Deep learning joint extremes of metocean variables using the SPAR model. Proceedings of the ASME 44th International Conference on Ocean, Offshore & Arctic Engineering (OMAE2025), to appear. arXiv.
2. Sainsbury-Dale, M., Zammit-Mangion, A., Richards, J., and Huser, R. (2025).
   Neural Bayes estimators for irregular spatial data using graph neural networks. Journal of Computational and Graphical Statistics, to appear.
3. Richards, J., Sainsbury-Dale, M., Zammit-Mangion, A., and Huser, R. (2024).
   Neural Bayes estimators for censored inference with peaks-over-threshold models. Journal of Machine Learning Research, 25(390):1-49.
4. Richards, J., Alotaibi, N., Cisneros, D., Gong, Y., Guerrero M. B., Redondo, P., and Shao., X. (2024).
   Modern extreme value statistics for Utopian extremes. Extremes, to appear.
5. Shao, X., Hazra, A., Richards, J., and Huser, R. (2024).
   Flexible modeling of non-stationary extremal dependence using spatially-fused LASSO and ridge penalties. Technometrics, 67(1):97–111.
6. Cisneros, D., Richards, J., Dahal, A., Lombardo, L., and Huser, R. (2024).
   Deep graphical regression for jointly moderate and extreme Australian wildfires. Spatial Statistics, 59:100811.
7. Richards, J., Huser, R., Bevacqua, E., and Zscheischler, J. (2023).
   Insights into the drivers and spatio-temporal trends of extreme Mediterranean wildfires with statistical deep-learning. Artificial Intelligence for the Earth Systems, 2(4):e220095.
8. Richards, J., Tawn, J. A., and Brown, S. (2023).
   Joint estimation of extreme spatially aggregated precipitation at different scales through mixture modelling. Spatial Statistics, 53:100725.
9. Richards, J. and Tawn, J. A. (2022).
   On the tail behaviour of aggregated random variables. Journal of Multivariate Analysis, 192:105065.
10. Richards, J., Tawn, J. A., and Brown, S. (2022).
    Modelling extremes of spatial aggregates using conditional methods. Annals of Applied Statistics, 16(4):2693-2713.
11. Richards, J. and Wadsworth, J. L. (2021).
    Spatial deformation for nonstationary extremal dependence. Environmetrics, 32(5):e2671.

1. Talento, M. S. D., Richards, J., Pinto-Orellana, M., Huser, R., and Ombao, H. C. (2025+).
   Spectral extremal connectivity of two-state seizure brain waves. arXiv.
2. Jiang, J., Richards, J., Huser, R., and Bolin, D. (2024+).
   The efficient tail hypothesis: an extreme value perspective on market efficiency. arXiv.
3. Murphy-Barltrop, C. J. R., Majumder, R., and Richards, J. (2024+).
   Deep learning of multivariate extremes via a geometric representation. arXiv.
4. Richards, J. and Huser, R. (2022+).
   Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks. arXiv.

1. Richards, J. and De Monte, L. (2024).
   Review of "Risk Revealed: Cautionary Tales, Understanding and Communication" by Paul Embrechts, Marius Hofert, and Valérie Chavez-Demoulin.
   Journal of Agricultural, Biological, and Environmental Statistics, to appear.
2. Richards, J., Lee, M. W., Carcaiso, V., and de Carvalho, M. (2024).
   Contribution to the Discussion of “Inference for extreme spatial temperature events in a changing climate with application to Ireland” by Healy et al.
   Journal of the Royal Statistical Society Series C: Applied Statistics, qlae084.
3. Richards, J. and Huser, R. (2024+). Extreme Quantile Regression with Deep Learning.
   Book chapter, for Chapman and Hall/CRC Handbook on Statistics of Extremes. Preview. Code.
4. Richards, J. (2021). Extremes of Aggregated Random Variables and Spatial Processes.
   Lancaster University, PhD Thesis.

2024

RSS, 2024 - A deep learning approach to modelling joint environmental extremes. Poster.

JSM, 2024 - Extreme causal analysis for both tails in time series data. Slides.

EcoSta, 2024 - A deep geometric approach to modelling multivariate extremes. Slides.

Southampton; Exeter; Edinburgh seminars, 2024 - Neural Bayes estimators for likelihood-free and amortised inference for spatial extremes. Slides.

2023

KAUST Statistics workshop, 2023 -

1. Advancements in neural Bayes estimation for spatial processes. Poster.
2. Dual extremal cross-frequency interactions in brain connectivity. Poster.
3. A new dependence measure for extremal brain connectivity. Poster.
4. Causal analysis for both tails in time series: with application to China's derivatives market. Poster.

Spatial Statistics, 2023 - Deep compositional models for non-stationary extremal dependence. Poster.

EVA; ICSA symposium, 2023 - Neural Bayes estimators for fast and efficient inference with spatial peaks-over-threshold models. Slides.

2022

EGU (different title); CMStats, 2022 - Partially-interpretable neural networks for extreme quantile regression: With application to Mediterranean Europe wildfires. Slides.

ENVR workshop, 2022 - Partially-interpretable neural networks for extreme quantile regression. Poster.

EVAN (different title); JSM, 2022 - Partially-interpretable neural networks for high-dimensional extreme quantile regression: With application to U.S. wildfires. Slides.

Older

Bath; Lancaster seminars, 2021 - Joint estimation of extreme precipitation aggregates at different spatial scales through mixture modelling and conditional methods. Slides.

EVA, 2021 - Modelling the extremes of spatial aggregates of precipitation using conditional methods. Slides.

CMStats, 2020; vEGU, 2021 - Modelling the tail behaviour of precipitation aggregates using conditional spatial extremes. Slides.

WET workshop; EVA, 2019 - Aggregation of multivariate extremes. Poster.

1. yalla. Repository of R code used by Team Yalla in the EVA 2024 conference data challenge. Accompanies Richards et al. (Extremes; 2024).
2. CensoredNeuralEstimators.jl. Julia scripts for training neural Bayes estimators for censored data. Supports Richards, Sainsbury-Dale, Zammit-Mangion, and Huser (JMLR; 2024).
3. pinnEV. R package for fitting extreme value (and other) regression models using the partially-interpretable neural network (PINN) framework proposed by Richards and Huser (2022+). Currently in development.
4. scePrecip. R code for fitting spatial conditional extremes models to censored data, e.g., precipitation. Accompanies Richards, Tawn, and Brown (2022, 2023).
5. sdfExtreme. R package for performing spatial deformations to handle nonstationarity in spatial extremal dependence. Accompanies Richards and Wadsworth (2021).